From 09154cf7ed55b8c1f081130648b2cbcc2d175844 Mon Sep 17 00:00:00 2001 From: brian604 Date: Sun, 20 Jun 2021 00:18:26 +0900 Subject: [PATCH] Except for discussion --- README.md | 7 +- images/analysis/cluster12_dotplot.png | Bin 0 -> 196179 bytes .../cluster2_integrated_curated_dotplot.png | Bin 0 -> 161123 bytes instruction.txt | 3 + src/post_integration.ipynb | 3498 +++++++++++++++++ 5 files changed, 3507 insertions(+), 1 deletion(-) create mode 100644 images/analysis/cluster12_dotplot.png create mode 100644 images/analysis/cluster2_integrated_curated_dotplot.png create mode 100644 instruction.txt create mode 100644 src/post_integration.ipynb diff --git a/README.md b/README.md index eb1fb81..f0d3428 100644 --- a/README.md +++ b/README.md @@ -117,7 +117,7 @@ Figure 3 and Figure 4 have shown the distinct patterns do patients have. To delv #### Cluster 12 (Admixed)

- Figure 12 shows dot-plot expression for the most variable genes (Figure 8) across subjects from Severe (0_S) to Healthy Control (0_HC) in cluster 12. Those genes are generally expressed exclusively for the COVID-19 patients (0_S, 0_M).

+ Figure 12 shows dot-plot expression for the most variable genes (Figure 8) across subjects from Severe (0_S) to Healthy Control (0_HC) in cluster 12. Those genes are generally expressed exclusively for the COVID-19 patients (0_S, 0_M). No pattern has been observed in the curated gene lists (not shown).

**Figure 7: Dot-plot expression in cluster 0** @@ -135,3 +135,8 @@ Figure 3 and Figure 4 have shown the distinct patterns do patients have. To delv **Figure 10: Dot-plot expression in cluster 2** ![Figure 10](images/analysis/cluster2_dotplot.png) +**Figure 11: Dot-plot expression in cluster 2 for curated genes** +![Figure 11](images/analysis/cluster2_integrated_curated_dotplot.png) + +**Figure 12: Dot-plot expression in cluster 12** +![Figure 12](images/analysis/cluster12_dotplot.png) diff --git a/images/analysis/cluster12_dotplot.png b/images/analysis/cluster12_dotplot.png new file mode 100644 index 0000000000000000000000000000000000000000..bcefdfd882eca9e42deb16c65a965610d4a176d2 GIT binary patch literal 196179 zcmeFYbx_<<_C44@kOY_DmXIKU0KuIk5C{&z9fG^lxCerVU_lxJ1PJbK4Fq>>v~e15 z++{zR`ObXzH?_4@Tl?>HK{dQ;;Prj?o_o%@k8ov0Y1}82Pe33LuFOZtPaqJPBM5}P zfQ1UI92dx&15b|~KWe*xKHNux^xeyK_c=RzS zvi>Vc0ffkmC5c(L^7+W0m^y*J7a4*~V|^(pRQ~w>-v^22BlbsW=e60ivujjee0aU^ zAp*NT5EM>h>8c6iBC90k`2I04n*HaYh^CuI-Qb|3BA2Bq8r9{&O)PNSPV! zKNpn=o|ygT8tBRU|8DYMLHYj~$?Wf7kXDD3l$5!-xmfeT&dyw=nOv*qzkd6_p0zeR 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  1. 'HC1'
  2. 'HC2'
  3. 'HC3'
  4. NA
  5. 'M1'
  6. 'M2'
  7. 'M3'
  8. 'S2'
  9. 'S1'
  10. 'S3'
  11. 'S4'
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Adding another scale for 'fill', which\n", + "will replace the existing scale.\n", + "\n", + "Warning message:\n", + "“Removed 40 rows containing missing values (geom_point).”\n" + ] + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“Ignoring unknown parameters: xmax”\n", + "Warning message:\n", + "“Transformation introduced infinite values in continuous x-axis”\n", + "Warning message:\n", + "“Transformation introduced infinite values in continuous x-axis”\n" + ] + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cluster0 <- subset(x = nCoV.integrated, subset = seurat_clusters == 0)\n", + "cluster.averages <- AverageExpression(cluster0,\n", + " return.seurat = TRUE,\n", + " group.by = 'sample_new')\n", + "sample_new <- unique(cluster0@meta.data[['sample_new']])\n", + "#\n", + "Idents(cluster.averages) <- unlist(sample_new)\n", + "#\n", + "dpi <- 300\n", + "png(file=\"cluster0_integrated_top.png\", width = dpi*10, height = dpi*10, units = \"px\",res = dpi,type='cairo')\n", + "DoHeatmap(cluster.averages, \n", + " features = unlist(TopFeatures(cluster0[['pca']],\n", + " balanced = FALSE)),\n", + " size =3, draw.lines = TRUE,\n", + " group.by = 'ident',\n", + " label = FALSE)\n", + "dev.off()\n", + "\n", + "feature_points <- TopFeatures(cluster0[['pca']])\n", + "#\n", + "dpi <- 300\n", + "png(file=\"cluster0_integrated_heatmap_variable.png\", width = dpi*10, height = dpi*10, units = \"px\",res = dpi,type='cairo')\n", + "DoHeatmap(cluster.averages, features = feature_points, raster=F,\n", + " label= FALSE) + scale_fill_gradientn(colors = rev(RColorBrewer::brewer.pal(n = 10, name = \"RdBu\")) ) + guides(color=FALSE)\n", + "dev.off()\n", + "\n", + "top10 <- head(VariableFeatures(cluster0), 10)\n", + "\n", + "options(ggrepel.max.overlaps = Inf)\n", + "\n", + "dpi <- 300\n", + "plot1 <- VariableFeaturePlot(cluster0) \n", + "plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE,\n", + " xnudge = 0, ynudge = 0, xmax = Inf) \n", + "png(file=\"cluster0_integrated.png\", width = dpi*10, height = dpi*15, units = \"px\",res = dpi,type='cairo')\n", + "plot1/plot2\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "6c3ae8b7-935b-4c3f-bc6c-f6a673fa073f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in RColorBrewer::brewer.pal(n = 40, name = \"RdBu\"):\n", + "“n too large, allowed maximum for palette RdBu is 11\n", + "Returning the palette you asked for with that many colors\n", + "”\n" + ] + }, + { + "data": { + "image/png": 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TvKHCQ7O7v4Qw8Pj+IPJUkqLCwUQpSe2bJ4F+UMVdlcXV2feeaZhQsXCiGGDx9e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\nkp3tZF4QAABHag52wquD3ulsx5kJxNYQcSakJBXjtddeI4QIn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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "mypalette <- RColorBrewer::brewer.pal(n = 40, name = \"RdBu\")\n", + "features <- TopFeatures(cluster0[['pca']])\n", + "#png(file=\"cluster0_dotplot.png\", width = dpi*20, height = dpi*5, units = \"px\",res = dpi,type='cairo')\n", + "DotPlot(cluster0, features = features, cols = mypalette, dot.scale = 9, col.max = 40, split.by = 'group') + \n", + " RotatedAxis()\n", + "#dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "id": "f40600da-2778-4fcf-bf96-8cdef6e107cb", + "metadata": {}, + "outputs": [], + "source": [ + "cluster2 <- subset(x = nCoV.integrated, subset = seurat_clusters == 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "2ac16ba0-4fed-40c8-980b-dc664ab67862", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in RColorBrewer::brewer.pal(n = 40, name = \"RdBu\"):\n", + "“n too large, allowed maximum for palette RdBu is 11\n", + "Returning the palette you asked for with that many colors\n", + "”\n" + ] + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mypalette <- RColorBrewer::brewer.pal(n = 40, name = \"RdBu\")\n", + "features <- TopFeatures(cluster2[['pca']])\n", + "png(file=\"cluster2_dotplot.png\", width = dpi*20, height = dpi*5, units = \"px\",res = dpi,type='cairo')\n", + "DotPlot(cluster2, features = features, cols = mypalette, dot.scale = 9, col.max = 40, split.by = 'group') + \n", + " RotatedAxis()\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "id": "4515c10b-ff31-4dd9-938d-b9c58d02491f", + "metadata": {}, + "outputs": [], + "source": [ + "DoHeatmap(cluster.averages, \n", + " features = unlist(TopFeatures(cluster0[['pca']],\n", + " balanced = FALSE)),\n", + " size =3, draw.lines = TRUE,\n", + " group.by = 'ident',\n", + " label = FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "id": "780cb1aa-f838-42e2-9d67-e4ce1bcefab4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Removing cells with NA for 1 or more grouping variables\n", + "\n", + "Warning message:\n", + "“The following arguments are not used: row.names”\n", + "Centering and scaling data matrix\n", + "\n", + "Warning message:\n", + "“Transformation introduced infinite values in continuous x-axis”\n", + "Warning message:\n", + "“Transformation introduced infinite values in continuous x-axis”\n" + ] + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "cluster0 <- subset(x = nCoV.integrated, subset = seurat_clusters == 0)\n", + "cluster.averages <- AverageExpression(cluster0,\n", + " return.seurat = TRUE,\n", + " group.by = 'sample_new')\n", + "\n", + "sample_new <- unique(cluster0@meta.data[['sample_new']])\n", + "Idents(cluster.averages) <- unlist(sample_new)\n", + "\n", + "features <- c('MARCO', 'CD68', 'FCGR3A', 'TREM2', 'FCN1', 'SPP1', 'FABP4', 'CD8A')\n", + "dpi <- 300\n", + "options(ggrepel.max.overlaps = Inf)\n", + "plot1 <- VariableFeaturePlot(cluster0) \n", + "plot2 <- LabelPoints(plot = plot1, points = features, repel = TRUE,\n", + " xnudge = 0, ynudge = 0) \n", + "png(file=\"cluster0_integrated_curated.png\", width = dpi*10, height = dpi*15, units = \"px\",res = dpi,type='cairo')\n", + "plot1/plot2\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "id": "8084be25-55bb-4cfd-b26c-b2c57b280dee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mypalette <- RColorBrewer::brewer.pal(n = 11, name = \"RdBu\")\n", + "features <- c('MARCO', 'CD68', 'FCGR3A', 'TREM2', 'FCN1', 'SPP1', 'FABP4','CD8A')\n", + "png(file=\"cluster0_integrated_curated_dotplot.png\", width = dpi*10, height = dpi*15, units = \"px\",res = dpi,type='cairo')\n", + "DotPlot(cluster0, features = features, cols = mypalette, dot.scale = 9, col.max = 40, split.by = 'group') + \n", + " RotatedAxis()\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "id": "53931b5c-c9f8-460f-9aeb-4c9d98c6eb76", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Removing cells with NA for 1 or more grouping variables\n", + "\n", + "Warning message:\n", + "“The following arguments are not used: row.names”\n", + "Centering and scaling data matrix\n", + "\n", + "Warning message:\n", + "“Transformation introduced infinite values in continuous x-axis”\n", + "Warning message:\n", + "“Transformation introduced infinite values in continuous x-axis”\n" + ] + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "cluster2 <- subset(x = nCoV.integrated, subset = seurat_clusters == 2)\n", + "cluster.averages <- AverageExpression(cluster2,\n", + " return.seurat = TRUE,\n", + " group.by = 'sample_new')\n", + "\n", + "sample_new <- unique(cluster2@meta.data[['sample_new']])\n", + "Idents(cluster.averages) <- unlist(sample_new)\n", + "\n", + "features <- c('MARCO', 'CD68', 'FCGR3A', 'TREM2', 'FCN1', 'SPP1', 'FABP4', 'CD8A')\n", + "dpi <- 300\n", + "options(ggrepel.max.overlaps = Inf)\n", + "plot1 <- VariableFeaturePlot(cluster2) \n", + "plot2 <- LabelPoints(plot = plot1, points = features, repel = TRUE,\n", + " xnudge = 0, ynudge = 0) \n", + "png(file=\"cluster2_integrated_curated.png\", width = dpi*10, height = dpi*15, units = \"px\",res = dpi,type='cairo')\n", + "plot1/plot2\n", + "dev.off()\n", + "mypalette <- RColorBrewer::brewer.pal(n = 11, name = \"RdBu\")\n", + "png(file=\"cluster2_integrated_curated_dotplot.png\", width = dpi*10, height = dpi*15, units = \"px\",res = dpi,type='cairo')\n", + "DotPlot(cluster2, features = features, cols = mypalette, dot.scale = 9, col.max = 40, split.by = 'group') + \n", + " RotatedAxis()\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "id": "3626f002-b260-49cb-955c-03c5d0361d0a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Removing cells with NA for 1 or more grouping variables\n", + "\n", + "Warning message:\n", + "“The following arguments are not used: row.names”\n", + "Centering and scaling data matrix\n", + "\n", + "Warning message:\n", + "“Transformation introduced infinite values in continuous x-axis”\n", + "Warning message:\n", + "“Transformation introduced infinite values in continuous x-axis”\n" + ] + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "cluster12 <- subset(x = nCoV.integrated, subset = seurat_clusters == 12)\n", + "cluster.averages <- AverageExpression(cluster12,\n", + " return.seurat = TRUE,\n", + " group.by = 'sample_new')\n", + "\n", + "sample_new <- unique(cluster12@meta.data[['sample_new']])\n", + "Idents(cluster.averages) <- unlist(sample_new)\n", + "\n", + "features <- c('MARCO', 'CD68', 'FCGR3A', 'TREM2', 'FCN1', 'SPP1', 'FABP4', 'CD8A')\n", + "\n", + "dpi <- 300\n", + "options(ggrepel.max.overlaps = Inf)\n", + "plot1 <- VariableFeaturePlot(cluster12) \n", + "plot2 <- LabelPoints(plot = plot1, points = features, repel = TRUE,\n", + " xnudge = 0, ynudge = 0) \n", + "png(file=\"cluster12_integrated_curated.png\", width = dpi*10, height = dpi*15, units = \"px\",res = dpi,type='cairo')\n", + "plot1/plot2\n", + "dev.off()\n", + "mypalette <- RColorBrewer::brewer.pal(n = 11, name = \"RdBu\")\n", + "png(file=\"cluster12_integrated_curated_dotplot.png\", width = dpi*10, height = dpi*15, units = \"px\",res = dpi,type='cairo')\n", + "DotPlot(cluster12, features = features, cols = mypalette, dot.scale = 9, col.max = 40, split.by = 'group') + \n", + " RotatedAxis()\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4ef2e81-f531-4980-a1cc-73d935bb9d52", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R [conda env:r_bioinfo]", + "language": "R", + "name": "conda-env-r_bioinfo-r" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.0.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}

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Integration code loads all the raw data and starts pre-cleaning to do further analysis (integration_code.r) +2. covid_project{.r and .ipynb) is next. +3. Analysis file is in post_integration.ipynb. diff --git a/src/post_integration.ipynb b/src/post_integration.ipynb new file mode 100644 index 0000000..7450050 --- /dev/null +++ b/src/post_integration.ipynb @@ -0,0 +1,3498 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 153, + "id": "46cb1bba-c504-4425-9d4c-4f0c8af01654", + "metadata": {}, + "outputs": [], + "source": [ + "suppressMessages({library(Seurat)\n", + " library(Matrix)\n", + " library(dplyr)\n", + " library(ggplot2)\n", + " library(data.table)\n", + " library(stringr)\n", + " library(tibble)\n", + " library(tidyr)\n", + " library(data.table)\n", + " library(scales)})" + ] + }, + { + "cell_type": "markdown", + "id": "390941d1-a0a6-4ab9-bf12-4340e0385805", + "metadata": {}, + "source": [ + "### Add Sample Information\n", + "1. all.cells.annotation.txt\n", + "2. nCoV_mine_integrated_celltype_final_marker.rds (size 3.2GB) --> This will be sent to an email over cloud. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "0359425b-85a9-4ba2-84a9-c593339209f2", + "metadata": {}, + "outputs": [], + "source": [ + "nCoV.integrated <- readRDS(file = 'nCoV_mine_integrated_celltype_final_marker.rds') #from covid_project.ipynb\n", + "to_be_deleted <- c('ID','sample','sample_new', 'group', 'disease', 'hasnCoV', 'cluster','celltype')\n", + "nCoV.integrated@meta.data <- nCoV.integrated@meta.data[, !(colnames(nCoV.integrated@meta.data) %in% to_be_deleted)]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "466c2441-c353-43a5-b117-bfdf91292cf3", + "metadata": {}, + "outputs": [], + "source": [ + "annotated_df <- read.table('covid_balf/singlecellstudy/data/all.cell.annotation.meta.txt',\n", + " header = TRUE)\n", + "annotated_df <- annotated_df %>%\n", + " dplyr::filter(sample != 'GSM3660650')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "3ce67037-f5bc-4c14-8e33-b6e1a9aac16c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 8
IDsamplesample_newgroupdiseasehasnCoVclustercelltype
<chr><chr><chr><chr><chr><chr><int><chr>
1AAACCTGAGACACTAA_1C51HC1HCNN3Macrophages
2AAACCTGAGGAGTACC_1C51HC1HCNN3Macrophages
3AAACCTGAGGATATAC_1C51HC1HCNN3Macrophages
4AAACCTGAGGTCATCT_1C51HC1HCNN3Macrophages
5AAACCTGCACGGATAG_1C51HC1HCNN5Macrophages
6AAACCTGCAGGGAGAG_1C51HC1HCNN2Macrophages
\n" + ], + "text/latex": [ + "A data.frame: 6 × 8\n", + "\\begin{tabular}{r|llllllll}\n", + " & ID & sample & sample\\_new & group & disease & hasnCoV & cluster & celltype\\\\\n", + " & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & AAACCTGAGACACTAA\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\t2 & AAACCTGAGGAGTACC\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\t3 & AAACCTGAGGATATAC\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\t4 & AAACCTGAGGTCATCT\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\t5 & AAACCTGCACGGATAG\\_1 & C51 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\t6 & AAACCTGCAGGGAGAG\\_1 & C51 & HC1 & HC & N & N & 2 & Macrophages\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 8\n", + "\n", + "| | ID <chr> | sample <chr> | sample_new <chr> | group <chr> | disease <chr> | hasnCoV <chr> | cluster <int> | celltype <chr> |\n", + "|---|---|---|---|---|---|---|---|---|\n", + "| 1 | AAACCTGAGACACTAA_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| 2 | AAACCTGAGGAGTACC_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| 3 | AAACCTGAGGATATAC_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| 4 | AAACCTGAGGTCATCT_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| 5 | AAACCTGCACGGATAG_1 | C51 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| 6 | AAACCTGCAGGGAGAG_1 | C51 | HC1 | HC | N | N | 2 | Macrophages |\n", + "\n" + ], + "text/plain": [ + " ID sample sample_new group disease hasnCoV cluster\n", + "1 AAACCTGAGACACTAA_1 C51 HC1 HC N N 3 \n", + "2 AAACCTGAGGAGTACC_1 C51 HC1 HC N N 3 \n", + "3 AAACCTGAGGATATAC_1 C51 HC1 HC N N 3 \n", + "4 AAACCTGAGGTCATCT_1 C51 HC1 HC N N 3 \n", + "5 AAACCTGCACGGATAG_1 C51 HC1 HC N N 5 \n", + "6 AAACCTGCAGGGAGAG_1 C51 HC1 HC N N 2 \n", + " celltype \n", + "1 Macrophages\n", + "2 Macrophages\n", + "3 Macrophages\n", + "4 Macrophages\n", + "5 Macrophages\n", + "6 Macrophages" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(annotated_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "6680d403-5f65-4922-bdd9-15b5fd7b656c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 6
orig.identnCount_RNAnFeature_RNApercent.mitointegrated_snn_res.1.2seurat_clusters
<chr><dbl><int><dbl><fct><fct>
AAACCTGAGACACTAA-1_1C51522816351.5493502 2
AAACCTGAGGAGTACC-1_1C51507113851.7945181212
AAACCTGAGGATATAC-1_1C51553117291.6271922 2
AAACCTGAGGTCATCT-1_1C51563016391.9538192 2
AAACCTGCACGGATAG-1_1C51735817222.7724936 6
AAACCTGCAGGGAGAG-1_1C51461513121.4734562 2
\n" + ], + "text/latex": [ + "A data.frame: 6 × 6\n", + "\\begin{tabular}{r|llllll}\n", + " & orig.ident & nCount\\_RNA & nFeature\\_RNA & percent.mito & integrated\\_snn\\_res.1.2 & seurat\\_clusters\\\\\n", + " & & & & & & \\\\\n", + "\\hline\n", + "\tAAACCTGAGACACTAA-1\\_1 & C51 & 5228 & 1635 & 1.549350 & 2 & 2 \\\\\n", + "\tAAACCTGAGGAGTACC-1\\_1 & C51 & 5071 & 1385 & 1.794518 & 12 & 12\\\\\n", + "\tAAACCTGAGGATATAC-1\\_1 & C51 & 5531 & 1729 & 1.627192 & 2 & 2 \\\\\n", + "\tAAACCTGAGGTCATCT-1\\_1 & C51 & 5630 & 1639 & 1.953819 & 2 & 2 \\\\\n", + "\tAAACCTGCACGGATAG-1\\_1 & C51 & 7358 & 1722 & 2.772493 & 6 & 6 \\\\\n", + "\tAAACCTGCAGGGAGAG-1\\_1 & C51 & 4615 & 1312 & 1.473456 & 2 & 2 \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 6\n", + "\n", + "| | orig.ident <chr> | nCount_RNA <dbl> | nFeature_RNA <int> | percent.mito <dbl> | integrated_snn_res.1.2 <fct> | seurat_clusters <fct> |\n", + "|---|---|---|---|---|---|---|\n", + "| AAACCTGAGACACTAA-1_1 | C51 | 5228 | 1635 | 1.549350 | 2 | 2 |\n", + "| AAACCTGAGGAGTACC-1_1 | C51 | 5071 | 1385 | 1.794518 | 12 | 12 |\n", + "| AAACCTGAGGATATAC-1_1 | C51 | 5531 | 1729 | 1.627192 | 2 | 2 |\n", + "| AAACCTGAGGTCATCT-1_1 | C51 | 5630 | 1639 | 1.953819 | 2 | 2 |\n", + "| AAACCTGCACGGATAG-1_1 | C51 | 7358 | 1722 | 2.772493 | 6 | 6 |\n", + "| AAACCTGCAGGGAGAG-1_1 | C51 | 4615 | 1312 | 1.473456 | 2 | 2 |\n", + "\n" + ], + "text/plain": [ + " orig.ident nCount_RNA nFeature_RNA percent.mito\n", + "AAACCTGAGACACTAA-1_1 C51 5228 1635 1.549350 \n", + "AAACCTGAGGAGTACC-1_1 C51 5071 1385 1.794518 \n", + "AAACCTGAGGATATAC-1_1 C51 5531 1729 1.627192 \n", + "AAACCTGAGGTCATCT-1_1 C51 5630 1639 1.953819 \n", + "AAACCTGCACGGATAG-1_1 C51 7358 1722 2.772493 \n", + "AAACCTGCAGGGAGAG-1_1 C51 4615 1312 1.473456 \n", + " integrated_snn_res.1.2 seurat_clusters\n", + "AAACCTGAGACACTAA-1_1 2 2 \n", + "AAACCTGAGGAGTACC-1_1 12 12 \n", + "AAACCTGAGGATATAC-1_1 2 2 \n", + "AAACCTGAGGTCATCT-1_1 2 2 \n", + "AAACCTGCACGGATAG-1_1 6 6 \n", + "AAACCTGCAGGGAGAG-1_1 2 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(nCoV.integrated@meta.data)" + ] + }, + { + "cell_type": "markdown", + "id": "8054cb7f-e3c2-49d5-96a2-6b9fc9ee52b8", + "metadata": {}, + "source": [ + "### Patient meta-data and available integrated dataframe have been compared against each other using various EDA methods" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "0819b4b9-6e7d-4959-bd1c-830018c8c382", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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h2Ob1oHpPSAZAWkgi8783GBpA7NNw0k63B80zogpQckKyAVfNmZjwskdWi+aSBZ\nh+Ob1gEpPSBZAangy858XCCpQ/NNA8k6HN+0DkjpAckKSAVfdubjAkkdmm8aSNbh+KZ1QEoP\nSFZAKviyMx8XSOrQfNNAsg7HN60DUnpAsgJSwZed+bhAUofmmwaSdTi+aR2Q0gOSFZAKvuzM\nxwWSOjTfNJCsw/FN64CUHpCsgFTwZWc+LpDUofmmgWQdjm9aB6T0gGQFpIIvO/NxgaQOzTcN\nJOtwfNM6IKUHJCsgFXzZmY8LJHVovmkgWYfjm9YBKT0gWQGp4MvOfNxpDsl12TmHgdS90yni\naTUCksp12TmHgdS90yniaTUCksp12TmHgdS90yniaTUCksp12TmHgdS90yniaTUCksp12TmH\ngdS90yniaTUCksp12TmHgdS90yniaTUCksp12TmHgdS90yniaTUCksp12TmHgdS90yniaTUC\nksp12TmHgdS90yniaTUCksp12TmHgdS90yniaTUCksp12TmHgdS90yniaTUCksp12TmHgdS9\n0yniaTUCksp12TmHgdS90yniaTUCksp12TmHewdShdfnGgZSHJDUNJCMYSDFAUlNdxHSvoHJ\ncu7QNeybLmLjRvVG5l26tfT+MJR5H91YcAzvDaOZ93Fdds7hQqZbl9clSEOHyrlD17BvuoiN\nGzUamXfp1tIj4UDmfXT1kH0f2XAYy7yP67JzDhcy3axbkPjUTsandirXZeccnu6f2gFJBiSV\n67JzDgOpe9NFbNwISCrXZeccBlL3povYuBGQVK7LzjkMpO5NF7FxIyCpXJedcxhI3ZsuYuNG\nQFK5LjvnMJC6N13Exo2ApHJdds5hIHVvuoiNGwFJ5brsnMNA6t50ERs3ApLKddk5h4HUveki\nNm4EJJXrsnMOA6l700Vs3AhIKtdl5xwGUvemi9i4EZBUrsvOOQyk7k0XsXEjIKlcl51zGEjd\nmy5i40ZAUrkuO+cwkLo3XcTGjYCkcl12zmEgdW+6iI0bAUnluuycw0Dq3nQRGzcCksp12TmH\ngdS96SI2bjSTIeXceBHPVyVLAyl7GEhxQFLTQDKGgRQHJDUNJGMYSHFAUtNAMoaBFAckNQ0k\nYxhIcUBS00AyhoEUByQ1DSRjGEhxQFLTQDKGgRQHJDUNJGMYSHFAUtNAMoaBFAckNQ0kYxhI\ncUBS00AyhoEUByQ1DSRjGEhxQFLTQDKGgRQHJDUNJGP4SQop59LVP1+VLA2k7GEg5Vi7kunq\nNw4kYxhIOdauZLr6jQPJGAZSjrUrma5+40AyhoGUY+1KpqvfOJCMYSDlWLuS6eo3DiRjGEg5\n1q5kuvqNA8kYBlKOtSuZrn7jQDKGgZRj7Uqmq984kIxhIOVYu5Lp6jcOJGMYSDnWrmS6+o0D\nyRgGUo61K5mufuNAMoaBlGPtSqar3ziQjGEg5Vi7kunqNw4kYxhIOdauZLr6jQPJGAZSjrUr\nma5+40AyhoGUY+1KpqvfOJCMYSDlWLuS6eo3DiRjuHchuaZzDlf/fFWyNJCyh4HUK9PVbxxI\nxjCQemW6+o0DyRgGUq9MV79xIBnDQOqV6eo3DiRjGEi9Ml39xoFkDAOpV6ar3ziQjGEg9cp0\n9RsHkjEMpF6Zrn7jQDKGgdQr09VvHEjGMJB6Zbr6jQPJGAZSr0xXv3EgGcNA6pXp6jcOJGMY\nSL0yXf3GgWQMA6lXpqvfOJCMYSD1ynT1GweSMQykXpmufuNAMoaB1CvT1W8cSMYwkHpluvqN\nA8kYBlKvTFe/cSAZw0DqlenqNw4kYxhIvTJd/caBZAwDqVemq984kIxhIPXKdPUbB5IxDKRe\nma5+40AyhoHUK9PVbxxIxjCQemW6+o0DyRgGUq9MV79xIBnDQOqV6eo3DiRjGEi9Ml39xoFk\nDAOpV6ar3ziQjGEg9cp09RsHkjEMpF6Zrn7jQDKGgdQr09VvHEjGMJB6Zbr6jQPJGAZSr0xX\nv3EgGcNA6pXp6jcOJGMYSL0yXf3GgWQMA6lXpqvfOJCMYTbeK9PVbxxIxjAb75Xp6jcOJGOY\njffKdPUbB5IxzMZ7Zbr6jQPJGGbjvTJd/caBZAyz8V6Zrn7jQDKG2XivTFe/cSAZw2y8V6ar\n3ziQjGE23ivT1W8cSMYwG++V6eo3XiSkvVedsWjldiCx8dKnq994kZBWXbLl0SuX14HExsue\nrn7jBULaMWdz86PSvPuAxMbLnq5+4wVCuntBo/ny/JuAxMbLnq5+42lHgIMAAAuWSURBVAVC\nWv/+1stL1zZf3DOn2b1jk+XcoWvYN83Ge2u6+o2PjY0WBmmZgqRrhA7uJAue6XpoOKYboe6Y\nrnDjddfGnc/XjN54YZB+MPGp3c2Tbz/xqZ1uOOzu4F6qxphjeE8YckwPdXR5qg7+YZ/u8TDs\nmB4M/Y7p0eAY3hX2O6b3hgHH9EjYmX94Zxjp4F6FfWq3a84DIfTPvR9ImQEpvScPpHD5R7Zs\nXXFhA0iZASm9JxGkwTVLF69+4uGAJANSek8iSFFAkgEpPSBZASk9IOUISFZASg9I6QHJCkg5\nApIOSHkCUnpAAlJbQEoPSEBqC0jpAQlIbQEpPSABqS0gpQckILUFpPSABKS2gJQekIDUFpDS\nAxKQ2gJSekACUltASg9IQGoLSOkBCUhtASk9IAGpLSClByQgtQWk9IAEpLaAlB6QgNQWkNID\nEpDaAlJ6QAJSW0BKD0hAagtI6QEJSG0BKT0gAaktIKUHJCC1BaT0gASktoCUHpCA1BaQ0gMS\nkNoCUnpAAlJbQEoPSN3r25ftdExf9QXH8L9fdodj+juXPeKY/us1juHtl93qmP7+ZQ84pq9b\n7RgeuOwfHNM/vuxnjukbLzuQf3j0sq8l3b8CSCtm/cYx/dYFjuGfzLrGMX31rH91TM/5M8fw\nA7P+p2P6b2Z93zG95PWO4e2zPuqYvmmW578fy2ftyz+8f9YHku4PpM4DUnpA6l5ASg9IOQKS\nFZDSA1J6PQCJaOYFJKICAhJRAQGJqIBKgbTj82fNX7Lq/tarWy+a27rZe9UZi1Zuf+I2abjZ\n7X33HPZ2juldV55+ysc25Z1+eNWi0z7xq7zTU27Thz/c12xh7qVvPXv++T/KO/3IXyw+9WO/\nyDm8bfXpCz+7J3Xpycvt5Pk6/BEOHVQH7ydyuNNly4D0yOLldz/8s6vm/ksIdy1dM35Nqy7Z\n8uiVy+uHbpOGQ3h8yYJ7pr6dZ/q/X7L5t3+5eDjf9OgZV2/97Zr3DeVc+4nbHMPLbtnR+tGb\nnNO3L924/ZvnDOabbpxz7eD+ry0cyDU88oFVWx/65MdTNz55uR08X9EjTE528H6ihztcthRI\nnzhv/Ec1rr8xhDseu6d1TTvmbG5+NJp33+Rt0nCzy69bcs/Ut3NMD6x+OITH+n6db3rPPzYN\nbe3bnG96ym2O4fdutAazps/J/iEp47L7mh+Ed/dZ/4HWw5v6djaf+b6HEpc+eLmdPF/RI0we\nVAfvJ3K402XLgLSn7/Ypb41f090LGs2X5980eZs03Bw/e3ji3TDrgOzpEH41d3f7UKfTA5//\n0Eje6Sl7SB0e6bvmgjNXbzWGremdfXd8+L0XmZ+TWhv/6JqB4RvPNn6EzRi+v6/5kWxs3u1H\nHlTTh12u/XxFjzBlskNIRx7uZNlSIG3qm/ojk+PXtP79rVcvXTt5mzQc9i69N3QGyZ4OA+d9\nJfd0/T19Hzd/+NaanrKH5OE9S67etGnFEuvvGo3pTX0ff2Rg7fusr1Ssje9a3te39MF8w0OL\nvzA6esO8b6RNT73cjOcreoQpkx1COvJwJ8uWBOnfprw1AWlZ69UmpIO3ScPhr/4qdAzJmn7k\n3M83HNM/u/zcvTmnn3iUXEuHMLRwQ77pTX3Nz6PHTrM+LBjToxdcu2fw5sXGf5+tjf/83Hmn\n3nDutxOXbjVxuVnPV/sjTE52DOlIw50sWwqkvXNuG7+tj29n/Jp+MPEp3c2Tt0nD9y4d6BSS\nOX3folsc060/P3VdvunDHiXX0uG8G/NN7xj/j+5y48St6Z/OaX3VfaZhwd74vtHR+YmXPVHr\ncjOfr/ZHODjZKaQjDne0bDnfbPjMmePfJPo/l7Zejl/TrjnNp7N/7v2Tt0nDVyxYtGjRnFNW\nhw4OyJj+xWk/Tt/45PRPz9kfQmOxBcmYnnIF6cMPXTsawvDCO/MtXV/afLc4cMpd+aZ/0tf6\nX5ZaH1T08NhdzY9kP5rXn7b0ocvt4Pk6/BGmHFSn32w4wnCHy5YC6dHTz/7ewz9fs+CnIeze\nsWFu69/IXv6RLVtXXNg4dJsyPP6PJk/f0H/owXJNHzjn663Xck7vXfLZh7etXbAt3/ShK8g1\nvGjNtq2rl+3Pt3S4efG9O65ZmvOyB5deu/fAPyz4bb6lL1i94/4zPm+tfKTpg5fbyfN1+CMc\nOqgO3k/kcKfLlvQXsv9r2fyln219r/is1l9z9X0rDK5Zunh18z9Qk7dJw61anypMfTt5+r7x\nN/vMjynG2g99ZuEpF1vftjenp94mD2/+5Kmnr/pd3qXr1y+Z/7GH804/tHLx+/6H/a9W9fCj\nn3jv6X8zmrr0wcvt6Pk6/BEmD6qT9xM13PGy/IgQUQEBiaiAgERUQEAiKiAgERUQkIgKCEhE\nBQQkogIC0rRqdMmxxyT8XuSdLzsz6eFP+s9T3rj0P/wmaZisgDStWldb/O0j/evVe4/4PNXf\neYL5b3TbOgzS2NtmmT9nRCkBaVr11dqRf5z0miM+T39b+27awx8GKfzyqL9MGycdkKZTs2vN\nfhO+/rpjjpv19dYfbDj5WS9c+EB4V/OPZ4Vw21ue9Yw/vqrRBPGWW178xjD2ypObd/nt2S99\n+gvf0/o3r4fG3vLmu173jOOvGLnk+GfN3hzCn77hjtcd85xlew5C+u47jjvmxOtaj37KCxy/\nipQOC0jTqU2fqX1p44G/q81ft+7dtXVNR09559eue/l/3PbrubWNvwzfeMq7v3n7hbWPhvD2\nE1711+vC92pfbs684UVfuvOG17xgMDwxNvvFb/vJI/Nr71i59XvP/vMQ3vj81/7Ljr89ev4E\npNufevItGz5Ya30wurVm/CN/SgpI06qv1L4fwuq3Hwih//cWh/DaPxwN4YdP+1w4q/U8veql\nrV+WMO/onc2PXP/YfO1Tta3N+9U+1nz1wdWPThmbXbsvhO/X3tT8HxY/s8ln/PPFs2oPj0M6\n8RWtL8LmHDccwuDTzqrwWmdWQJpWjUOa6MVvCTtr5x18owXp0doHW69e1/yYM/tprV+78ucv\nbL4Yee7Lbp/668yaY2F2U094sHZx8+XFtYFw0jNb/+Dr+tptLUjbaxcMN/tCrfW77U74k3Iu\n60kQkKZV45D6P/XqZz/1qbWTws9rKw7+eQvSj2qrWq/eVlsbZh/feu31f9x6+c9/WHvughtG\np4yF2S9r/vlvapc3X15Sezyc9PLW/dbVvtKCdG/tYK2PaW9/cfmXOEMD0rRqHNLJT/3EXT/7\n+fEnhV/UPn3wz1uQNtZWtl69tfalCSnhVW8a/9/G7rj4v9ReO/TE2JEhfbN2/QSkM+8Zb0fz\nz97zrNKvcKYGpGlVC9IDtXOar40+46QwUBv/LUsPPTYOaVvt3NZba2vrD0Ka+Ig03udrX31i\nrA3SMWPN177YnGtC2lU744nl3v4HZV3YjA9I06oWpF+Of+S5pvaGEF7z/IEQftX8BO/sWvNz\nt1cf3/rVAe8+tv8gpPGvkX58autXpz9Yu3LKWAyp1vrlOPOevnv8mw2v//3Hm29df2nr33zz\nNVJhAWla1YI08pI/+NY/X/TWtx535751R/23G9e+8gXbwqdrK/8+3HrUO7/1fz/U8jEB6dLW\nd+22HXfCdd/5uzc9+8EpYzGkl7zyf3/n4trSiW9/f/foE67/f588uvWrOQefvqzSq51JAWla\nNf410sY3HvvCD/Tf8rznbAq3vuHYF8z/dQiPnHh008CGNz/z6Sd+OUxC+qda6zeA/uv8Fxx9\n/PyfTh2LIb3qxycf+5xz9h78C9nv/9lxR7/yitYHpNtqX6/sSmdaQOrdRv/TWzu63+E/GDSl\n9z3P+lWxlBKQerjrxU/mRSlIvzrqiiJ38+QOSD1c/R1/0smvPRSQ6rNP7GSaOgpIvdyOjv49\nkoD0qedsKXY3T+qARFRAQCIqICARFRCQiAoISEQFBCSiAgISUQEBiaiA/j+4mJQCVuVCUAAA\nAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p <- ggplot(annotated_df)\n", + "p + aes(factor(sample)) + geom_bar()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "7480dad8-b747-4e63-900f-b5a5d08c88d8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p <- ggplot(nCoV.integrated@meta.data)\n", + "p + aes(factor(orig.ident)) + geom_bar()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "f03878d0-af37-4241-9d60-5d0956fc2285", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'data.frame':\t63103 obs. of 8 variables:\n", + " $ ID : chr \"AAACCTGAGACACTAA_1\" \"AAACCTGAGGAGTACC_1\" \"AAACCTGAGGATATAC_1\" \"AAACCTGAGGTCATCT_1\" ...\n", + " $ sample : chr \"C51\" \"C51\" \"C51\" \"C51\" ...\n", + " $ sample_new: chr \"HC1\" \"HC1\" \"HC1\" \"HC1\" ...\n", + " $ group : chr \"HC\" \"HC\" \"HC\" \"HC\" ...\n", + " $ disease : chr \"N\" \"N\" \"N\" \"N\" ...\n", + " $ hasnCoV : chr \"N\" \"N\" \"N\" \"N\" ...\n", + " $ cluster : int 3 3 3 3 5 2 8 5 5 5 ...\n", + " $ celltype : chr \"Macrophages\" \"Macrophages\" \"Macrophages\" \"Macrophages\" ...\n", + "'data.frame':\t63734 obs. of 6 variables:\n", + " $ orig.ident : chr \"C51\" \"C51\" \"C51\" \"C51\" ...\n", + " $ nCount_RNA : num 5228 5071 5531 5630 7358 ...\n", + " $ nFeature_RNA : int 1635 1385 1729 1639 1722 1312 1307 2211 1859 1835 ...\n", + " $ percent.mito : num 1.55 1.79 1.63 1.95 2.77 ...\n", + " $ integrated_snn_res.1.2: Factor w/ 30 levels \"0\",\"1\",\"10\",\"11\",..: 13 5 13 13 27 13 25 6 6 6 ...\n", + " $ seurat_clusters : Factor w/ 30 levels \"0\",\"1\",\"2\",\"3\",..: 3 13 3 3 7 3 5 14 14 14 ...\n" + ] + }, + { + "data": { + "text/plain": [ + "NULL" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c(str(annotated_df), str(nCoV.integrated@meta.data))" + ] + }, + { + "cell_type": "markdown", + "id": "0c5cb807-afc3-46bf-9376-68de7956072e", + "metadata": {}, + "source": [ + "> 63103 cells have been annotated, but 63734 cells exist ready to be analyzed.\n", + ">\n", + "> Barcode distributions for both graphs seem really similar. " + ] + }, + { + "cell_type": "markdown", + "id": "377e6f9a-9330-4580-a6cb-664776735a11", + "metadata": {}, + "source": [ + "### Can we compensate the differences??" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "79743984-c0cf-467c-9d00-b85fa67d2f40", + "metadata": {}, + "outputs": [], + "source": [ + "####add sample info\n", + "sample_info = as.data.frame(colnames(nCoV.integrated))\n", + "colnames(sample_info) = c('ID')\n", + "rownames(sample_info) = sample_info$ID\n", + "sample_info$sample = nCoV.integrated@meta.data$orig.ident\n", + "rownames(sample_info) <- sample_info$ID\n", + "sample_info$ID <- NULL" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "78615d94-ae9a-4c70-9d08-accea477db42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 8
IDsamplesample_newgroupdiseasehasnCoVclustercelltype
<chr><chr><chr><chr><chr><chr><int><chr>
63098TTTGGTTTCAAACCAC_13C152S6SYN13Epithelial
63099TTTGGTTTCGCTTGTC_13C152S6SYN 1Macrophages
63100TTTGTCAAGAAACGCC_13C152S6SYN14T
63101TTTGTCACAACACCCG_13C152S6SYN19Plasma
63102TTTGTCAGTCTGCAAT_13C152S6SYN19Plasma
63103TTTGTCAGTGTTGGGA_13C152S6SYN10Macrophages
\n" + ], + "text/latex": [ + "A data.frame: 6 × 8\n", + "\\begin{tabular}{r|llllllll}\n", + " & ID & sample & sample\\_new & group & disease & hasnCoV & cluster & celltype\\\\\n", + " & & & & & & & & \\\\\n", + "\\hline\n", + "\t63098 & TTTGGTTTCAAACCAC\\_13 & C152 & S6 & S & Y & N & 13 & Epithelial \\\\\n", + "\t63099 & TTTGGTTTCGCTTGTC\\_13 & C152 & S6 & S & Y & N & 1 & Macrophages\\\\\n", + "\t63100 & TTTGTCAAGAAACGCC\\_13 & C152 & S6 & S & Y & N & 14 & T \\\\\n", + "\t63101 & TTTGTCACAACACCCG\\_13 & C152 & S6 & S & Y & N & 19 & Plasma \\\\\n", + "\t63102 & TTTGTCAGTCTGCAAT\\_13 & C152 & S6 & S & Y & N & 19 & Plasma \\\\\n", + "\t63103 & TTTGTCAGTGTTGGGA\\_13 & C152 & S6 & S & Y & N & 10 & Macrophages\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 8\n", + "\n", + "| | ID <chr> | sample <chr> | sample_new <chr> | group <chr> | disease <chr> | hasnCoV <chr> | cluster <int> | celltype <chr> |\n", + "|---|---|---|---|---|---|---|---|---|\n", + "| 63098 | TTTGGTTTCAAACCAC_13 | C152 | S6 | S | Y | N | 13 | Epithelial |\n", + "| 63099 | TTTGGTTTCGCTTGTC_13 | C152 | S6 | S | Y | N | 1 | Macrophages |\n", + "| 63100 | TTTGTCAAGAAACGCC_13 | C152 | S6 | S | Y | N | 14 | T |\n", + "| 63101 | TTTGTCACAACACCCG_13 | C152 | S6 | S | Y | N | 19 | Plasma |\n", + "| 63102 | TTTGTCAGTCTGCAAT_13 | C152 | S6 | S | Y | N | 19 | Plasma |\n", + "| 63103 | TTTGTCAGTGTTGGGA_13 | C152 | S6 | S | Y | N | 10 | Macrophages |\n", + "\n" + ], + "text/plain": [ + " ID sample sample_new group disease hasnCoV cluster\n", + "63098 TTTGGTTTCAAACCAC_13 C152 S6 S Y N 13 \n", + "63099 TTTGGTTTCGCTTGTC_13 C152 S6 S Y N 1 \n", + "63100 TTTGTCAAGAAACGCC_13 C152 S6 S Y N 14 \n", + "63101 TTTGTCACAACACCCG_13 C152 S6 S Y N 19 \n", + "63102 TTTGTCAGTCTGCAAT_13 C152 S6 S Y N 19 \n", + "63103 TTTGTCAGTGTTGGGA_13 C152 S6 S Y N 10 \n", + " celltype \n", + "63098 Epithelial \n", + "63099 Macrophages\n", + "63100 T \n", + "63101 Plasma \n", + "63102 Plasma \n", + "63103 Macrophages" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 1
sample
<chr>
TTTGGTTTCAAACCAC-1_12C152
TTTGGTTTCGCTTGTC-1_12C152
TTTGTCAAGAAACGCC-1_12C152
TTTGTCACAACACCCG-1_12C152
TTTGTCAGTCTGCAAT-1_12C152
TTTGTCAGTGTTGGGA-1_12C152
\n" + ], + "text/latex": [ + "A data.frame: 6 × 1\n", + "\\begin{tabular}{r|l}\n", + " & sample\\\\\n", + " & \\\\\n", + "\\hline\n", + "\tTTTGGTTTCAAACCAC-1\\_12 & C152\\\\\n", + "\tTTTGGTTTCGCTTGTC-1\\_12 & C152\\\\\n", + "\tTTTGTCAAGAAACGCC-1\\_12 & C152\\\\\n", + "\tTTTGTCACAACACCCG-1\\_12 & C152\\\\\n", + "\tTTTGTCAGTCTGCAAT-1\\_12 & C152\\\\\n", + "\tTTTGTCAGTGTTGGGA-1\\_12 & C152\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 1\n", + "\n", + "| | sample <chr> |\n", + "|---|---|\n", + "| TTTGGTTTCAAACCAC-1_12 | C152 |\n", + "| TTTGGTTTCGCTTGTC-1_12 | C152 |\n", + "| TTTGTCAAGAAACGCC-1_12 | C152 |\n", + "| TTTGTCACAACACCCG-1_12 | C152 |\n", + "| TTTGTCAGTCTGCAAT-1_12 | C152 |\n", + "| TTTGTCAGTGTTGGGA-1_12 | C152 |\n", + "\n" + ], + "text/plain": [ + " sample\n", + "TTTGGTTTCAAACCAC-1_12 C152 \n", + "TTTGGTTTCGCTTGTC-1_12 C152 \n", + "TTTGTCAAGAAACGCC-1_12 C152 \n", + "TTTGTCACAACACCCG-1_12 C152 \n", + "TTTGTCAGTCTGCAAT-1_12 C152 \n", + "TTTGTCAGTGTTGGGA-1_12 C152 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tail(annotated_df)\n", + "tail(sample_info)" + ] + }, + { + "cell_type": "markdown", + "id": "acaa5193-786f-45a8-b322-459a9df96ba2", + "metadata": {}, + "source": [ + "### Barcode names change to match that of analysis-ready data" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "f7edb901-a3c6-4895-9e71-3e1903e1fcc7", + "metadata": {}, + "outputs": [], + "source": [ + "modify_names <- function(l){\n", + " paste0(strsplit(l, split = '_')[[1]][1],\n", + " '-1_',\n", + " strsplit(l, split = '_')[[1]][2])\n", + "} \n", + "annotated_df$ID <- as.character(lapply(annotated_df$ID,modify_names))\n", + "rownames(annotated_df) = annotated_df$ID\n", + "annotated_df$ID <- NULL" + ] + }, + { + "cell_type": "markdown", + "id": "13d3235f-0419-438e-995c-282a0e9b173b", + "metadata": {}, + "source": [ + "> Use semi_join function to match up the dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "020383c2-feab-4b57-b665-f58f47b30d00", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Joining, by = \"sample\"\n", + "\n" + ] + } + ], + "source": [ + "sample_info <- dplyr::semi_join(annotated_df, sample_info)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "02ce3a15-5ae9-4151-8a66-ab04f2d0b5a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'data.frame':\t63103 obs. of 7 variables:\n", + " $ sample : chr \"C51\" \"C51\" \"C51\" \"C51\" ...\n", + " $ sample_new: chr \"HC1\" \"HC1\" \"HC1\" \"HC1\" ...\n", + " $ group : chr \"HC\" \"HC\" \"HC\" \"HC\" ...\n", + " $ disease : chr \"N\" \"N\" \"N\" \"N\" ...\n", + " $ hasnCoV : chr \"N\" \"N\" \"N\" \"N\" ...\n", + " $ cluster : int 3 3 3 3 5 2 8 5 5 5 ...\n", + " $ celltype : chr \"Macrophages\" \"Macrophages\" \"Macrophages\" \"Macrophages\" ...\n" + ] + } + ], + "source": [ + "str(sample_info)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "7cdcad8c-db2c-4a88-aa7c-4d7b4801b20e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 63103 × 7
samplesample_newgroupdiseasehasnCoVclustercelltype
<chr><chr><chr><chr><chr><int><chr>
AAACCTGAGACACTAA-1_1C51HC1HCNN 3Macrophages
AAACCTGAGGAGTACC-1_1C51HC1HCNN 3Macrophages
AAACCTGAGGATATAC-1_1C51HC1HCNN 3Macrophages
AAACCTGAGGTCATCT-1_1C51HC1HCNN 3Macrophages
AAACCTGCACGGATAG-1_1C51HC1HCNN 5Macrophages
AAACCTGCAGGGAGAG-1_1C51HC1HCNN 2Macrophages
AAACCTGGTCTAAACC-1_1C51HC1HCNN 8Macrophages
AAACCTGGTGCACCAC-1_1C51HC1HCNN 5Macrophages
AAACCTGGTTACGCGC-1_1C51HC1HCNN 5Macrophages
AAACCTGTCCGCATCT-1_1C51HC1HCNN 5Macrophages
AAACGGGAGCTACCTA-1_1C51HC1HCNN 3Macrophages
AAACGGGAGGAATTAC-1_1C51HC1HCNN18Macrophages
AAACGGGAGGACCACA-1_1C51HC1HCNN 4Macrophages
AAACGGGAGGCAAAGA-1_1C51HC1HCNN 1Macrophages
AAACGGGAGTCGTTTG-1_1C51HC1HCNN 5Macrophages
AAACGGGCAAGGACTG-1_1C51HC1HCNN 3Macrophages
AAACGGGCACCGAAAG-1_1C51HC1HCNN 3Macrophages
AAACGGGCAGCCAATT-1_1C51HC1HCNN20mDC
AAACGGGCATCCGTGG-1_1C51HC1HCNN 2Macrophages
AAACGGGCATGCCTTC-1_1C51HC1HCNN 3Macrophages
AAACGGGGTACATGTC-1_1C51HC1HCNN 5Macrophages
AAACGGGGTCCATGAT-1_1C51HC1HCNN 8Macrophages
AAACGGGGTCTCTCTG-1_1C51HC1HCNN20mDC
AAACGGGGTGCGAAAC-1_1C51HC1HCNN 4Macrophages
AAACGGGGTTCTGAAC-1_1C51HC1HCNN 8Macrophages
AAACGGGTCCAAACTG-1_1C51HC1HCNN 4Macrophages
AAACGGGTCCTTTACA-1_1C51HC1HCNN21Macrophages
AAACGGGTCGCAAACT-1_1C51HC1HCNN 5Macrophages
AAAGATGAGACAGAGA-1_1C51HC1HCNN 3Macrophages
AAAGATGAGAGGTTAT-1_1C51HC1HCNN 5Macrophages
⋮⋮⋮⋮⋮⋮⋮⋮
TTTATGCGTCGACTGC-1_13C152S6SYN19Plasma
TTTATGCGTTAAGATG-1_13C152S6SYN13Epithelial
TTTATGCTCGCACTCT-1_13C152S6SYY19Plasma
TTTCCTCAGAGACTTA-1_13C152S6SYN 9T
TTTCCTCAGATCTGAA-1_13C152S6SYN 0Macrophages
TTTCCTCAGGATGGAA-1_13C152S6SYN 1Macrophages
TTTCCTCCAAGTCATC-1_13C152S6SYN13Epithelial
TTTCCTCCAGCTCCGA-1_13C152S6SYN14T
TTTCCTCCAGGTCTCG-1_13C152S6SYN14T
TTTCCTCGTTAAGATG-1_13C152S6SYN19Plasma
TTTCCTCTCTTGTTTG-1_13C152S6SYN10Macrophages
TTTGCGCAGACTACAA-1_13C152S6SYN10Macrophages
TTTGCGCAGGGTGTGT-1_13C152S6SYN28Epithelial
TTTGCGCGTCGGCATC-1_13C152S6SYN19Plasma
TTTGCGCTCCAGGGCT-1_13C152S6SYN19Plasma
TTTGCGCTCTAACCGA-1_13C152S6SYN 9T
TTTGCGCTCTGCTTGC-1_13C152S6SYN14T
TTTGGTTAGATCCCGC-1_13C152S6SYN 8Macrophages
TTTGGTTAGCACCGTC-1_13C152S6SYN 1Macrophages
TTTGGTTAGTCTCCTC-1_13C152S6SYN 1Macrophages
TTTGGTTCACGAGAGT-1_13C152S6SYN 1Macrophages
TTTGGTTCACTATCTT-1_13C152S6SYN14T
TTTGGTTCATCGGGTC-1_13C152S6SYN 1Macrophages
TTTGGTTGTCTCTCTG-1_13C152S6SYN 1Macrophages
TTTGGTTTCAAACCAC-1_13C152S6SYN13Epithelial
TTTGGTTTCGCTTGTC-1_13C152S6SYN 1Macrophages
TTTGTCAAGAAACGCC-1_13C152S6SYN14T
TTTGTCACAACACCCG-1_13C152S6SYN19Plasma
TTTGTCAGTCTGCAAT-1_13C152S6SYN19Plasma
TTTGTCAGTGTTGGGA-1_13C152S6SYN10Macrophages
\n" + ], + "text/latex": [ + "A data.frame: 63103 × 7\n", + "\\begin{tabular}{r|lllllll}\n", + " & sample & sample\\_new & group & disease & hasnCoV & cluster & celltype\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\tAAACCTGAGACACTAA-1\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGAGGAGTACC-1\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGAGGATATAC-1\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGAGGTCATCT-1\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGCACGGATAG-1\\_1 & C51 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACCTGCAGGGAGAG-1\\_1 & C51 & HC1 & HC & N & N & 2 & Macrophages\\\\\n", + "\tAAACCTGGTCTAAACC-1\\_1 & C51 & HC1 & HC & N & N & 8 & Macrophages\\\\\n", + "\tAAACCTGGTGCACCAC-1\\_1 & C51 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACCTGGTTACGCGC-1\\_1 & C51 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACCTGTCCGCATCT-1\\_1 & C51 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACGGGAGCTACCTA-1\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGAGGAATTAC-1\\_1 & C51 & HC1 & HC & N & N & 18 & Macrophages\\\\\n", + "\tAAACGGGAGGACCACA-1\\_1 & C51 & HC1 & HC & N & N & 4 & Macrophages\\\\\n", + "\tAAACGGGAGGCAAAGA-1\\_1 & C51 & HC1 & HC & N & N & 1 & Macrophages\\\\\n", + "\tAAACGGGAGTCGTTTG-1\\_1 & C51 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACGGGCAAGGACTG-1\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGCACCGAAAG-1\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGCAGCCAATT-1\\_1 & C51 & HC1 & HC & N & N & 20 & mDC \\\\\n", + "\tAAACGGGCATCCGTGG-1\\_1 & C51 & HC1 & HC & N & N & 2 & Macrophages\\\\\n", + "\tAAACGGGCATGCCTTC-1\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGGTACATGTC-1\\_1 & C51 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACGGGGTCCATGAT-1\\_1 & C51 & HC1 & HC & N & N & 8 & Macrophages\\\\\n", + "\tAAACGGGGTCTCTCTG-1\\_1 & C51 & HC1 & HC & N & N & 20 & mDC \\\\\n", + "\tAAACGGGGTGCGAAAC-1\\_1 & C51 & HC1 & HC & N & N & 4 & Macrophages\\\\\n", + "\tAAACGGGGTTCTGAAC-1\\_1 & C51 & HC1 & HC & N & N & 8 & Macrophages\\\\\n", + "\tAAACGGGTCCAAACTG-1\\_1 & C51 & HC1 & HC & N & N & 4 & Macrophages\\\\\n", + "\tAAACGGGTCCTTTACA-1\\_1 & C51 & HC1 & HC & N & N & 21 & Macrophages\\\\\n", + "\tAAACGGGTCGCAAACT-1\\_1 & C51 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAAGATGAGACAGAGA-1\\_1 & C51 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAAGATGAGAGGTTAT-1\\_1 & C51 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\tTTTATGCGTCGACTGC-1\\_13 & C152 & S6 & S & Y & N & 19 & Plasma \\\\\n", + "\tTTTATGCGTTAAGATG-1\\_13 & C152 & S6 & S & Y & N & 13 & Epithelial \\\\\n", + "\tTTTATGCTCGCACTCT-1\\_13 & C152 & S6 & S & Y & Y & 19 & Plasma \\\\\n", + "\tTTTCCTCAGAGACTTA-1\\_13 & C152 & S6 & S & Y & N & 9 & T \\\\\n", + "\tTTTCCTCAGATCTGAA-1\\_13 & C152 & S6 & S & Y & N & 0 & Macrophages\\\\\n", + "\tTTTCCTCAGGATGGAA-1\\_13 & C152 & S6 & S & Y & N & 1 & Macrophages\\\\\n", + "\tTTTCCTCCAAGTCATC-1\\_13 & C152 & S6 & S & Y & N & 13 & Epithelial \\\\\n", + "\tTTTCCTCCAGCTCCGA-1\\_13 & C152 & S6 & S & Y & N & 14 & T \\\\\n", + "\tTTTCCTCCAGGTCTCG-1\\_13 & C152 & S6 & S & Y & N & 14 & T \\\\\n", + "\tTTTCCTCGTTAAGATG-1\\_13 & C152 & S6 & S & Y & N & 19 & Plasma \\\\\n", + "\tTTTCCTCTCTTGTTTG-1\\_13 & C152 & S6 & S & Y & N & 10 & Macrophages\\\\\n", + "\tTTTGCGCAGACTACAA-1\\_13 & C152 & S6 & S & Y & N & 10 & Macrophages\\\\\n", + "\tTTTGCGCAGGGTGTGT-1\\_13 & C152 & S6 & S & Y & N & 28 & Epithelial \\\\\n", + "\tTTTGCGCGTCGGCATC-1\\_13 & C152 & S6 & S & Y & N & 19 & Plasma \\\\\n", + "\tTTTGCGCTCCAGGGCT-1\\_13 & C152 & S6 & S & Y & N & 19 & Plasma \\\\\n", + "\tTTTGCGCTCTAACCGA-1\\_13 & C152 & S6 & S & Y & N & 9 & T \\\\\n", + "\tTTTGCGCTCTGCTTGC-1\\_13 & C152 & S6 & S & Y & N & 14 & T \\\\\n", + "\tTTTGGTTAGATCCCGC-1\\_13 & C152 & S6 & S & Y & N & 8 & Macrophages\\\\\n", + "\tTTTGGTTAGCACCGTC-1\\_13 & C152 & S6 & S & Y & N & 1 & Macrophages\\\\\n", + "\tTTTGGTTAGTCTCCTC-1\\_13 & C152 & S6 & S & Y & N & 1 & Macrophages\\\\\n", + "\tTTTGGTTCACGAGAGT-1\\_13 & C152 & S6 & S & Y & N & 1 & Macrophages\\\\\n", + "\tTTTGGTTCACTATCTT-1\\_13 & C152 & S6 & S & Y & N & 14 & T \\\\\n", + "\tTTTGGTTCATCGGGTC-1\\_13 & C152 & S6 & S & Y & N & 1 & Macrophages\\\\\n", + "\tTTTGGTTGTCTCTCTG-1\\_13 & C152 & S6 & S & Y & N & 1 & Macrophages\\\\\n", + "\tTTTGGTTTCAAACCAC-1\\_13 & C152 & S6 & S & Y & N & 13 & Epithelial \\\\\n", + "\tTTTGGTTTCGCTTGTC-1\\_13 & C152 & S6 & S & Y & N & 1 & Macrophages\\\\\n", + "\tTTTGTCAAGAAACGCC-1\\_13 & C152 & S6 & S & Y & N & 14 & T \\\\\n", + "\tTTTGTCACAACACCCG-1\\_13 & C152 & S6 & S & Y & N & 19 & Plasma \\\\\n", + "\tTTTGTCAGTCTGCAAT-1\\_13 & C152 & S6 & S & Y & N & 19 & Plasma \\\\\n", + "\tTTTGTCAGTGTTGGGA-1\\_13 & C152 & S6 & S & Y & N & 10 & Macrophages\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 63103 × 7\n", + "\n", + "| | sample <chr> | sample_new <chr> | group <chr> | disease <chr> | hasnCoV <chr> | cluster <int> | celltype <chr> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| AAACCTGAGACACTAA-1_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGAGGAGTACC-1_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGAGGATATAC-1_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGAGGTCATCT-1_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGCACGGATAG-1_1 | C51 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACCTGCAGGGAGAG-1_1 | C51 | HC1 | HC | N | N | 2 | Macrophages |\n", + "| AAACCTGGTCTAAACC-1_1 | C51 | HC1 | HC | N | N | 8 | Macrophages |\n", + "| AAACCTGGTGCACCAC-1_1 | C51 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACCTGGTTACGCGC-1_1 | C51 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACCTGTCCGCATCT-1_1 | C51 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACGGGAGCTACCTA-1_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGAGGAATTAC-1_1 | C51 | HC1 | HC | N | N | 18 | Macrophages |\n", + "| AAACGGGAGGACCACA-1_1 | C51 | HC1 | HC | N | N | 4 | Macrophages |\n", + "| AAACGGGAGGCAAAGA-1_1 | C51 | HC1 | HC | N | N | 1 | Macrophages |\n", + "| AAACGGGAGTCGTTTG-1_1 | C51 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACGGGCAAGGACTG-1_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGCACCGAAAG-1_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGCAGCCAATT-1_1 | C51 | HC1 | HC | N | N | 20 | mDC |\n", + "| AAACGGGCATCCGTGG-1_1 | C51 | HC1 | HC | N | N | 2 | Macrophages |\n", + "| AAACGGGCATGCCTTC-1_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGGTACATGTC-1_1 | C51 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACGGGGTCCATGAT-1_1 | C51 | HC1 | HC | N | N | 8 | Macrophages |\n", + "| AAACGGGGTCTCTCTG-1_1 | C51 | HC1 | HC | N | N | 20 | mDC |\n", + "| AAACGGGGTGCGAAAC-1_1 | C51 | HC1 | HC | N | N | 4 | Macrophages |\n", + "| AAACGGGGTTCTGAAC-1_1 | C51 | HC1 | HC | N | N | 8 | Macrophages |\n", + "| AAACGGGTCCAAACTG-1_1 | C51 | HC1 | HC | N | N | 4 | Macrophages |\n", + "| AAACGGGTCCTTTACA-1_1 | C51 | HC1 | HC | N | N | 21 | Macrophages |\n", + "| AAACGGGTCGCAAACT-1_1 | C51 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAAGATGAGACAGAGA-1_1 | C51 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAAGATGAGAGGTTAT-1_1 | C51 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", + "| TTTATGCGTCGACTGC-1_13 | C152 | S6 | S | Y | N | 19 | Plasma |\n", + "| TTTATGCGTTAAGATG-1_13 | C152 | S6 | S | Y | N | 13 | Epithelial |\n", + "| TTTATGCTCGCACTCT-1_13 | C152 | S6 | S | Y | Y | 19 | Plasma |\n", + "| TTTCCTCAGAGACTTA-1_13 | C152 | S6 | S | Y | N | 9 | T |\n", + "| TTTCCTCAGATCTGAA-1_13 | C152 | S6 | S | Y | N | 0 | Macrophages |\n", + "| TTTCCTCAGGATGGAA-1_13 | C152 | S6 | S | Y | N | 1 | Macrophages |\n", + "| TTTCCTCCAAGTCATC-1_13 | C152 | S6 | S | Y | N | 13 | Epithelial |\n", + "| TTTCCTCCAGCTCCGA-1_13 | C152 | S6 | S | Y | N | 14 | T |\n", + "| TTTCCTCCAGGTCTCG-1_13 | C152 | S6 | S | Y | N | 14 | T |\n", + "| TTTCCTCGTTAAGATG-1_13 | C152 | S6 | S | Y | N | 19 | Plasma |\n", + "| TTTCCTCTCTTGTTTG-1_13 | C152 | S6 | S | Y | N | 10 | Macrophages |\n", + "| TTTGCGCAGACTACAA-1_13 | C152 | S6 | S | Y | N | 10 | Macrophages |\n", + "| TTTGCGCAGGGTGTGT-1_13 | C152 | S6 | S | Y | N | 28 | Epithelial |\n", + "| TTTGCGCGTCGGCATC-1_13 | C152 | S6 | S | Y | N | 19 | Plasma |\n", + "| TTTGCGCTCCAGGGCT-1_13 | C152 | S6 | S | Y | N | 19 | Plasma |\n", + "| TTTGCGCTCTAACCGA-1_13 | C152 | S6 | S | Y | N | 9 | T |\n", + "| TTTGCGCTCTGCTTGC-1_13 | C152 | S6 | S | Y | N | 14 | T |\n", + "| TTTGGTTAGATCCCGC-1_13 | C152 | S6 | S | Y | N | 8 | Macrophages |\n", + "| TTTGGTTAGCACCGTC-1_13 | C152 | S6 | S | Y | N | 1 | Macrophages |\n", + "| TTTGGTTAGTCTCCTC-1_13 | C152 | S6 | S | Y | N | 1 | Macrophages |\n", + "| TTTGGTTCACGAGAGT-1_13 | C152 | S6 | S | Y | N | 1 | Macrophages |\n", + "| TTTGGTTCACTATCTT-1_13 | C152 | S6 | S | Y | N | 14 | T |\n", + "| TTTGGTTCATCGGGTC-1_13 | C152 | S6 | S | Y | N | 1 | Macrophages |\n", + "| TTTGGTTGTCTCTCTG-1_13 | C152 | S6 | S | Y | N | 1 | Macrophages |\n", + "| TTTGGTTTCAAACCAC-1_13 | C152 | S6 | S | Y | N | 13 | Epithelial |\n", + "| TTTGGTTTCGCTTGTC-1_13 | C152 | S6 | S | Y | N | 1 | Macrophages |\n", + "| TTTGTCAAGAAACGCC-1_13 | C152 | S6 | S | Y | N | 14 | T |\n", + "| TTTGTCACAACACCCG-1_13 | C152 | S6 | S | Y | N | 19 | Plasma |\n", + "| TTTGTCAGTCTGCAAT-1_13 | C152 | S6 | S | Y | N | 19 | Plasma |\n", + "| TTTGTCAGTGTTGGGA-1_13 | C152 | S6 | S | Y | N | 10 | Macrophages |\n", + "\n" + ], + "text/plain": [ + " sample sample_new group disease hasnCoV cluster\n", + "AAACCTGAGACACTAA-1_1 C51 HC1 HC N N 3 \n", + "AAACCTGAGGAGTACC-1_1 C51 HC1 HC N N 3 \n", + "AAACCTGAGGATATAC-1_1 C51 HC1 HC N N 3 \n", + "AAACCTGAGGTCATCT-1_1 C51 HC1 HC N N 3 \n", + "AAACCTGCACGGATAG-1_1 C51 HC1 HC N N 5 \n", + "AAACCTGCAGGGAGAG-1_1 C51 HC1 HC N N 2 \n", + "AAACCTGGTCTAAACC-1_1 C51 HC1 HC N N 8 \n", + "AAACCTGGTGCACCAC-1_1 C51 HC1 HC N N 5 \n", + "AAACCTGGTTACGCGC-1_1 C51 HC1 HC N N 5 \n", + "AAACCTGTCCGCATCT-1_1 C51 HC1 HC N N 5 \n", + "AAACGGGAGCTACCTA-1_1 C51 HC1 HC N N 3 \n", + "AAACGGGAGGAATTAC-1_1 C51 HC1 HC N N 18 \n", + "AAACGGGAGGACCACA-1_1 C51 HC1 HC N N 4 \n", + "AAACGGGAGGCAAAGA-1_1 C51 HC1 HC N N 1 \n", + "AAACGGGAGTCGTTTG-1_1 C51 HC1 HC N N 5 \n", + "AAACGGGCAAGGACTG-1_1 C51 HC1 HC N N 3 \n", + "AAACGGGCACCGAAAG-1_1 C51 HC1 HC N N 3 \n", + "AAACGGGCAGCCAATT-1_1 C51 HC1 HC N N 20 \n", + "AAACGGGCATCCGTGG-1_1 C51 HC1 HC N N 2 \n", + "AAACGGGCATGCCTTC-1_1 C51 HC1 HC N N 3 \n", + "AAACGGGGTACATGTC-1_1 C51 HC1 HC N N 5 \n", + "AAACGGGGTCCATGAT-1_1 C51 HC1 HC N N 8 \n", + "AAACGGGGTCTCTCTG-1_1 C51 HC1 HC N N 20 \n", + "AAACGGGGTGCGAAAC-1_1 C51 HC1 HC N N 4 \n", + "AAACGGGGTTCTGAAC-1_1 C51 HC1 HC N N 8 \n", + "AAACGGGTCCAAACTG-1_1 C51 HC1 HC N N 4 \n", + "AAACGGGTCCTTTACA-1_1 C51 HC1 HC N N 21 \n", + "AAACGGGTCGCAAACT-1_1 C51 HC1 HC N N 5 \n", + "AAAGATGAGACAGAGA-1_1 C51 HC1 HC N N 3 \n", + "AAAGATGAGAGGTTAT-1_1 C51 HC1 HC N N 5 \n", + "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", + "TTTATGCGTCGACTGC-1_13 C152 S6 S Y N 19 \n", + "TTTATGCGTTAAGATG-1_13 C152 S6 S Y N 13 \n", + "TTTATGCTCGCACTCT-1_13 C152 S6 S Y Y 19 \n", + "TTTCCTCAGAGACTTA-1_13 C152 S6 S Y N 9 \n", + "TTTCCTCAGATCTGAA-1_13 C152 S6 S Y N 0 \n", + "TTTCCTCAGGATGGAA-1_13 C152 S6 S Y N 1 \n", + "TTTCCTCCAAGTCATC-1_13 C152 S6 S Y N 13 \n", + "TTTCCTCCAGCTCCGA-1_13 C152 S6 S Y N 14 \n", + "TTTCCTCCAGGTCTCG-1_13 C152 S6 S Y N 14 \n", + "TTTCCTCGTTAAGATG-1_13 C152 S6 S Y N 19 \n", + "TTTCCTCTCTTGTTTG-1_13 C152 S6 S Y N 10 \n", + "TTTGCGCAGACTACAA-1_13 C152 S6 S Y N 10 \n", + "TTTGCGCAGGGTGTGT-1_13 C152 S6 S Y N 28 \n", + "TTTGCGCGTCGGCATC-1_13 C152 S6 S Y N 19 \n", + "TTTGCGCTCCAGGGCT-1_13 C152 S6 S Y N 19 \n", + "TTTGCGCTCTAACCGA-1_13 C152 S6 S Y N 9 \n", + "TTTGCGCTCTGCTTGC-1_13 C152 S6 S Y N 14 \n", + "TTTGGTTAGATCCCGC-1_13 C152 S6 S Y N 8 \n", + "TTTGGTTAGCACCGTC-1_13 C152 S6 S Y N 1 \n", + "TTTGGTTAGTCTCCTC-1_13 C152 S6 S Y N 1 \n", + "TTTGGTTCACGAGAGT-1_13 C152 S6 S Y N 1 \n", + "TTTGGTTCACTATCTT-1_13 C152 S6 S Y N 14 \n", + "TTTGGTTCATCGGGTC-1_13 C152 S6 S Y N 1 \n", + "TTTGGTTGTCTCTCTG-1_13 C152 S6 S Y N 1 \n", + "TTTGGTTTCAAACCAC-1_13 C152 S6 S Y N 13 \n", + "TTTGGTTTCGCTTGTC-1_13 C152 S6 S Y N 1 \n", + "TTTGTCAAGAAACGCC-1_13 C152 S6 S Y N 14 \n", + "TTTGTCACAACACCCG-1_13 C152 S6 S Y N 19 \n", + "TTTGTCAGTCTGCAAT-1_13 C152 S6 S Y N 19 \n", + "TTTGTCAGTGTTGGGA-1_13 C152 S6 S Y N 10 \n", + " celltype \n", + "AAACCTGAGACACTAA-1_1 Macrophages\n", + "AAACCTGAGGAGTACC-1_1 Macrophages\n", + "AAACCTGAGGATATAC-1_1 Macrophages\n", + "AAACCTGAGGTCATCT-1_1 Macrophages\n", + "AAACCTGCACGGATAG-1_1 Macrophages\n", + "AAACCTGCAGGGAGAG-1_1 Macrophages\n", + "AAACCTGGTCTAAACC-1_1 Macrophages\n", + "AAACCTGGTGCACCAC-1_1 Macrophages\n", + "AAACCTGGTTACGCGC-1_1 Macrophages\n", + "AAACCTGTCCGCATCT-1_1 Macrophages\n", + "AAACGGGAGCTACCTA-1_1 Macrophages\n", + "AAACGGGAGGAATTAC-1_1 Macrophages\n", + "AAACGGGAGGACCACA-1_1 Macrophages\n", + "AAACGGGAGGCAAAGA-1_1 Macrophages\n", + "AAACGGGAGTCGTTTG-1_1 Macrophages\n", + "AAACGGGCAAGGACTG-1_1 Macrophages\n", + "AAACGGGCACCGAAAG-1_1 Macrophages\n", + "AAACGGGCAGCCAATT-1_1 mDC \n", + "AAACGGGCATCCGTGG-1_1 Macrophages\n", + "AAACGGGCATGCCTTC-1_1 Macrophages\n", + "AAACGGGGTACATGTC-1_1 Macrophages\n", + "AAACGGGGTCCATGAT-1_1 Macrophages\n", + "AAACGGGGTCTCTCTG-1_1 mDC \n", + "AAACGGGGTGCGAAAC-1_1 Macrophages\n", + "AAACGGGGTTCTGAAC-1_1 Macrophages\n", + "AAACGGGTCCAAACTG-1_1 Macrophages\n", + "AAACGGGTCCTTTACA-1_1 Macrophages\n", + "AAACGGGTCGCAAACT-1_1 Macrophages\n", + "AAAGATGAGACAGAGA-1_1 Macrophages\n", + "AAAGATGAGAGGTTAT-1_1 Macrophages\n", + "⋮ ⋮ \n", + "TTTATGCGTCGACTGC-1_13 Plasma \n", + "TTTATGCGTTAAGATG-1_13 Epithelial \n", + "TTTATGCTCGCACTCT-1_13 Plasma \n", + "TTTCCTCAGAGACTTA-1_13 T \n", + "TTTCCTCAGATCTGAA-1_13 Macrophages\n", + "TTTCCTCAGGATGGAA-1_13 Macrophages\n", + "TTTCCTCCAAGTCATC-1_13 Epithelial \n", + "TTTCCTCCAGCTCCGA-1_13 T \n", + "TTTCCTCCAGGTCTCG-1_13 T \n", + "TTTCCTCGTTAAGATG-1_13 Plasma \n", + "TTTCCTCTCTTGTTTG-1_13 Macrophages\n", + "TTTGCGCAGACTACAA-1_13 Macrophages\n", + "TTTGCGCAGGGTGTGT-1_13 Epithelial \n", + "TTTGCGCGTCGGCATC-1_13 Plasma \n", + "TTTGCGCTCCAGGGCT-1_13 Plasma \n", + "TTTGCGCTCTAACCGA-1_13 T \n", + "TTTGCGCTCTGCTTGC-1_13 T \n", + "TTTGGTTAGATCCCGC-1_13 Macrophages\n", + "TTTGGTTAGCACCGTC-1_13 Macrophages\n", + "TTTGGTTAGTCTCCTC-1_13 Macrophages\n", + "TTTGGTTCACGAGAGT-1_13 Macrophages\n", + "TTTGGTTCACTATCTT-1_13 T \n", + "TTTGGTTCATCGGGTC-1_13 Macrophages\n", + "TTTGGTTGTCTCTCTG-1_13 Macrophages\n", + "TTTGGTTTCAAACCAC-1_13 Epithelial \n", + "TTTGGTTTCGCTTGTC-1_13 Macrophages\n", + "TTTGTCAAGAAACGCC-1_13 T \n", + "TTTGTCACAACACCCG-1_13 Plasma \n", + "TTTGTCAGTCTGCAAT-1_13 Plasma \n", + "TTTGTCAGTGTTGGGA-1_13 Macrophages" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sample_info" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "7158e3b5-fda4-4014-a9c9-2eb5bea55884", + "metadata": {}, + "outputs": [], + "source": [ + "sample_info <- sample_info %>%\n", + " dplyr::rename(orig.ident = sample)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "77491865-97b1-4a52-98ea-7aecb468b9a0", + "metadata": {}, + "outputs": [], + "source": [ + "bad <- dplyr::left_join(nCoV.integrated@meta.data %>%\n", + " mutate(Symbol = rownames(nCoV.integrated@meta.data)),\n", + " sample_info %>%\n", + " mutate(Symbol = rownames(sample_info)),\n", + " by = 'Symbol')\n", + "to_be_deleted <- c('orig.ident.y')\n", + "bad <- bad[, !(colnames(bad) %in% to_be_deleted)]\n", + "bad <- bad %>%\n", + " dplyr::rename(orig.ident = orig.ident.x)\n", + "rownames(bad) <- bad$Symbol\n", + "bad$Symbol <- NULL" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "896e9c70-b436-4e56-9593-dab7a4a66a6f", + "metadata": {}, + "outputs": [], + "source": [ + "good <- bad %>% \n", + " dplyr::filter(!is.na(nCount_RNA))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "d360a057-0ee0-4ae6-8062-f8ea550cc3a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'data.frame':\t63734 obs. of 12 variables:\n", + " $ orig.ident : chr \"C51\" \"C51\" \"C51\" \"C51\" ...\n", + " $ nCount_RNA : num 5228 5071 5531 5630 7358 ...\n", + " $ nFeature_RNA : int 1635 1385 1729 1639 1722 1312 1307 2211 1859 1835 ...\n", + " $ percent.mito : num 1.55 1.79 1.63 1.95 2.77 ...\n", + " $ integrated_snn_res.1.2: Factor w/ 30 levels \"0\",\"1\",\"10\",\"11\",..: 13 5 13 13 27 13 25 6 6 6 ...\n", + " $ seurat_clusters : Factor w/ 30 levels \"0\",\"1\",\"2\",\"3\",..: 3 13 3 3 7 3 5 14 14 14 ...\n", + " $ sample_new : chr \"HC1\" \"HC1\" \"HC1\" \"HC1\" ...\n", + " $ group : chr \"HC\" \"HC\" \"HC\" \"HC\" ...\n", + " $ disease : chr \"N\" \"N\" \"N\" \"N\" ...\n", + " $ hasnCoV : chr \"N\" \"N\" \"N\" \"N\" ...\n", + " $ cluster : int 3 3 3 3 5 2 8 5 5 5 ...\n", + " $ celltype : chr \"Macrophages\" \"Macrophages\" \"Macrophages\" \"Macrophages\" ...\n" + ] + } + ], + "source": [ + "str(good)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "a64ffe48-2f9b-41c8-b8d3-f3abcd7118ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + 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A data.frame: 63734 × 12
orig.identnCount_RNAnFeature_RNApercent.mitointegrated_snn_res.1.2seurat_clusterssample_newgroupdiseasehasnCoVclustercelltype
<chr><dbl><int><dbl><fct><fct><chr><chr><chr><chr><int><chr>
AAACCTGAGACACTAA-1_1C51 522816351.54934972 2 HC1HCNN 3Macrophages
AAACCTGAGGAGTACC-1_1C51 507113851.79451781212HC1HCNN 3Macrophages
AAACCTGAGGATATAC-1_1C51 553117291.62719222 2 HC1HCNN 3Macrophages
AAACCTGAGGTCATCT-1_1C51 563016391.95381882 2 HC1HCNN 3Macrophages
AAACCTGCACGGATAG-1_1C51 735817222.77249256 6 HC1HCNN 5Macrophages
AAACCTGCAGGGAGAG-1_1C51 461513121.47345612 2 HC1HCNN 2Macrophages
AAACCTGGTCTAAACC-1_1C51 426013072.46478874 4 HC1HCNN 8Macrophages
AAACCTGGTGCACCAC-1_1C511011222111.50316461313HC1HCNN 5Macrophages
AAACCTGGTTACGCGC-1_1C51 674818591.80794311313HC1HCNN 5Macrophages
AAACCTGTCCGCATCT-1_1C51 671318352.21957401313HC1HCNN 5Macrophages
AAACGGGAGCTACCTA-1_1C51 414813851.47058822 2 HC1HCNN 3Macrophages
AAACGGGAGGAATTAC-1_1C51 547016732.43144421818HC1HCNN18Macrophages
AAACGGGAGGACCACA-1_1C51 834418431.61792916 6 HC1HCNN 4Macrophages
AAACGGGAGGCAAAGA-1_1C51 3075 9751.65853660 0 HC1HCNN 1Macrophages
AAACGGGAGTCGTTTG-1_1C51 568415651.84729066 6 HC1HCNN 5Macrophages
AAACGGGCAAGGACTG-1_1C51 763219941.00890992 2 HC1HCNN 3Macrophages
AAACGGGCACCGAAAG-1_1C51 486515092.01438852 2 HC1HCNN 3Macrophages
AAACGGGCAGCCAATT-1_1C51 2381 7471.38597232323HC1HCNN20mDC
AAACGGGCATCCGTGG-1_1C51 666817951.73965216 6 HC1HCNN 2Macrophages
AAACGGGCATGCCTTC-1_1C51 540916572.14457392 2 HC1HCNN 3Macrophages
AAACGGGGTACATGTC-1_1C51 689817862.05856776 6 HC1HCNN 5Macrophages
AAACGGGGTCCATGAT-1_1C51 503313912.18557524 4 HC1HCNN 8Macrophages
AAACGGGGTCTCTCTG-1_1C51 2255 8931.99556542323HC1HCNN20mDC
AAACGGGGTGCGAAAC-1_1C51 848721902.00306353 3 HC1HCNN 4Macrophages
AAACGGGGTTCTGAAC-1_1C51 2984 9422.94906174 4 HC1HCNN 8Macrophages
AAACGGGTCCAAACTG-1_1C51 455812641.90873193 3 HC1HCNN 4Macrophages
AAACGGGTCCTTTACA-1_1C51 1245 6170.48192771616HC1HCNN21Macrophages
AAACGGGTCGCAAACT-1_1C51 703116092.36097281313HC1HCNN 5Macrophages
AAAGATGAGACAGAGA-1_1C51 339211381.41509432 2 HC1HCNN 3Macrophages
AAAGATGAGAGGTTAT-1_1C51 483915122.70717096 6 HC1HCNN 5Macrophages
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
TTTATGCGTCGACTGC-1_12C152 2300 5377.3043481919NANANANANANA
TTTATGCGTTAAGATG-1_12C1522387941867.1108511515NANANANANANA
TTTATGCTCGCACTCT-1_12C152 1649 6186.5494241919NANANANANANA
TTTCCTCAGAGACTTA-1_12C152 402817693.3018871010NANANANANANA
TTTCCTCAGATCTGAA-1_12C152 1354 6606.1299850 0 NANANANANANA
TTTCCTCAGGATGGAA-1_12C152 270610625.2845530 0 NANANANANANA
TTTCCTCCAAGTCATC-1_12C152 1909 9338.3289681515NANANANANANA
TTTCCTCCAGCTCCGA-1_12C152 661224915.0514221414NANANANANANA
TTTCCTCCAGGTCTCG-1_12C152 1951 8347.1245521414NANANANANANA
TTTCCTCGTTAAGATG-1_12C152 1174 4943.6626921919NANANANANANA
TTTCCTCTCTTGTTTG-1_12C152 403414009.7917705 5 NANANANANANA
TTTGCGCAGACTACAA-1_12C1522247846015.2228855 5 NANANANANANA
TTTGCGCAGGGTGTGT-1_12C152 804028049.8756222121NANANANANANA
TTTGCGCGTCGGCATC-1_12C1522171335662.5744951919NANANANANANA
TTTGCGCTCCAGGGCT-1_12C152 1624 5109.2364531919NANANANANANA
TTTGCGCTCTAACCGA-1_12C152 478919782.2760491010NANANANANANA
TTTGCGCTCTGCTTGC-1_12C152 1567 8469.7000641414NANANANANANA
TTTGGTTAGATCCCGC-1_12C1521161226792.2390634 4 NANANANANANA
TTTGGTTAGCACCGTC-1_12C152 259111605.0173680 0 NANANANANANA
TTTGGTTAGTCTCCTC-1_12C152 1878 9235.8572950 0 NANANANANANA
TTTGGTTCACGAGAGT-1_12C152 606320973.2822040 0 NANANANANANA
TTTGGTTCACTATCTT-1_12C152 753626405.0557321414NANANANANANA
TTTGGTTCATCGGGTC-1_12C152 410414507.0906430 0 NANANANANANA
TTTGGTTGTCTCTCTG-1_12C152 391614518.4014300 0 NANANANANANA
TTTGGTTTCAAACCAC-1_12C1521931237835.9444901515NANANANANANA
TTTGGTTTCGCTTGTC-1_12C152 1116 6418.6021511 1 NANANANANANA
TTTGTCAAGAAACGCC-1_12C1521286935073.1781801414NANANANANANA
TTTGTCACAACACCCG-1_12C152 1347 5568.6859691919NANANANANANA
TTTGTCAGTCTGCAAT-1_12C1522127433734.4749461919NANANANANANA
TTTGTCAGTGTTGGGA-1_12C152 430117056.2543595 5 NANANANANANA
\n" + ], + "text/latex": [ + "A data.frame: 63734 × 12\n", + "\\begin{tabular}{r|llllllllllll}\n", + " & orig.ident & nCount\\_RNA & nFeature\\_RNA & percent.mito & integrated\\_snn\\_res.1.2 & seurat\\_clusters & sample\\_new & group & disease & hasnCoV & cluster & celltype\\\\\n", + " & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\tAAACCTGAGACACTAA-1\\_1 & C51 & 5228 & 1635 & 1.5493497 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGAGGAGTACC-1\\_1 & C51 & 5071 & 1385 & 1.7945178 & 12 & 12 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGAGGATATAC-1\\_1 & C51 & 5531 & 1729 & 1.6271922 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGAGGTCATCT-1\\_1 & C51 & 5630 & 1639 & 1.9538188 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGCACGGATAG-1\\_1 & C51 & 7358 & 1722 & 2.7724925 & 6 & 6 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACCTGCAGGGAGAG-1\\_1 & C51 & 4615 & 1312 & 1.4734561 & 2 & 2 & HC1 & HC & N & N & 2 & Macrophages\\\\\n", + "\tAAACCTGGTCTAAACC-1\\_1 & C51 & 4260 & 1307 & 2.4647887 & 4 & 4 & HC1 & HC & N & N & 8 & Macrophages\\\\\n", + "\tAAACCTGGTGCACCAC-1\\_1 & C51 & 10112 & 2211 & 1.5031646 & 13 & 13 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACCTGGTTACGCGC-1\\_1 & C51 & 6748 & 1859 & 1.8079431 & 13 & 13 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACCTGTCCGCATCT-1\\_1 & C51 & 6713 & 1835 & 2.2195740 & 13 & 13 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACGGGAGCTACCTA-1\\_1 & C51 & 4148 & 1385 & 1.4705882 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGAGGAATTAC-1\\_1 & C51 & 5470 & 1673 & 2.4314442 & 18 & 18 & HC1 & HC & N & N & 18 & Macrophages\\\\\n", + "\tAAACGGGAGGACCACA-1\\_1 & C51 & 8344 & 1843 & 1.6179291 & 6 & 6 & HC1 & HC & N & N & 4 & Macrophages\\\\\n", + "\tAAACGGGAGGCAAAGA-1\\_1 & C51 & 3075 & 975 & 1.6585366 & 0 & 0 & HC1 & HC & N & N & 1 & Macrophages\\\\\n", + "\tAAACGGGAGTCGTTTG-1\\_1 & C51 & 5684 & 1565 & 1.8472906 & 6 & 6 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACGGGCAAGGACTG-1\\_1 & C51 & 7632 & 1994 & 1.0089099 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGCACCGAAAG-1\\_1 & C51 & 4865 & 1509 & 2.0143885 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGCAGCCAATT-1\\_1 & C51 & 2381 & 747 & 1.3859723 & 23 & 23 & HC1 & HC & N & N & 20 & mDC \\\\\n", + "\tAAACGGGCATCCGTGG-1\\_1 & C51 & 6668 & 1795 & 1.7396521 & 6 & 6 & HC1 & HC & N & N & 2 & Macrophages\\\\\n", + "\tAAACGGGCATGCCTTC-1\\_1 & C51 & 5409 & 1657 & 2.1445739 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGGTACATGTC-1\\_1 & C51 & 6898 & 1786 & 2.0585677 & 6 & 6 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACGGGGTCCATGAT-1\\_1 & C51 & 5033 & 1391 & 2.1855752 & 4 & 4 & HC1 & HC & N & N & 8 & Macrophages\\\\\n", + "\tAAACGGGGTCTCTCTG-1\\_1 & C51 & 2255 & 893 & 1.9955654 & 23 & 23 & HC1 & HC & N & N & 20 & mDC \\\\\n", + "\tAAACGGGGTGCGAAAC-1\\_1 & C51 & 8487 & 2190 & 2.0030635 & 3 & 3 & HC1 & HC & N & N & 4 & Macrophages\\\\\n", + "\tAAACGGGGTTCTGAAC-1\\_1 & C51 & 2984 & 942 & 2.9490617 & 4 & 4 & HC1 & HC & N & N & 8 & Macrophages\\\\\n", + "\tAAACGGGTCCAAACTG-1\\_1 & C51 & 4558 & 1264 & 1.9087319 & 3 & 3 & HC1 & HC & N & N & 4 & Macrophages\\\\\n", + "\tAAACGGGTCCTTTACA-1\\_1 & C51 & 1245 & 617 & 0.4819277 & 16 & 16 & HC1 & HC & N & N & 21 & Macrophages\\\\\n", + "\tAAACGGGTCGCAAACT-1\\_1 & C51 & 7031 & 1609 & 2.3609728 & 13 & 13 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAAGATGAGACAGAGA-1\\_1 & C51 & 3392 & 1138 & 1.4150943 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAAGATGAGAGGTTAT-1\\_1 & C51 & 4839 & 1512 & 2.7071709 & 6 & 6 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\tTTTATGCGTCGACTGC-1\\_12 & C152 & 2300 & 537 & 7.304348 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTATGCGTTAAGATG-1\\_12 & C152 & 23879 & 4186 & 7.110851 & 15 & 15 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTATGCTCGCACTCT-1\\_12 & C152 & 1649 & 618 & 6.549424 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCAGAGACTTA-1\\_12 & C152 & 4028 & 1769 & 3.301887 & 10 & 10 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCAGATCTGAA-1\\_12 & C152 & 1354 & 660 & 6.129985 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCAGGATGGAA-1\\_12 & C152 & 2706 & 1062 & 5.284553 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCCAAGTCATC-1\\_12 & C152 & 1909 & 933 & 8.328968 & 15 & 15 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCCAGCTCCGA-1\\_12 & C152 & 6612 & 2491 & 5.051422 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCCAGGTCTCG-1\\_12 & C152 & 1951 & 834 & 7.124552 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCGTTAAGATG-1\\_12 & C152 & 1174 & 494 & 3.662692 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCTCTTGTTTG-1\\_12 & C152 & 4034 & 1400 & 9.791770 & 5 & 5 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCAGACTACAA-1\\_12 & C152 & 22478 & 4601 & 5.222885 & 5 & 5 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCAGGGTGTGT-1\\_12 & C152 & 8040 & 2804 & 9.875622 & 21 & 21 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCGTCGGCATC-1\\_12 & C152 & 21713 & 3566 & 2.574495 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCTCCAGGGCT-1\\_12 & C152 & 1624 & 510 & 9.236453 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCTCTAACCGA-1\\_12 & C152 & 4789 & 1978 & 2.276049 & 10 & 10 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCTCTGCTTGC-1\\_12 & C152 & 1567 & 846 & 9.700064 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTAGATCCCGC-1\\_12 & C152 & 11612 & 2679 & 2.239063 & 4 & 4 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTAGCACCGTC-1\\_12 & C152 & 2591 & 1160 & 5.017368 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTAGTCTCCTC-1\\_12 & C152 & 1878 & 923 & 5.857295 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTCACGAGAGT-1\\_12 & C152 & 6063 & 2097 & 3.282204 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTCACTATCTT-1\\_12 & C152 & 7536 & 2640 & 5.055732 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTCATCGGGTC-1\\_12 & C152 & 4104 & 1450 & 7.090643 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTGTCTCTCTG-1\\_12 & C152 & 3916 & 1451 & 8.401430 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTTCAAACCAC-1\\_12 & C152 & 19312 & 3783 & 5.944490 & 15 & 15 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTTCGCTTGTC-1\\_12 & C152 & 1116 & 641 & 8.602151 & 1 & 1 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGTCAAGAAACGCC-1\\_12 & C152 & 12869 & 3507 & 3.178180 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGTCACAACACCCG-1\\_12 & C152 & 1347 & 556 & 8.685969 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGTCAGTCTGCAAT-1\\_12 & C152 & 21274 & 3373 & 4.474946 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGTCAGTGTTGGGA-1\\_12 & C152 & 4301 & 1705 & 6.254359 & 5 & 5 & NA & NA & NA & NA & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 63734 × 12\n", + "\n", + "| | orig.ident <chr> | nCount_RNA <dbl> | nFeature_RNA <int> | percent.mito <dbl> | integrated_snn_res.1.2 <fct> | seurat_clusters <fct> | sample_new <chr> | group <chr> | disease <chr> | hasnCoV <chr> | cluster <int> | celltype <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| AAACCTGAGACACTAA-1_1 | C51 | 5228 | 1635 | 1.5493497 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGAGGAGTACC-1_1 | C51 | 5071 | 1385 | 1.7945178 | 12 | 12 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGAGGATATAC-1_1 | C51 | 5531 | 1729 | 1.6271922 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGAGGTCATCT-1_1 | C51 | 5630 | 1639 | 1.9538188 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGCACGGATAG-1_1 | C51 | 7358 | 1722 | 2.7724925 | 6 | 6 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACCTGCAGGGAGAG-1_1 | C51 | 4615 | 1312 | 1.4734561 | 2 | 2 | HC1 | HC | N | N | 2 | Macrophages |\n", + "| AAACCTGGTCTAAACC-1_1 | C51 | 4260 | 1307 | 2.4647887 | 4 | 4 | HC1 | HC | N | N | 8 | Macrophages |\n", + "| AAACCTGGTGCACCAC-1_1 | C51 | 10112 | 2211 | 1.5031646 | 13 | 13 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACCTGGTTACGCGC-1_1 | C51 | 6748 | 1859 | 1.8079431 | 13 | 13 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACCTGTCCGCATCT-1_1 | C51 | 6713 | 1835 | 2.2195740 | 13 | 13 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACGGGAGCTACCTA-1_1 | C51 | 4148 | 1385 | 1.4705882 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGAGGAATTAC-1_1 | C51 | 5470 | 1673 | 2.4314442 | 18 | 18 | HC1 | HC | N | N | 18 | Macrophages |\n", + "| AAACGGGAGGACCACA-1_1 | C51 | 8344 | 1843 | 1.6179291 | 6 | 6 | HC1 | HC | N | N | 4 | Macrophages |\n", + "| AAACGGGAGGCAAAGA-1_1 | C51 | 3075 | 975 | 1.6585366 | 0 | 0 | HC1 | HC | N | N | 1 | Macrophages |\n", + "| AAACGGGAGTCGTTTG-1_1 | C51 | 5684 | 1565 | 1.8472906 | 6 | 6 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACGGGCAAGGACTG-1_1 | C51 | 7632 | 1994 | 1.0089099 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGCACCGAAAG-1_1 | C51 | 4865 | 1509 | 2.0143885 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGCAGCCAATT-1_1 | C51 | 2381 | 747 | 1.3859723 | 23 | 23 | HC1 | HC | N | N | 20 | mDC |\n", + "| AAACGGGCATCCGTGG-1_1 | C51 | 6668 | 1795 | 1.7396521 | 6 | 6 | HC1 | HC | N | N | 2 | Macrophages |\n", + "| AAACGGGCATGCCTTC-1_1 | C51 | 5409 | 1657 | 2.1445739 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGGTACATGTC-1_1 | C51 | 6898 | 1786 | 2.0585677 | 6 | 6 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACGGGGTCCATGAT-1_1 | C51 | 5033 | 1391 | 2.1855752 | 4 | 4 | HC1 | HC | N | N | 8 | Macrophages |\n", + "| AAACGGGGTCTCTCTG-1_1 | C51 | 2255 | 893 | 1.9955654 | 23 | 23 | HC1 | HC | N | N | 20 | mDC |\n", + "| AAACGGGGTGCGAAAC-1_1 | C51 | 8487 | 2190 | 2.0030635 | 3 | 3 | HC1 | HC | N | N | 4 | Macrophages |\n", + "| AAACGGGGTTCTGAAC-1_1 | C51 | 2984 | 942 | 2.9490617 | 4 | 4 | HC1 | HC | N | N | 8 | Macrophages |\n", + "| AAACGGGTCCAAACTG-1_1 | C51 | 4558 | 1264 | 1.9087319 | 3 | 3 | HC1 | HC | N | N | 4 | Macrophages |\n", + "| AAACGGGTCCTTTACA-1_1 | C51 | 1245 | 617 | 0.4819277 | 16 | 16 | HC1 | HC | N | N | 21 | Macrophages |\n", + "| AAACGGGTCGCAAACT-1_1 | C51 | 7031 | 1609 | 2.3609728 | 13 | 13 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAAGATGAGACAGAGA-1_1 | C51 | 3392 | 1138 | 1.4150943 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAAGATGAGAGGTTAT-1_1 | C51 | 4839 | 1512 | 2.7071709 | 6 | 6 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", + "| TTTATGCGTCGACTGC-1_12 | C152 | 2300 | 537 | 7.304348 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTATGCGTTAAGATG-1_12 | C152 | 23879 | 4186 | 7.110851 | 15 | 15 | NA | NA | NA | NA | NA | NA |\n", + "| TTTATGCTCGCACTCT-1_12 | C152 | 1649 | 618 | 6.549424 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCAGAGACTTA-1_12 | C152 | 4028 | 1769 | 3.301887 | 10 | 10 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCAGATCTGAA-1_12 | C152 | 1354 | 660 | 6.129985 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCAGGATGGAA-1_12 | C152 | 2706 | 1062 | 5.284553 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCCAAGTCATC-1_12 | C152 | 1909 | 933 | 8.328968 | 15 | 15 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCCAGCTCCGA-1_12 | C152 | 6612 | 2491 | 5.051422 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCCAGGTCTCG-1_12 | C152 | 1951 | 834 | 7.124552 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCGTTAAGATG-1_12 | C152 | 1174 | 494 | 3.662692 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCTCTTGTTTG-1_12 | C152 | 4034 | 1400 | 9.791770 | 5 | 5 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCAGACTACAA-1_12 | C152 | 22478 | 4601 | 5.222885 | 5 | 5 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCAGGGTGTGT-1_12 | C152 | 8040 | 2804 | 9.875622 | 21 | 21 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCGTCGGCATC-1_12 | C152 | 21713 | 3566 | 2.574495 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCTCCAGGGCT-1_12 | C152 | 1624 | 510 | 9.236453 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCTCTAACCGA-1_12 | C152 | 4789 | 1978 | 2.276049 | 10 | 10 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCTCTGCTTGC-1_12 | C152 | 1567 | 846 | 9.700064 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTAGATCCCGC-1_12 | C152 | 11612 | 2679 | 2.239063 | 4 | 4 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTAGCACCGTC-1_12 | C152 | 2591 | 1160 | 5.017368 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTAGTCTCCTC-1_12 | C152 | 1878 | 923 | 5.857295 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTCACGAGAGT-1_12 | C152 | 6063 | 2097 | 3.282204 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTCACTATCTT-1_12 | C152 | 7536 | 2640 | 5.055732 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTCATCGGGTC-1_12 | C152 | 4104 | 1450 | 7.090643 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTGTCTCTCTG-1_12 | C152 | 3916 | 1451 | 8.401430 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTTCAAACCAC-1_12 | C152 | 19312 | 3783 | 5.944490 | 15 | 15 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTTCGCTTGTC-1_12 | C152 | 1116 | 641 | 8.602151 | 1 | 1 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGTCAAGAAACGCC-1_12 | C152 | 12869 | 3507 | 3.178180 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGTCACAACACCCG-1_12 | C152 | 1347 | 556 | 8.685969 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGTCAGTCTGCAAT-1_12 | C152 | 21274 | 3373 | 4.474946 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGTCAGTGTTGGGA-1_12 | C152 | 4301 | 1705 | 6.254359 | 5 | 5 | NA | NA | NA | NA | NA | NA |\n", + "\n" + ], + "text/plain": [ + " orig.ident nCount_RNA nFeature_RNA percent.mito\n", + "AAACCTGAGACACTAA-1_1 C51 5228 1635 1.5493497 \n", + "AAACCTGAGGAGTACC-1_1 C51 5071 1385 1.7945178 \n", + "AAACCTGAGGATATAC-1_1 C51 5531 1729 1.6271922 \n", + "AAACCTGAGGTCATCT-1_1 C51 5630 1639 1.9538188 \n", + "AAACCTGCACGGATAG-1_1 C51 7358 1722 2.7724925 \n", + "AAACCTGCAGGGAGAG-1_1 C51 4615 1312 1.4734561 \n", + "AAACCTGGTCTAAACC-1_1 C51 4260 1307 2.4647887 \n", + "AAACCTGGTGCACCAC-1_1 C51 10112 2211 1.5031646 \n", + "AAACCTGGTTACGCGC-1_1 C51 6748 1859 1.8079431 \n", + "AAACCTGTCCGCATCT-1_1 C51 6713 1835 2.2195740 \n", + "AAACGGGAGCTACCTA-1_1 C51 4148 1385 1.4705882 \n", + "AAACGGGAGGAATTAC-1_1 C51 5470 1673 2.4314442 \n", + "AAACGGGAGGACCACA-1_1 C51 8344 1843 1.6179291 \n", + "AAACGGGAGGCAAAGA-1_1 C51 3075 975 1.6585366 \n", + "AAACGGGAGTCGTTTG-1_1 C51 5684 1565 1.8472906 \n", + "AAACGGGCAAGGACTG-1_1 C51 7632 1994 1.0089099 \n", + "AAACGGGCACCGAAAG-1_1 C51 4865 1509 2.0143885 \n", + "AAACGGGCAGCCAATT-1_1 C51 2381 747 1.3859723 \n", + "AAACGGGCATCCGTGG-1_1 C51 6668 1795 1.7396521 \n", + "AAACGGGCATGCCTTC-1_1 C51 5409 1657 2.1445739 \n", + "AAACGGGGTACATGTC-1_1 C51 6898 1786 2.0585677 \n", + "AAACGGGGTCCATGAT-1_1 C51 5033 1391 2.1855752 \n", + "AAACGGGGTCTCTCTG-1_1 C51 2255 893 1.9955654 \n", + "AAACGGGGTGCGAAAC-1_1 C51 8487 2190 2.0030635 \n", + "AAACGGGGTTCTGAAC-1_1 C51 2984 942 2.9490617 \n", + "AAACGGGTCCAAACTG-1_1 C51 4558 1264 1.9087319 \n", + "AAACGGGTCCTTTACA-1_1 C51 1245 617 0.4819277 \n", + "AAACGGGTCGCAAACT-1_1 C51 7031 1609 2.3609728 \n", + "AAAGATGAGACAGAGA-1_1 C51 3392 1138 1.4150943 \n", + "AAAGATGAGAGGTTAT-1_1 C51 4839 1512 2.7071709 \n", + "⋮ ⋮ ⋮ ⋮ ⋮ \n", + "TTTATGCGTCGACTGC-1_12 C152 2300 537 7.304348 \n", + "TTTATGCGTTAAGATG-1_12 C152 23879 4186 7.110851 \n", + "TTTATGCTCGCACTCT-1_12 C152 1649 618 6.549424 \n", + "TTTCCTCAGAGACTTA-1_12 C152 4028 1769 3.301887 \n", + "TTTCCTCAGATCTGAA-1_12 C152 1354 660 6.129985 \n", + "TTTCCTCAGGATGGAA-1_12 C152 2706 1062 5.284553 \n", + "TTTCCTCCAAGTCATC-1_12 C152 1909 933 8.328968 \n", + "TTTCCTCCAGCTCCGA-1_12 C152 6612 2491 5.051422 \n", + "TTTCCTCCAGGTCTCG-1_12 C152 1951 834 7.124552 \n", + "TTTCCTCGTTAAGATG-1_12 C152 1174 494 3.662692 \n", + "TTTCCTCTCTTGTTTG-1_12 C152 4034 1400 9.791770 \n", + "TTTGCGCAGACTACAA-1_12 C152 22478 4601 5.222885 \n", + "TTTGCGCAGGGTGTGT-1_12 C152 8040 2804 9.875622 \n", + "TTTGCGCGTCGGCATC-1_12 C152 21713 3566 2.574495 \n", + "TTTGCGCTCCAGGGCT-1_12 C152 1624 510 9.236453 \n", + "TTTGCGCTCTAACCGA-1_12 C152 4789 1978 2.276049 \n", + "TTTGCGCTCTGCTTGC-1_12 C152 1567 846 9.700064 \n", + "TTTGGTTAGATCCCGC-1_12 C152 11612 2679 2.239063 \n", + "TTTGGTTAGCACCGTC-1_12 C152 2591 1160 5.017368 \n", + "TTTGGTTAGTCTCCTC-1_12 C152 1878 923 5.857295 \n", + "TTTGGTTCACGAGAGT-1_12 C152 6063 2097 3.282204 \n", + "TTTGGTTCACTATCTT-1_12 C152 7536 2640 5.055732 \n", + "TTTGGTTCATCGGGTC-1_12 C152 4104 1450 7.090643 \n", + "TTTGGTTGTCTCTCTG-1_12 C152 3916 1451 8.401430 \n", + "TTTGGTTTCAAACCAC-1_12 C152 19312 3783 5.944490 \n", + "TTTGGTTTCGCTTGTC-1_12 C152 1116 641 8.602151 \n", + "TTTGTCAAGAAACGCC-1_12 C152 12869 3507 3.178180 \n", + "TTTGTCACAACACCCG-1_12 C152 1347 556 8.685969 \n", + "TTTGTCAGTCTGCAAT-1_12 C152 21274 3373 4.474946 \n", + "TTTGTCAGTGTTGGGA-1_12 C152 4301 1705 6.254359 \n", + " integrated_snn_res.1.2 seurat_clusters sample_new group\n", + "AAACCTGAGACACTAA-1_1 2 2 HC1 HC \n", + "AAACCTGAGGAGTACC-1_1 12 12 HC1 HC \n", + "AAACCTGAGGATATAC-1_1 2 2 HC1 HC \n", + "AAACCTGAGGTCATCT-1_1 2 2 HC1 HC \n", + "AAACCTGCACGGATAG-1_1 6 6 HC1 HC \n", + "AAACCTGCAGGGAGAG-1_1 2 2 HC1 HC \n", + "AAACCTGGTCTAAACC-1_1 4 4 HC1 HC \n", + "AAACCTGGTGCACCAC-1_1 13 13 HC1 HC \n", + "AAACCTGGTTACGCGC-1_1 13 13 HC1 HC \n", + "AAACCTGTCCGCATCT-1_1 13 13 HC1 HC \n", + "AAACGGGAGCTACCTA-1_1 2 2 HC1 HC \n", + "AAACGGGAGGAATTAC-1_1 18 18 HC1 HC \n", + "AAACGGGAGGACCACA-1_1 6 6 HC1 HC \n", + "AAACGGGAGGCAAAGA-1_1 0 0 HC1 HC \n", + "AAACGGGAGTCGTTTG-1_1 6 6 HC1 HC \n", + "AAACGGGCAAGGACTG-1_1 2 2 HC1 HC \n", + "AAACGGGCACCGAAAG-1_1 2 2 HC1 HC \n", + "AAACGGGCAGCCAATT-1_1 23 23 HC1 HC \n", + "AAACGGGCATCCGTGG-1_1 6 6 HC1 HC \n", + "AAACGGGCATGCCTTC-1_1 2 2 HC1 HC \n", + "AAACGGGGTACATGTC-1_1 6 6 HC1 HC \n", + "AAACGGGGTCCATGAT-1_1 4 4 HC1 HC \n", + "AAACGGGGTCTCTCTG-1_1 23 23 HC1 HC \n", + "AAACGGGGTGCGAAAC-1_1 3 3 HC1 HC \n", + "AAACGGGGTTCTGAAC-1_1 4 4 HC1 HC \n", + "AAACGGGTCCAAACTG-1_1 3 3 HC1 HC \n", + "AAACGGGTCCTTTACA-1_1 16 16 HC1 HC \n", + "AAACGGGTCGCAAACT-1_1 13 13 HC1 HC \n", + "AAAGATGAGACAGAGA-1_1 2 2 HC1 HC \n", + "AAAGATGAGAGGTTAT-1_1 6 6 HC1 HC \n", + "⋮ ⋮ ⋮ ⋮ ⋮ \n", + "TTTATGCGTCGACTGC-1_12 19 19 NA NA \n", + "TTTATGCGTTAAGATG-1_12 15 15 NA NA \n", + "TTTATGCTCGCACTCT-1_12 19 19 NA NA \n", + "TTTCCTCAGAGACTTA-1_12 10 10 NA NA \n", + "TTTCCTCAGATCTGAA-1_12 0 0 NA NA \n", + "TTTCCTCAGGATGGAA-1_12 0 0 NA NA \n", + "TTTCCTCCAAGTCATC-1_12 15 15 NA NA \n", + "TTTCCTCCAGCTCCGA-1_12 14 14 NA NA \n", + "TTTCCTCCAGGTCTCG-1_12 14 14 NA NA \n", + "TTTCCTCGTTAAGATG-1_12 19 19 NA NA \n", + "TTTCCTCTCTTGTTTG-1_12 5 5 NA NA \n", + "TTTGCGCAGACTACAA-1_12 5 5 NA NA \n", + "TTTGCGCAGGGTGTGT-1_12 21 21 NA NA \n", + "TTTGCGCGTCGGCATC-1_12 19 19 NA NA \n", + "TTTGCGCTCCAGGGCT-1_12 19 19 NA NA \n", + "TTTGCGCTCTAACCGA-1_12 10 10 NA NA \n", + "TTTGCGCTCTGCTTGC-1_12 14 14 NA NA \n", + "TTTGGTTAGATCCCGC-1_12 4 4 NA NA \n", + "TTTGGTTAGCACCGTC-1_12 0 0 NA NA \n", + "TTTGGTTAGTCTCCTC-1_12 0 0 NA NA \n", + "TTTGGTTCACGAGAGT-1_12 0 0 NA NA \n", + "TTTGGTTCACTATCTT-1_12 14 14 NA NA \n", + "TTTGGTTCATCGGGTC-1_12 0 0 NA NA \n", + "TTTGGTTGTCTCTCTG-1_12 0 0 NA NA \n", + "TTTGGTTTCAAACCAC-1_12 15 15 NA NA \n", + "TTTGGTTTCGCTTGTC-1_12 1 1 NA NA \n", + "TTTGTCAAGAAACGCC-1_12 14 14 NA NA \n", + "TTTGTCACAACACCCG-1_12 19 19 NA NA \n", + "TTTGTCAGTCTGCAAT-1_12 19 19 NA NA \n", + "TTTGTCAGTGTTGGGA-1_12 5 5 NA NA \n", + " disease hasnCoV cluster celltype \n", + "AAACCTGAGACACTAA-1_1 N N 3 Macrophages\n", + "AAACCTGAGGAGTACC-1_1 N N 3 Macrophages\n", + "AAACCTGAGGATATAC-1_1 N N 3 Macrophages\n", + "AAACCTGAGGTCATCT-1_1 N N 3 Macrophages\n", + "AAACCTGCACGGATAG-1_1 N N 5 Macrophages\n", + "AAACCTGCAGGGAGAG-1_1 N N 2 Macrophages\n", + "AAACCTGGTCTAAACC-1_1 N N 8 Macrophages\n", + "AAACCTGGTGCACCAC-1_1 N N 5 Macrophages\n", + "AAACCTGGTTACGCGC-1_1 N N 5 Macrophages\n", + "AAACCTGTCCGCATCT-1_1 N N 5 Macrophages\n", + "AAACGGGAGCTACCTA-1_1 N N 3 Macrophages\n", + "AAACGGGAGGAATTAC-1_1 N N 18 Macrophages\n", + "AAACGGGAGGACCACA-1_1 N N 4 Macrophages\n", + "AAACGGGAGGCAAAGA-1_1 N N 1 Macrophages\n", + "AAACGGGAGTCGTTTG-1_1 N N 5 Macrophages\n", + "AAACGGGCAAGGACTG-1_1 N N 3 Macrophages\n", + "AAACGGGCACCGAAAG-1_1 N N 3 Macrophages\n", + "AAACGGGCAGCCAATT-1_1 N N 20 mDC \n", + "AAACGGGCATCCGTGG-1_1 N N 2 Macrophages\n", + "AAACGGGCATGCCTTC-1_1 N N 3 Macrophages\n", + "AAACGGGGTACATGTC-1_1 N N 5 Macrophages\n", + "AAACGGGGTCCATGAT-1_1 N N 8 Macrophages\n", + "AAACGGGGTCTCTCTG-1_1 N N 20 mDC \n", + "AAACGGGGTGCGAAAC-1_1 N N 4 Macrophages\n", + "AAACGGGGTTCTGAAC-1_1 N N 8 Macrophages\n", + "AAACGGGTCCAAACTG-1_1 N N 4 Macrophages\n", + "AAACGGGTCCTTTACA-1_1 N N 21 Macrophages\n", + "AAACGGGTCGCAAACT-1_1 N N 5 Macrophages\n", + "AAAGATGAGACAGAGA-1_1 N N 3 Macrophages\n", + "AAAGATGAGAGGTTAT-1_1 N N 5 Macrophages\n", + "⋮ ⋮ ⋮ ⋮ ⋮ \n", + "TTTATGCGTCGACTGC-1_12 NA NA NA NA \n", + "TTTATGCGTTAAGATG-1_12 NA NA NA NA \n", + "TTTATGCTCGCACTCT-1_12 NA NA NA NA \n", + "TTTCCTCAGAGACTTA-1_12 NA NA NA NA \n", + "TTTCCTCAGATCTGAA-1_12 NA NA NA NA \n", + "TTTCCTCAGGATGGAA-1_12 NA NA NA NA \n", + "TTTCCTCCAAGTCATC-1_12 NA NA NA NA \n", + "TTTCCTCCAGCTCCGA-1_12 NA NA NA NA \n", + "TTTCCTCCAGGTCTCG-1_12 NA NA NA NA \n", + "TTTCCTCGTTAAGATG-1_12 NA NA NA NA \n", + "TTTCCTCTCTTGTTTG-1_12 NA NA NA NA \n", + "TTTGCGCAGACTACAA-1_12 NA NA NA NA \n", + "TTTGCGCAGGGTGTGT-1_12 NA NA NA NA \n", + "TTTGCGCGTCGGCATC-1_12 NA NA NA NA \n", + "TTTGCGCTCCAGGGCT-1_12 NA NA NA NA \n", + "TTTGCGCTCTAACCGA-1_12 NA NA NA NA \n", + "TTTGCGCTCTGCTTGC-1_12 NA NA NA NA \n", + "TTTGGTTAGATCCCGC-1_12 NA NA NA NA \n", + "TTTGGTTAGCACCGTC-1_12 NA NA NA NA \n", + "TTTGGTTAGTCTCCTC-1_12 NA NA NA NA \n", + "TTTGGTTCACGAGAGT-1_12 NA NA NA NA \n", + "TTTGGTTCACTATCTT-1_12 NA NA NA NA \n", + "TTTGGTTCATCGGGTC-1_12 NA NA NA NA \n", + "TTTGGTTGTCTCTCTG-1_12 NA NA NA NA \n", + "TTTGGTTTCAAACCAC-1_12 NA NA NA NA \n", + "TTTGGTTTCGCTTGTC-1_12 NA NA NA NA \n", + "TTTGTCAAGAAACGCC-1_12 NA NA NA NA \n", + "TTTGTCACAACACCCG-1_12 NA NA NA NA \n", + "TTTGTCAGTCTGCAAT-1_12 NA NA NA NA \n", + "TTTGTCAGTGTTGGGA-1_12 NA NA NA NA " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "good" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "bf159d07-9e91-45c3-90f0-cb5e81c6dbf7", + "metadata": {}, + "outputs": [], + "source": [ + "my_data <- read.table('covid_balf/meta.txt', \n", + " sep =\"\\t\", header = TRUE)\n", + "sample_identity <- as.data.frame(my_data)\n", + "sample_identity <- sample_identity %>%\n", + " rowwise() %>%\n", + " mutate(new = paste(sample, group, sep = '_')) %>%\n", + " dplyr::filter(new != 'GSM3660650_HC')" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c9069706-8476-4037-8746-9e028cb9bd9b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A rowwise_df: 12 × 11
samplesample_newsample_new_oldgroupdiseasenCoV_meannFeature_RNA_lownFeature_RNA_highnCount_RNA_thresholdpercent.mitonew
<chr><chr><chr><chr><chr><dbl><int><int><int><int><chr>
C51 HC1HC1HCN0.0000000002006000100010C51_HC
C52 HC2HC2HCN0.0000000002006000100010C52_HC
C100HC3HC3HCN0.0000000002006000100010C100_HC
C141M1 O1 M Y0.0000000002006000100010C141_M
C142M2 O2 M Y0.0000000002006000100010C142_M
C144M3 O3 M Y0.0000000002006000100010C144_M
C145S1 S1 S Y0.0859011312006000100010C145_S
C143S2 C1 S Y0.0070000482006000100010C143_S
C146S3 C2 S Y0.7499391882006000100010C146_S
C148S4 C3 S Y0.0025510202006000100010C148_S
C149S5 C4 S Y0.4088225082006000100010C149_S
C152S6 C5 S Y0.0964821522006000100010C152_S
\n" + ], + "text/latex": [ + "A rowwise\\_df: 12 × 11\n", + "\\begin{tabular}{lllllllllll}\n", + " sample & sample\\_new & sample\\_new\\_old & group & disease & nCoV\\_mean & nFeature\\_RNA\\_low & nFeature\\_RNA\\_high & nCount\\_RNA\\_threshold & percent.mito & new\\\\\n", + " & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t C51 & HC1 & HC1 & HC & N & 0.000000000 & 200 & 6000 & 1000 & 10 & C51\\_HC \\\\\n", + "\t C52 & HC2 & HC2 & HC & N & 0.000000000 & 200 & 6000 & 1000 & 10 & C52\\_HC \\\\\n", + "\t C100 & HC3 & HC3 & HC & N & 0.000000000 & 200 & 6000 & 1000 & 10 & C100\\_HC\\\\\n", + "\t C141 & M1 & O1 & M & Y & 0.000000000 & 200 & 6000 & 1000 & 10 & C141\\_M \\\\\n", + "\t C142 & M2 & O2 & M & Y & 0.000000000 & 200 & 6000 & 1000 & 10 & C142\\_M \\\\\n", + "\t C144 & M3 & O3 & M & Y & 0.000000000 & 200 & 6000 & 1000 & 10 & C144\\_M \\\\\n", + "\t C145 & S1 & S1 & S & Y & 0.085901131 & 200 & 6000 & 1000 & 10 & C145\\_S \\\\\n", + "\t C143 & S2 & C1 & S & Y & 0.007000048 & 200 & 6000 & 1000 & 10 & C143\\_S \\\\\n", + "\t C146 & S3 & C2 & S & Y & 0.749939188 & 200 & 6000 & 1000 & 10 & C146\\_S \\\\\n", + "\t C148 & S4 & C3 & S & Y & 0.002551020 & 200 & 6000 & 1000 & 10 & C148\\_S \\\\\n", + "\t C149 & S5 & C4 & S & Y & 0.408822508 & 200 & 6000 & 1000 & 10 & C149\\_S \\\\\n", + "\t C152 & S6 & C5 & S & Y & 0.096482152 & 200 & 6000 & 1000 & 10 & C152\\_S \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A rowwise_df: 12 × 11\n", + "\n", + "| sample <chr> | sample_new <chr> | sample_new_old <chr> | group <chr> | disease <chr> | nCoV_mean <dbl> | nFeature_RNA_low <int> | nFeature_RNA_high <int> | nCount_RNA_threshold <int> | percent.mito <int> | new <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|\n", + "| C51 | HC1 | HC1 | HC | N | 0.000000000 | 200 | 6000 | 1000 | 10 | C51_HC |\n", + "| C52 | HC2 | HC2 | HC | N | 0.000000000 | 200 | 6000 | 1000 | 10 | C52_HC |\n", + "| C100 | HC3 | HC3 | HC | N | 0.000000000 | 200 | 6000 | 1000 | 10 | C100_HC |\n", + "| C141 | M1 | O1 | M | Y | 0.000000000 | 200 | 6000 | 1000 | 10 | C141_M |\n", + "| C142 | M2 | O2 | M | Y | 0.000000000 | 200 | 6000 | 1000 | 10 | C142_M |\n", + "| C144 | M3 | O3 | M | Y | 0.000000000 | 200 | 6000 | 1000 | 10 | C144_M |\n", + "| C145 | S1 | S1 | S | Y | 0.085901131 | 200 | 6000 | 1000 | 10 | C145_S |\n", + "| C143 | S2 | C1 | S | Y | 0.007000048 | 200 | 6000 | 1000 | 10 | C143_S |\n", + "| C146 | S3 | C2 | S | Y | 0.749939188 | 200 | 6000 | 1000 | 10 | C146_S |\n", + "| C148 | S4 | C3 | S | Y | 0.002551020 | 200 | 6000 | 1000 | 10 | C148_S |\n", + "| C149 | S5 | C4 | S | Y | 0.408822508 | 200 | 6000 | 1000 | 10 | C149_S |\n", + "| C152 | S6 | C5 | S | Y | 0.096482152 | 200 | 6000 | 1000 | 10 | C152_S |\n", + "\n" + ], + "text/plain": [ + " sample sample_new sample_new_old group disease nCoV_mean nFeature_RNA_low\n", + "1 C51 HC1 HC1 HC N 0.000000000 200 \n", + "2 C52 HC2 HC2 HC N 0.000000000 200 \n", + "3 C100 HC3 HC3 HC N 0.000000000 200 \n", + "4 C141 M1 O1 M Y 0.000000000 200 \n", + "5 C142 M2 O2 M Y 0.000000000 200 \n", + "6 C144 M3 O3 M Y 0.000000000 200 \n", + "7 C145 S1 S1 S Y 0.085901131 200 \n", + "8 C143 S2 C1 S Y 0.007000048 200 \n", + "9 C146 S3 C2 S Y 0.749939188 200 \n", + "10 C148 S4 C3 S Y 0.002551020 200 \n", + "11 C149 S5 C4 S Y 0.408822508 200 \n", + "12 C152 S6 C5 S Y 0.096482152 200 \n", + " nFeature_RNA_high nCount_RNA_threshold percent.mito new \n", + "1 6000 1000 10 C51_HC \n", + "2 6000 1000 10 C52_HC \n", + "3 6000 1000 10 C100_HC\n", + "4 6000 1000 10 C141_M \n", + "5 6000 1000 10 C142_M \n", + "6 6000 1000 10 C144_M \n", + "7 6000 1000 10 C145_S \n", + "8 6000 1000 10 C143_S \n", + "9 6000 1000 10 C146_S \n", + "10 6000 1000 10 C148_S \n", + "11 6000 1000 10 C149_S \n", + "12 6000 1000 10 C152_S " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sample_identity\n", + "sample <- c('C51', 'C52', 'C100', 'C141', 'C142', 'C144', 'C145', 'C143', 'C146', 'C148', 'C149', 'C152')\n", + "sample_new <- sample_identity$sample_new\n", + "group <- sample_identity$group \n", + "disease <- sample_identity$group \n", + "df <- data.frame(sample, sample_new, group, disease)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "beabd9bd-33b9-4a7e-a379-e4ecdf6206f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + 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A data.frame: 63734 × 12
orig.identnCount_RNAnFeature_RNApercent.mitointegrated_snn_res.1.2seurat_clusterssample_newgroupdiseasehasnCoVclustercelltype
<chr><dbl><int><dbl><fct><fct><chr><chr><chr><chr><int><chr>
AAACCTGAGACACTAA-1_1C51 522816351.54934972 2 HC1HCNN 3Macrophages
AAACCTGAGGAGTACC-1_1C51 507113851.79451781212HC1HCNN 3Macrophages
AAACCTGAGGATATAC-1_1C51 553117291.62719222 2 HC1HCNN 3Macrophages
AAACCTGAGGTCATCT-1_1C51 563016391.95381882 2 HC1HCNN 3Macrophages
AAACCTGCACGGATAG-1_1C51 735817222.77249256 6 HC1HCNN 5Macrophages
AAACCTGCAGGGAGAG-1_1C51 461513121.47345612 2 HC1HCNN 2Macrophages
AAACCTGGTCTAAACC-1_1C51 426013072.46478874 4 HC1HCNN 8Macrophages
AAACCTGGTGCACCAC-1_1C511011222111.50316461313HC1HCNN 5Macrophages
AAACCTGGTTACGCGC-1_1C51 674818591.80794311313HC1HCNN 5Macrophages
AAACCTGTCCGCATCT-1_1C51 671318352.21957401313HC1HCNN 5Macrophages
AAACGGGAGCTACCTA-1_1C51 414813851.47058822 2 HC1HCNN 3Macrophages
AAACGGGAGGAATTAC-1_1C51 547016732.43144421818HC1HCNN18Macrophages
AAACGGGAGGACCACA-1_1C51 834418431.61792916 6 HC1HCNN 4Macrophages
AAACGGGAGGCAAAGA-1_1C51 3075 9751.65853660 0 HC1HCNN 1Macrophages
AAACGGGAGTCGTTTG-1_1C51 568415651.84729066 6 HC1HCNN 5Macrophages
AAACGGGCAAGGACTG-1_1C51 763219941.00890992 2 HC1HCNN 3Macrophages
AAACGGGCACCGAAAG-1_1C51 486515092.01438852 2 HC1HCNN 3Macrophages
AAACGGGCAGCCAATT-1_1C51 2381 7471.38597232323HC1HCNN20mDC
AAACGGGCATCCGTGG-1_1C51 666817951.73965216 6 HC1HCNN 2Macrophages
AAACGGGCATGCCTTC-1_1C51 540916572.14457392 2 HC1HCNN 3Macrophages
AAACGGGGTACATGTC-1_1C51 689817862.05856776 6 HC1HCNN 5Macrophages
AAACGGGGTCCATGAT-1_1C51 503313912.18557524 4 HC1HCNN 8Macrophages
AAACGGGGTCTCTCTG-1_1C51 2255 8931.99556542323HC1HCNN20mDC
AAACGGGGTGCGAAAC-1_1C51 848721902.00306353 3 HC1HCNN 4Macrophages
AAACGGGGTTCTGAAC-1_1C51 2984 9422.94906174 4 HC1HCNN 8Macrophages
AAACGGGTCCAAACTG-1_1C51 455812641.90873193 3 HC1HCNN 4Macrophages
AAACGGGTCCTTTACA-1_1C51 1245 6170.48192771616HC1HCNN21Macrophages
AAACGGGTCGCAAACT-1_1C51 703116092.36097281313HC1HCNN 5Macrophages
AAAGATGAGACAGAGA-1_1C51 339211381.41509432 2 HC1HCNN 3Macrophages
AAAGATGAGAGGTTAT-1_1C51 483915122.70717096 6 HC1HCNN 5Macrophages
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
TTTATGCGTCGACTGC-1_12C152 2300 5377.3043481919NANANANANANA
TTTATGCGTTAAGATG-1_12C1522387941867.1108511515NANANANANANA
TTTATGCTCGCACTCT-1_12C152 1649 6186.5494241919NANANANANANA
TTTCCTCAGAGACTTA-1_12C152 402817693.3018871010NANANANANANA
TTTCCTCAGATCTGAA-1_12C152 1354 6606.1299850 0 NANANANANANA
TTTCCTCAGGATGGAA-1_12C152 270610625.2845530 0 NANANANANANA
TTTCCTCCAAGTCATC-1_12C152 1909 9338.3289681515NANANANANANA
TTTCCTCCAGCTCCGA-1_12C152 661224915.0514221414NANANANANANA
TTTCCTCCAGGTCTCG-1_12C152 1951 8347.1245521414NANANANANANA
TTTCCTCGTTAAGATG-1_12C152 1174 4943.6626921919NANANANANANA
TTTCCTCTCTTGTTTG-1_12C152 403414009.7917705 5 NANANANANANA
TTTGCGCAGACTACAA-1_12C1522247846015.2228855 5 NANANANANANA
TTTGCGCAGGGTGTGT-1_12C152 804028049.8756222121NANANANANANA
TTTGCGCGTCGGCATC-1_12C1522171335662.5744951919NANANANANANA
TTTGCGCTCCAGGGCT-1_12C152 1624 5109.2364531919NANANANANANA
TTTGCGCTCTAACCGA-1_12C152 478919782.2760491010NANANANANANA
TTTGCGCTCTGCTTGC-1_12C152 1567 8469.7000641414NANANANANANA
TTTGGTTAGATCCCGC-1_12C1521161226792.2390634 4 NANANANANANA
TTTGGTTAGCACCGTC-1_12C152 259111605.0173680 0 NANANANANANA
TTTGGTTAGTCTCCTC-1_12C152 1878 9235.8572950 0 NANANANANANA
TTTGGTTCACGAGAGT-1_12C152 606320973.2822040 0 NANANANANANA
TTTGGTTCACTATCTT-1_12C152 753626405.0557321414NANANANANANA
TTTGGTTCATCGGGTC-1_12C152 410414507.0906430 0 NANANANANANA
TTTGGTTGTCTCTCTG-1_12C152 391614518.4014300 0 NANANANANANA
TTTGGTTTCAAACCAC-1_12C1521931237835.9444901515NANANANANANA
TTTGGTTTCGCTTGTC-1_12C152 1116 6418.6021511 1 NANANANANANA
TTTGTCAAGAAACGCC-1_12C1521286935073.1781801414NANANANANANA
TTTGTCACAACACCCG-1_12C152 1347 5568.6859691919NANANANANANA
TTTGTCAGTCTGCAAT-1_12C1522127433734.4749461919NANANANANANA
TTTGTCAGTGTTGGGA-1_12C152 430117056.2543595 5 NANANANANANA
\n" + ], + "text/latex": [ + "A data.frame: 63734 × 12\n", + "\\begin{tabular}{r|llllllllllll}\n", + " & orig.ident & nCount\\_RNA & nFeature\\_RNA & percent.mito & integrated\\_snn\\_res.1.2 & seurat\\_clusters & sample\\_new & group & disease & hasnCoV & cluster & celltype\\\\\n", + " & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\tAAACCTGAGACACTAA-1\\_1 & C51 & 5228 & 1635 & 1.5493497 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGAGGAGTACC-1\\_1 & C51 & 5071 & 1385 & 1.7945178 & 12 & 12 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGAGGATATAC-1\\_1 & C51 & 5531 & 1729 & 1.6271922 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGAGGTCATCT-1\\_1 & C51 & 5630 & 1639 & 1.9538188 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACCTGCACGGATAG-1\\_1 & C51 & 7358 & 1722 & 2.7724925 & 6 & 6 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACCTGCAGGGAGAG-1\\_1 & C51 & 4615 & 1312 & 1.4734561 & 2 & 2 & HC1 & HC & N & N & 2 & Macrophages\\\\\n", + "\tAAACCTGGTCTAAACC-1\\_1 & C51 & 4260 & 1307 & 2.4647887 & 4 & 4 & HC1 & HC & N & N & 8 & Macrophages\\\\\n", + "\tAAACCTGGTGCACCAC-1\\_1 & C51 & 10112 & 2211 & 1.5031646 & 13 & 13 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACCTGGTTACGCGC-1\\_1 & C51 & 6748 & 1859 & 1.8079431 & 13 & 13 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACCTGTCCGCATCT-1\\_1 & C51 & 6713 & 1835 & 2.2195740 & 13 & 13 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACGGGAGCTACCTA-1\\_1 & C51 & 4148 & 1385 & 1.4705882 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGAGGAATTAC-1\\_1 & C51 & 5470 & 1673 & 2.4314442 & 18 & 18 & HC1 & HC & N & N & 18 & Macrophages\\\\\n", + "\tAAACGGGAGGACCACA-1\\_1 & C51 & 8344 & 1843 & 1.6179291 & 6 & 6 & HC1 & HC & N & N & 4 & Macrophages\\\\\n", + "\tAAACGGGAGGCAAAGA-1\\_1 & C51 & 3075 & 975 & 1.6585366 & 0 & 0 & HC1 & HC & N & N & 1 & Macrophages\\\\\n", + "\tAAACGGGAGTCGTTTG-1\\_1 & C51 & 5684 & 1565 & 1.8472906 & 6 & 6 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACGGGCAAGGACTG-1\\_1 & C51 & 7632 & 1994 & 1.0089099 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGCACCGAAAG-1\\_1 & C51 & 4865 & 1509 & 2.0143885 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGCAGCCAATT-1\\_1 & C51 & 2381 & 747 & 1.3859723 & 23 & 23 & HC1 & HC & N & N & 20 & mDC \\\\\n", + "\tAAACGGGCATCCGTGG-1\\_1 & C51 & 6668 & 1795 & 1.7396521 & 6 & 6 & HC1 & HC & N & N & 2 & Macrophages\\\\\n", + "\tAAACGGGCATGCCTTC-1\\_1 & C51 & 5409 & 1657 & 2.1445739 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAACGGGGTACATGTC-1\\_1 & C51 & 6898 & 1786 & 2.0585677 & 6 & 6 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAACGGGGTCCATGAT-1\\_1 & C51 & 5033 & 1391 & 2.1855752 & 4 & 4 & HC1 & HC & N & N & 8 & Macrophages\\\\\n", + "\tAAACGGGGTCTCTCTG-1\\_1 & C51 & 2255 & 893 & 1.9955654 & 23 & 23 & HC1 & HC & N & N & 20 & mDC \\\\\n", + "\tAAACGGGGTGCGAAAC-1\\_1 & C51 & 8487 & 2190 & 2.0030635 & 3 & 3 & HC1 & HC & N & N & 4 & Macrophages\\\\\n", + "\tAAACGGGGTTCTGAAC-1\\_1 & C51 & 2984 & 942 & 2.9490617 & 4 & 4 & HC1 & HC & N & N & 8 & Macrophages\\\\\n", + "\tAAACGGGTCCAAACTG-1\\_1 & C51 & 4558 & 1264 & 1.9087319 & 3 & 3 & HC1 & HC & N & N & 4 & Macrophages\\\\\n", + "\tAAACGGGTCCTTTACA-1\\_1 & C51 & 1245 & 617 & 0.4819277 & 16 & 16 & HC1 & HC & N & N & 21 & Macrophages\\\\\n", + "\tAAACGGGTCGCAAACT-1\\_1 & C51 & 7031 & 1609 & 2.3609728 & 13 & 13 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\tAAAGATGAGACAGAGA-1\\_1 & C51 & 3392 & 1138 & 1.4150943 & 2 & 2 & HC1 & HC & N & N & 3 & Macrophages\\\\\n", + "\tAAAGATGAGAGGTTAT-1\\_1 & C51 & 4839 & 1512 & 2.7071709 & 6 & 6 & HC1 & HC & N & N & 5 & Macrophages\\\\\n", + "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\tTTTATGCGTCGACTGC-1\\_12 & C152 & 2300 & 537 & 7.304348 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTATGCGTTAAGATG-1\\_12 & C152 & 23879 & 4186 & 7.110851 & 15 & 15 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTATGCTCGCACTCT-1\\_12 & C152 & 1649 & 618 & 6.549424 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCAGAGACTTA-1\\_12 & C152 & 4028 & 1769 & 3.301887 & 10 & 10 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCAGATCTGAA-1\\_12 & C152 & 1354 & 660 & 6.129985 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCAGGATGGAA-1\\_12 & C152 & 2706 & 1062 & 5.284553 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCCAAGTCATC-1\\_12 & C152 & 1909 & 933 & 8.328968 & 15 & 15 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCCAGCTCCGA-1\\_12 & C152 & 6612 & 2491 & 5.051422 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCCAGGTCTCG-1\\_12 & C152 & 1951 & 834 & 7.124552 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCGTTAAGATG-1\\_12 & C152 & 1174 & 494 & 3.662692 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTCCTCTCTTGTTTG-1\\_12 & C152 & 4034 & 1400 & 9.791770 & 5 & 5 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCAGACTACAA-1\\_12 & C152 & 22478 & 4601 & 5.222885 & 5 & 5 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCAGGGTGTGT-1\\_12 & C152 & 8040 & 2804 & 9.875622 & 21 & 21 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCGTCGGCATC-1\\_12 & C152 & 21713 & 3566 & 2.574495 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCTCCAGGGCT-1\\_12 & C152 & 1624 & 510 & 9.236453 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCTCTAACCGA-1\\_12 & C152 & 4789 & 1978 & 2.276049 & 10 & 10 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGCGCTCTGCTTGC-1\\_12 & C152 & 1567 & 846 & 9.700064 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTAGATCCCGC-1\\_12 & C152 & 11612 & 2679 & 2.239063 & 4 & 4 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTAGCACCGTC-1\\_12 & C152 & 2591 & 1160 & 5.017368 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTAGTCTCCTC-1\\_12 & C152 & 1878 & 923 & 5.857295 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTCACGAGAGT-1\\_12 & C152 & 6063 & 2097 & 3.282204 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTCACTATCTT-1\\_12 & C152 & 7536 & 2640 & 5.055732 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTCATCGGGTC-1\\_12 & C152 & 4104 & 1450 & 7.090643 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTGTCTCTCTG-1\\_12 & C152 & 3916 & 1451 & 8.401430 & 0 & 0 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTTCAAACCAC-1\\_12 & C152 & 19312 & 3783 & 5.944490 & 15 & 15 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGGTTTCGCTTGTC-1\\_12 & C152 & 1116 & 641 & 8.602151 & 1 & 1 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGTCAAGAAACGCC-1\\_12 & C152 & 12869 & 3507 & 3.178180 & 14 & 14 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGTCACAACACCCG-1\\_12 & C152 & 1347 & 556 & 8.685969 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGTCAGTCTGCAAT-1\\_12 & C152 & 21274 & 3373 & 4.474946 & 19 & 19 & NA & NA & NA & NA & NA & NA\\\\\n", + "\tTTTGTCAGTGTTGGGA-1\\_12 & C152 & 4301 & 1705 & 6.254359 & 5 & 5 & NA & NA & NA & NA & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 63734 × 12\n", + "\n", + "| | orig.ident <chr> | nCount_RNA <dbl> | nFeature_RNA <int> | percent.mito <dbl> | integrated_snn_res.1.2 <fct> | seurat_clusters <fct> | sample_new <chr> | group <chr> | disease <chr> | hasnCoV <chr> | cluster <int> | celltype <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| AAACCTGAGACACTAA-1_1 | C51 | 5228 | 1635 | 1.5493497 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGAGGAGTACC-1_1 | C51 | 5071 | 1385 | 1.7945178 | 12 | 12 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGAGGATATAC-1_1 | C51 | 5531 | 1729 | 1.6271922 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGAGGTCATCT-1_1 | C51 | 5630 | 1639 | 1.9538188 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACCTGCACGGATAG-1_1 | C51 | 7358 | 1722 | 2.7724925 | 6 | 6 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACCTGCAGGGAGAG-1_1 | C51 | 4615 | 1312 | 1.4734561 | 2 | 2 | HC1 | HC | N | N | 2 | Macrophages |\n", + "| AAACCTGGTCTAAACC-1_1 | C51 | 4260 | 1307 | 2.4647887 | 4 | 4 | HC1 | HC | N | N | 8 | Macrophages |\n", + "| AAACCTGGTGCACCAC-1_1 | C51 | 10112 | 2211 | 1.5031646 | 13 | 13 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACCTGGTTACGCGC-1_1 | C51 | 6748 | 1859 | 1.8079431 | 13 | 13 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACCTGTCCGCATCT-1_1 | C51 | 6713 | 1835 | 2.2195740 | 13 | 13 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACGGGAGCTACCTA-1_1 | C51 | 4148 | 1385 | 1.4705882 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGAGGAATTAC-1_1 | C51 | 5470 | 1673 | 2.4314442 | 18 | 18 | HC1 | HC | N | N | 18 | Macrophages |\n", + "| AAACGGGAGGACCACA-1_1 | C51 | 8344 | 1843 | 1.6179291 | 6 | 6 | HC1 | HC | N | N | 4 | Macrophages |\n", + "| AAACGGGAGGCAAAGA-1_1 | C51 | 3075 | 975 | 1.6585366 | 0 | 0 | HC1 | HC | N | N | 1 | Macrophages |\n", + "| AAACGGGAGTCGTTTG-1_1 | C51 | 5684 | 1565 | 1.8472906 | 6 | 6 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACGGGCAAGGACTG-1_1 | C51 | 7632 | 1994 | 1.0089099 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGCACCGAAAG-1_1 | C51 | 4865 | 1509 | 2.0143885 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGCAGCCAATT-1_1 | C51 | 2381 | 747 | 1.3859723 | 23 | 23 | HC1 | HC | N | N | 20 | mDC |\n", + "| AAACGGGCATCCGTGG-1_1 | C51 | 6668 | 1795 | 1.7396521 | 6 | 6 | HC1 | HC | N | N | 2 | Macrophages |\n", + "| AAACGGGCATGCCTTC-1_1 | C51 | 5409 | 1657 | 2.1445739 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAACGGGGTACATGTC-1_1 | C51 | 6898 | 1786 | 2.0585677 | 6 | 6 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAACGGGGTCCATGAT-1_1 | C51 | 5033 | 1391 | 2.1855752 | 4 | 4 | HC1 | HC | N | N | 8 | Macrophages |\n", + "| AAACGGGGTCTCTCTG-1_1 | C51 | 2255 | 893 | 1.9955654 | 23 | 23 | HC1 | HC | N | N | 20 | mDC |\n", + "| AAACGGGGTGCGAAAC-1_1 | C51 | 8487 | 2190 | 2.0030635 | 3 | 3 | HC1 | HC | N | N | 4 | Macrophages |\n", + "| AAACGGGGTTCTGAAC-1_1 | C51 | 2984 | 942 | 2.9490617 | 4 | 4 | HC1 | HC | N | N | 8 | Macrophages |\n", + "| AAACGGGTCCAAACTG-1_1 | C51 | 4558 | 1264 | 1.9087319 | 3 | 3 | HC1 | HC | N | N | 4 | Macrophages |\n", + "| AAACGGGTCCTTTACA-1_1 | C51 | 1245 | 617 | 0.4819277 | 16 | 16 | HC1 | HC | N | N | 21 | Macrophages |\n", + "| AAACGGGTCGCAAACT-1_1 | C51 | 7031 | 1609 | 2.3609728 | 13 | 13 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| AAAGATGAGACAGAGA-1_1 | C51 | 3392 | 1138 | 1.4150943 | 2 | 2 | HC1 | HC | N | N | 3 | Macrophages |\n", + "| AAAGATGAGAGGTTAT-1_1 | C51 | 4839 | 1512 | 2.7071709 | 6 | 6 | HC1 | HC | N | N | 5 | Macrophages |\n", + "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", + "| TTTATGCGTCGACTGC-1_12 | C152 | 2300 | 537 | 7.304348 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTATGCGTTAAGATG-1_12 | C152 | 23879 | 4186 | 7.110851 | 15 | 15 | NA | NA | NA | NA | NA | NA |\n", + "| TTTATGCTCGCACTCT-1_12 | C152 | 1649 | 618 | 6.549424 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCAGAGACTTA-1_12 | C152 | 4028 | 1769 | 3.301887 | 10 | 10 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCAGATCTGAA-1_12 | C152 | 1354 | 660 | 6.129985 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCAGGATGGAA-1_12 | C152 | 2706 | 1062 | 5.284553 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCCAAGTCATC-1_12 | C152 | 1909 | 933 | 8.328968 | 15 | 15 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCCAGCTCCGA-1_12 | C152 | 6612 | 2491 | 5.051422 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCCAGGTCTCG-1_12 | C152 | 1951 | 834 | 7.124552 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCGTTAAGATG-1_12 | C152 | 1174 | 494 | 3.662692 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTCCTCTCTTGTTTG-1_12 | C152 | 4034 | 1400 | 9.791770 | 5 | 5 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCAGACTACAA-1_12 | C152 | 22478 | 4601 | 5.222885 | 5 | 5 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCAGGGTGTGT-1_12 | C152 | 8040 | 2804 | 9.875622 | 21 | 21 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCGTCGGCATC-1_12 | C152 | 21713 | 3566 | 2.574495 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCTCCAGGGCT-1_12 | C152 | 1624 | 510 | 9.236453 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCTCTAACCGA-1_12 | C152 | 4789 | 1978 | 2.276049 | 10 | 10 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGCGCTCTGCTTGC-1_12 | C152 | 1567 | 846 | 9.700064 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTAGATCCCGC-1_12 | C152 | 11612 | 2679 | 2.239063 | 4 | 4 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTAGCACCGTC-1_12 | C152 | 2591 | 1160 | 5.017368 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTAGTCTCCTC-1_12 | C152 | 1878 | 923 | 5.857295 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTCACGAGAGT-1_12 | C152 | 6063 | 2097 | 3.282204 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTCACTATCTT-1_12 | C152 | 7536 | 2640 | 5.055732 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTCATCGGGTC-1_12 | C152 | 4104 | 1450 | 7.090643 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTGTCTCTCTG-1_12 | C152 | 3916 | 1451 | 8.401430 | 0 | 0 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTTCAAACCAC-1_12 | C152 | 19312 | 3783 | 5.944490 | 15 | 15 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGGTTTCGCTTGTC-1_12 | C152 | 1116 | 641 | 8.602151 | 1 | 1 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGTCAAGAAACGCC-1_12 | C152 | 12869 | 3507 | 3.178180 | 14 | 14 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGTCACAACACCCG-1_12 | C152 | 1347 | 556 | 8.685969 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGTCAGTCTGCAAT-1_12 | C152 | 21274 | 3373 | 4.474946 | 19 | 19 | NA | NA | NA | NA | NA | NA |\n", + "| TTTGTCAGTGTTGGGA-1_12 | C152 | 4301 | 1705 | 6.254359 | 5 | 5 | NA | NA | NA | NA | NA | NA |\n", + "\n" + ], + "text/plain": [ + " orig.ident nCount_RNA nFeature_RNA percent.mito\n", + "AAACCTGAGACACTAA-1_1 C51 5228 1635 1.5493497 \n", + "AAACCTGAGGAGTACC-1_1 C51 5071 1385 1.7945178 \n", + "AAACCTGAGGATATAC-1_1 C51 5531 1729 1.6271922 \n", + "AAACCTGAGGTCATCT-1_1 C51 5630 1639 1.9538188 \n", + "AAACCTGCACGGATAG-1_1 C51 7358 1722 2.7724925 \n", + "AAACCTGCAGGGAGAG-1_1 C51 4615 1312 1.4734561 \n", + "AAACCTGGTCTAAACC-1_1 C51 4260 1307 2.4647887 \n", + "AAACCTGGTGCACCAC-1_1 C51 10112 2211 1.5031646 \n", + "AAACCTGGTTACGCGC-1_1 C51 6748 1859 1.8079431 \n", + "AAACCTGTCCGCATCT-1_1 C51 6713 1835 2.2195740 \n", + "AAACGGGAGCTACCTA-1_1 C51 4148 1385 1.4705882 \n", + "AAACGGGAGGAATTAC-1_1 C51 5470 1673 2.4314442 \n", + "AAACGGGAGGACCACA-1_1 C51 8344 1843 1.6179291 \n", + "AAACGGGAGGCAAAGA-1_1 C51 3075 975 1.6585366 \n", + "AAACGGGAGTCGTTTG-1_1 C51 5684 1565 1.8472906 \n", + "AAACGGGCAAGGACTG-1_1 C51 7632 1994 1.0089099 \n", + "AAACGGGCACCGAAAG-1_1 C51 4865 1509 2.0143885 \n", + "AAACGGGCAGCCAATT-1_1 C51 2381 747 1.3859723 \n", + "AAACGGGCATCCGTGG-1_1 C51 6668 1795 1.7396521 \n", + "AAACGGGCATGCCTTC-1_1 C51 5409 1657 2.1445739 \n", + "AAACGGGGTACATGTC-1_1 C51 6898 1786 2.0585677 \n", + "AAACGGGGTCCATGAT-1_1 C51 5033 1391 2.1855752 \n", + "AAACGGGGTCTCTCTG-1_1 C51 2255 893 1.9955654 \n", + "AAACGGGGTGCGAAAC-1_1 C51 8487 2190 2.0030635 \n", + "AAACGGGGTTCTGAAC-1_1 C51 2984 942 2.9490617 \n", + "AAACGGGTCCAAACTG-1_1 C51 4558 1264 1.9087319 \n", + "AAACGGGTCCTTTACA-1_1 C51 1245 617 0.4819277 \n", + "AAACGGGTCGCAAACT-1_1 C51 7031 1609 2.3609728 \n", + "AAAGATGAGACAGAGA-1_1 C51 3392 1138 1.4150943 \n", + "AAAGATGAGAGGTTAT-1_1 C51 4839 1512 2.7071709 \n", + "⋮ ⋮ ⋮ ⋮ ⋮ \n", + "TTTATGCGTCGACTGC-1_12 C152 2300 537 7.304348 \n", + "TTTATGCGTTAAGATG-1_12 C152 23879 4186 7.110851 \n", + "TTTATGCTCGCACTCT-1_12 C152 1649 618 6.549424 \n", + "TTTCCTCAGAGACTTA-1_12 C152 4028 1769 3.301887 \n", + "TTTCCTCAGATCTGAA-1_12 C152 1354 660 6.129985 \n", + "TTTCCTCAGGATGGAA-1_12 C152 2706 1062 5.284553 \n", + "TTTCCTCCAAGTCATC-1_12 C152 1909 933 8.328968 \n", + "TTTCCTCCAGCTCCGA-1_12 C152 6612 2491 5.051422 \n", + "TTTCCTCCAGGTCTCG-1_12 C152 1951 834 7.124552 \n", + "TTTCCTCGTTAAGATG-1_12 C152 1174 494 3.662692 \n", + "TTTCCTCTCTTGTTTG-1_12 C152 4034 1400 9.791770 \n", + "TTTGCGCAGACTACAA-1_12 C152 22478 4601 5.222885 \n", + "TTTGCGCAGGGTGTGT-1_12 C152 8040 2804 9.875622 \n", + "TTTGCGCGTCGGCATC-1_12 C152 21713 3566 2.574495 \n", + "TTTGCGCTCCAGGGCT-1_12 C152 1624 510 9.236453 \n", + "TTTGCGCTCTAACCGA-1_12 C152 4789 1978 2.276049 \n", + "TTTGCGCTCTGCTTGC-1_12 C152 1567 846 9.700064 \n", + "TTTGGTTAGATCCCGC-1_12 C152 11612 2679 2.239063 \n", + "TTTGGTTAGCACCGTC-1_12 C152 2591 1160 5.017368 \n", + "TTTGGTTAGTCTCCTC-1_12 C152 1878 923 5.857295 \n", + "TTTGGTTCACGAGAGT-1_12 C152 6063 2097 3.282204 \n", + "TTTGGTTCACTATCTT-1_12 C152 7536 2640 5.055732 \n", + "TTTGGTTCATCGGGTC-1_12 C152 4104 1450 7.090643 \n", + "TTTGGTTGTCTCTCTG-1_12 C152 3916 1451 8.401430 \n", + "TTTGGTTTCAAACCAC-1_12 C152 19312 3783 5.944490 \n", + "TTTGGTTTCGCTTGTC-1_12 C152 1116 641 8.602151 \n", + "TTTGTCAAGAAACGCC-1_12 C152 12869 3507 3.178180 \n", + "TTTGTCACAACACCCG-1_12 C152 1347 556 8.685969 \n", + "TTTGTCAGTCTGCAAT-1_12 C152 21274 3373 4.474946 \n", + "TTTGTCAGTGTTGGGA-1_12 C152 4301 1705 6.254359 \n", + " integrated_snn_res.1.2 seurat_clusters sample_new group\n", + "AAACCTGAGACACTAA-1_1 2 2 HC1 HC \n", + "AAACCTGAGGAGTACC-1_1 12 12 HC1 HC \n", + "AAACCTGAGGATATAC-1_1 2 2 HC1 HC \n", + "AAACCTGAGGTCATCT-1_1 2 2 HC1 HC \n", + "AAACCTGCACGGATAG-1_1 6 6 HC1 HC \n", + "AAACCTGCAGGGAGAG-1_1 2 2 HC1 HC \n", + "AAACCTGGTCTAAACC-1_1 4 4 HC1 HC \n", + "AAACCTGGTGCACCAC-1_1 13 13 HC1 HC \n", + "AAACCTGGTTACGCGC-1_1 13 13 HC1 HC \n", + "AAACCTGTCCGCATCT-1_1 13 13 HC1 HC \n", + "AAACGGGAGCTACCTA-1_1 2 2 HC1 HC \n", + "AAACGGGAGGAATTAC-1_1 18 18 HC1 HC \n", + "AAACGGGAGGACCACA-1_1 6 6 HC1 HC \n", + "AAACGGGAGGCAAAGA-1_1 0 0 HC1 HC \n", + "AAACGGGAGTCGTTTG-1_1 6 6 HC1 HC \n", + "AAACGGGCAAGGACTG-1_1 2 2 HC1 HC \n", + "AAACGGGCACCGAAAG-1_1 2 2 HC1 HC \n", + "AAACGGGCAGCCAATT-1_1 23 23 HC1 HC \n", + "AAACGGGCATCCGTGG-1_1 6 6 HC1 HC \n", + "AAACGGGCATGCCTTC-1_1 2 2 HC1 HC \n", + "AAACGGGGTACATGTC-1_1 6 6 HC1 HC \n", + "AAACGGGGTCCATGAT-1_1 4 4 HC1 HC \n", + "AAACGGGGTCTCTCTG-1_1 23 23 HC1 HC \n", + "AAACGGGGTGCGAAAC-1_1 3 3 HC1 HC \n", + "AAACGGGGTTCTGAAC-1_1 4 4 HC1 HC \n", + "AAACGGGTCCAAACTG-1_1 3 3 HC1 HC \n", + "AAACGGGTCCTTTACA-1_1 16 16 HC1 HC \n", + "AAACGGGTCGCAAACT-1_1 13 13 HC1 HC \n", + "AAAGATGAGACAGAGA-1_1 2 2 HC1 HC \n", + "AAAGATGAGAGGTTAT-1_1 6 6 HC1 HC \n", + "⋮ ⋮ ⋮ ⋮ ⋮ \n", + "TTTATGCGTCGACTGC-1_12 19 19 NA NA \n", + "TTTATGCGTTAAGATG-1_12 15 15 NA NA \n", + "TTTATGCTCGCACTCT-1_12 19 19 NA NA \n", + "TTTCCTCAGAGACTTA-1_12 10 10 NA NA \n", + "TTTCCTCAGATCTGAA-1_12 0 0 NA NA \n", + "TTTCCTCAGGATGGAA-1_12 0 0 NA NA \n", + "TTTCCTCCAAGTCATC-1_12 15 15 NA NA \n", + "TTTCCTCCAGCTCCGA-1_12 14 14 NA NA \n", + "TTTCCTCCAGGTCTCG-1_12 14 14 NA NA \n", + "TTTCCTCGTTAAGATG-1_12 19 19 NA NA \n", + "TTTCCTCTCTTGTTTG-1_12 5 5 NA NA \n", + "TTTGCGCAGACTACAA-1_12 5 5 NA NA \n", + "TTTGCGCAGGGTGTGT-1_12 21 21 NA NA \n", + "TTTGCGCGTCGGCATC-1_12 19 19 NA NA \n", + "TTTGCGCTCCAGGGCT-1_12 19 19 NA NA \n", + "TTTGCGCTCTAACCGA-1_12 10 10 NA NA \n", + "TTTGCGCTCTGCTTGC-1_12 14 14 NA NA \n", + "TTTGGTTAGATCCCGC-1_12 4 4 NA NA \n", + "TTTGGTTAGCACCGTC-1_12 0 0 NA NA \n", + "TTTGGTTAGTCTCCTC-1_12 0 0 NA NA \n", + "TTTGGTTCACGAGAGT-1_12 0 0 NA NA \n", + "TTTGGTTCACTATCTT-1_12 14 14 NA NA \n", + "TTTGGTTCATCGGGTC-1_12 0 0 NA NA \n", + "TTTGGTTGTCTCTCTG-1_12 0 0 NA NA \n", + "TTTGGTTTCAAACCAC-1_12 15 15 NA NA \n", + "TTTGGTTTCGCTTGTC-1_12 1 1 NA NA \n", + "TTTGTCAAGAAACGCC-1_12 14 14 NA NA \n", + "TTTGTCACAACACCCG-1_12 19 19 NA NA \n", + "TTTGTCAGTCTGCAAT-1_12 19 19 NA NA \n", + "TTTGTCAGTGTTGGGA-1_12 5 5 NA NA \n", + " disease hasnCoV cluster celltype \n", + "AAACCTGAGACACTAA-1_1 N N 3 Macrophages\n", + "AAACCTGAGGAGTACC-1_1 N N 3 Macrophages\n", + "AAACCTGAGGATATAC-1_1 N N 3 Macrophages\n", + "AAACCTGAGGTCATCT-1_1 N N 3 Macrophages\n", + "AAACCTGCACGGATAG-1_1 N N 5 Macrophages\n", + "AAACCTGCAGGGAGAG-1_1 N N 2 Macrophages\n", + "AAACCTGGTCTAAACC-1_1 N N 8 Macrophages\n", + "AAACCTGGTGCACCAC-1_1 N N 5 Macrophages\n", + "AAACCTGGTTACGCGC-1_1 N N 5 Macrophages\n", + "AAACCTGTCCGCATCT-1_1 N N 5 Macrophages\n", + "AAACGGGAGCTACCTA-1_1 N N 3 Macrophages\n", + "AAACGGGAGGAATTAC-1_1 N N 18 Macrophages\n", + "AAACGGGAGGACCACA-1_1 N N 4 Macrophages\n", + "AAACGGGAGGCAAAGA-1_1 N N 1 Macrophages\n", + "AAACGGGAGTCGTTTG-1_1 N N 5 Macrophages\n", + "AAACGGGCAAGGACTG-1_1 N N 3 Macrophages\n", + "AAACGGGCACCGAAAG-1_1 N N 3 Macrophages\n", + "AAACGGGCAGCCAATT-1_1 N N 20 mDC \n", + "AAACGGGCATCCGTGG-1_1 N N 2 Macrophages\n", + "AAACGGGCATGCCTTC-1_1 N N 3 Macrophages\n", + "AAACGGGGTACATGTC-1_1 N N 5 Macrophages\n", + "AAACGGGGTCCATGAT-1_1 N N 8 Macrophages\n", + "AAACGGGGTCTCTCTG-1_1 N N 20 mDC \n", + "AAACGGGGTGCGAAAC-1_1 N N 4 Macrophages\n", + "AAACGGGGTTCTGAAC-1_1 N N 8 Macrophages\n", + "AAACGGGTCCAAACTG-1_1 N N 4 Macrophages\n", + "AAACGGGTCCTTTACA-1_1 N N 21 Macrophages\n", + "AAACGGGTCGCAAACT-1_1 N N 5 Macrophages\n", + "AAAGATGAGACAGAGA-1_1 N N 3 Macrophages\n", + "AAAGATGAGAGGTTAT-1_1 N N 5 Macrophages\n", + "⋮ ⋮ ⋮ ⋮ ⋮ \n", + "TTTATGCGTCGACTGC-1_12 NA NA NA NA \n", + "TTTATGCGTTAAGATG-1_12 NA NA NA NA \n", + "TTTATGCTCGCACTCT-1_12 NA NA NA NA \n", + "TTTCCTCAGAGACTTA-1_12 NA NA NA NA \n", + "TTTCCTCAGATCTGAA-1_12 NA NA NA NA \n", + "TTTCCTCAGGATGGAA-1_12 NA NA NA NA \n", + "TTTCCTCCAAGTCATC-1_12 NA NA NA NA \n", + "TTTCCTCCAGCTCCGA-1_12 NA NA NA NA \n", + "TTTCCTCCAGGTCTCG-1_12 NA NA NA NA \n", + "TTTCCTCGTTAAGATG-1_12 NA NA NA NA \n", + "TTTCCTCTCTTGTTTG-1_12 NA NA NA NA \n", + "TTTGCGCAGACTACAA-1_12 NA NA NA NA \n", + "TTTGCGCAGGGTGTGT-1_12 NA NA NA NA \n", + "TTTGCGCGTCGGCATC-1_12 NA NA NA NA \n", + "TTTGCGCTCCAGGGCT-1_12 NA NA NA NA \n", + "TTTGCGCTCTAACCGA-1_12 NA NA NA NA \n", + "TTTGCGCTCTGCTTGC-1_12 NA NA NA NA \n", + "TTTGGTTAGATCCCGC-1_12 NA NA NA NA \n", + "TTTGGTTAGCACCGTC-1_12 NA NA NA NA \n", + "TTTGGTTAGTCTCCTC-1_12 NA NA NA NA \n", + "TTTGGTTCACGAGAGT-1_12 NA NA NA NA \n", + "TTTGGTTCACTATCTT-1_12 NA NA NA NA \n", + "TTTGGTTCATCGGGTC-1_12 NA NA NA NA \n", + "TTTGGTTGTCTCTCTG-1_12 NA NA NA NA \n", + "TTTGGTTTCAAACCAC-1_12 NA NA NA NA \n", + "TTTGGTTTCGCTTGTC-1_12 NA NA NA NA \n", + "TTTGTCAAGAAACGCC-1_12 NA NA NA NA \n", + "TTTGTCACAACACCCG-1_12 NA NA NA NA \n", + "TTTGTCAGTCTGCAAT-1_12 NA NA NA NA \n", + "TTTGTCAGTGTTGGGA-1_12 NA NA NA NA " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "good" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "231ae69d-c715-4902-b0fa-544cbe25def1", + "metadata": {}, + "outputs": [], + "source": [ + "good$cluster <- NULL\n", + "good$ID <- rownames(good)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "bffccf4c-e951-410d-ab44-85cfe2ecec3e", + "metadata": {}, + "outputs": [], + "source": [ + "good <- good %>%\n", + " dplyr::group_by(orig.ident) %>%\n", + " fill(c('sample_new', 'group', 'disease', 'hasnCoV'),\n", + " .direction = 'downup') %>%\n", + " dplyr::ungroup()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "238c4947-7d40-4955-9f72-d839f36e849f", + "metadata": {}, + "outputs": [], + "source": [ + "good <- data.frame(good)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "17708008-87f1-46b6-b162-cb2411936837", + "metadata": {}, + "outputs": [], + "source": [ + "rownames(good) <- good$ID" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "38514ea1-ed9f-4b69-aa22-7e80266fc361", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + 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A data.frame: 63734 × 11
orig.identnCount_RNAnFeature_RNApercent.mitointegrated_snn_res.1.2seurat_clusterssample_newgroupdiseasehasnCoVcelltype
<chr><dbl><int><dbl><fct><fct><chr><chr><chr><chr><chr>
C51 522816351.54934972 2 HC1HCNNMacrophages
C51 507113851.79451781212HC1HCNNMacrophages
C51 553117291.62719222 2 HC1HCNNMacrophages
C51 563016391.95381882 2 HC1HCNNMacrophages
C51 735817222.77249256 6 HC1HCNNMacrophages
C51 461513121.47345612 2 HC1HCNNMacrophages
C51 426013072.46478874 4 HC1HCNNMacrophages
C511011222111.50316461313HC1HCNNMacrophages
C51 674818591.80794311313HC1HCNNMacrophages
C51 671318352.21957401313HC1HCNNMacrophages
C51 414813851.47058822 2 HC1HCNNMacrophages
C51 547016732.43144421818HC1HCNNMacrophages
C51 834418431.61792916 6 HC1HCNNMacrophages
C51 3075 9751.65853660 0 HC1HCNNMacrophages
C51 568415651.84729066 6 HC1HCNNMacrophages
C51 763219941.00890992 2 HC1HCNNMacrophages
C51 486515092.01438852 2 HC1HCNNMacrophages
C51 2381 7471.38597232323HC1HCNNmDC
C51 666817951.73965216 6 HC1HCNNMacrophages
C51 540916572.14457392 2 HC1HCNNMacrophages
C51 689817862.05856776 6 HC1HCNNMacrophages
C51 503313912.18557524 4 HC1HCNNMacrophages
C51 2255 8931.99556542323HC1HCNNmDC
C51 848721902.00306353 3 HC1HCNNMacrophages
C51 2984 9422.94906174 4 HC1HCNNMacrophages
C51 455812641.90873193 3 HC1HCNNMacrophages
C51 1245 6170.48192771616HC1HCNNMacrophages
C51 703116092.36097281313HC1HCNNMacrophages
C51 339211381.41509432 2 HC1HCNNMacrophages
C51 483915122.70717096 6 HC1HCNNMacrophages
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
C152 2300 5377.3043481919S5SYNNA
C1522387941867.1108511515S5SYNNA
C152 1649 6186.5494241919S5SYNNA
C152 402817693.3018871010S5SYNNA
C152 1354 6606.1299850 0 S5SYNNA
C152 270610625.2845530 0 S5SYNNA
C152 1909 9338.3289681515S5SYNNA
C152 661224915.0514221414S5SYNNA
C152 1951 8347.1245521414S5SYNNA
C152 1174 4943.6626921919S5SYNNA
C152 403414009.7917705 5 S5SYNNA
C1522247846015.2228855 5 S5SYNNA
C152 804028049.8756222121S5SYNNA
C1522171335662.5744951919S5SYNNA
C152 1624 5109.2364531919S5SYNNA
C152 478919782.2760491010S5SYNNA
C152 1567 8469.7000641414S5SYNNA
C1521161226792.2390634 4 S5SYNNA
C152 259111605.0173680 0 S5SYNNA
C152 1878 9235.8572950 0 S5SYNNA
C152 606320973.2822040 0 S5SYNNA
C152 753626405.0557321414S5SYNNA
C152 410414507.0906430 0 S5SYNNA
C152 391614518.4014300 0 S5SYNNA
C1521931237835.9444901515S5SYNNA
C152 1116 6418.6021511 1 S5SYNNA
C1521286935073.1781801414S5SYNNA
C152 1347 5568.6859691919S5SYNNA
C1522127433734.4749461919S5SYNNA
C152 430117056.2543595 5 S5SYNNA
\n" + ], + "text/latex": [ + "A data.frame: 63734 × 11\n", + "\\begin{tabular}{lllllllllll}\n", + " orig.ident & nCount\\_RNA & nFeature\\_RNA & percent.mito & integrated\\_snn\\_res.1.2 & seurat\\_clusters & sample\\_new & group & disease & hasnCoV & celltype\\\\\n", + " & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t C51 & 5228 & 1635 & 1.5493497 & 2 & 2 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 5071 & 1385 & 1.7945178 & 12 & 12 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 5531 & 1729 & 1.6271922 & 2 & 2 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 5630 & 1639 & 1.9538188 & 2 & 2 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 7358 & 1722 & 2.7724925 & 6 & 6 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 4615 & 1312 & 1.4734561 & 2 & 2 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 4260 & 1307 & 2.4647887 & 4 & 4 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 10112 & 2211 & 1.5031646 & 13 & 13 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 6748 & 1859 & 1.8079431 & 13 & 13 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 6713 & 1835 & 2.2195740 & 13 & 13 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 4148 & 1385 & 1.4705882 & 2 & 2 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 5470 & 1673 & 2.4314442 & 18 & 18 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 8344 & 1843 & 1.6179291 & 6 & 6 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 3075 & 975 & 1.6585366 & 0 & 0 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 5684 & 1565 & 1.8472906 & 6 & 6 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 7632 & 1994 & 1.0089099 & 2 & 2 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 4865 & 1509 & 2.0143885 & 2 & 2 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 2381 & 747 & 1.3859723 & 23 & 23 & HC1 & HC & N & N & mDC \\\\\n", + "\t C51 & 6668 & 1795 & 1.7396521 & 6 & 6 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 5409 & 1657 & 2.1445739 & 2 & 2 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 6898 & 1786 & 2.0585677 & 6 & 6 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 5033 & 1391 & 2.1855752 & 4 & 4 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 2255 & 893 & 1.9955654 & 23 & 23 & HC1 & HC & N & N & mDC \\\\\n", + "\t C51 & 8487 & 2190 & 2.0030635 & 3 & 3 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 2984 & 942 & 2.9490617 & 4 & 4 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 4558 & 1264 & 1.9087319 & 3 & 3 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 1245 & 617 & 0.4819277 & 16 & 16 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 7031 & 1609 & 2.3609728 & 13 & 13 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 3392 & 1138 & 1.4150943 & 2 & 2 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t C51 & 4839 & 1512 & 2.7071709 & 6 & 6 & HC1 & HC & N & N & Macrophages\\\\\n", + "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\t C152 & 2300 & 537 & 7.304348 & 19 & 19 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 23879 & 4186 & 7.110851 & 15 & 15 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1649 & 618 & 6.549424 & 19 & 19 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 4028 & 1769 & 3.301887 & 10 & 10 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1354 & 660 & 6.129985 & 0 & 0 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 2706 & 1062 & 5.284553 & 0 & 0 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1909 & 933 & 8.328968 & 15 & 15 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 6612 & 2491 & 5.051422 & 14 & 14 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1951 & 834 & 7.124552 & 14 & 14 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1174 & 494 & 3.662692 & 19 & 19 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 4034 & 1400 & 9.791770 & 5 & 5 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 22478 & 4601 & 5.222885 & 5 & 5 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 8040 & 2804 & 9.875622 & 21 & 21 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 21713 & 3566 & 2.574495 & 19 & 19 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1624 & 510 & 9.236453 & 19 & 19 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 4789 & 1978 & 2.276049 & 10 & 10 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1567 & 846 & 9.700064 & 14 & 14 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 11612 & 2679 & 2.239063 & 4 & 4 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 2591 & 1160 & 5.017368 & 0 & 0 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1878 & 923 & 5.857295 & 0 & 0 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 6063 & 2097 & 3.282204 & 0 & 0 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 7536 & 2640 & 5.055732 & 14 & 14 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 4104 & 1450 & 7.090643 & 0 & 0 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 3916 & 1451 & 8.401430 & 0 & 0 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 19312 & 3783 & 5.944490 & 15 & 15 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1116 & 641 & 8.602151 & 1 & 1 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 12869 & 3507 & 3.178180 & 14 & 14 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 1347 & 556 & 8.685969 & 19 & 19 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 21274 & 3373 & 4.474946 & 19 & 19 & S5 & S & Y & N & NA\\\\\n", + "\t C152 & 4301 & 1705 & 6.254359 & 5 & 5 & S5 & S & Y & N & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 63734 × 11\n", + "\n", + "| orig.ident <chr> | nCount_RNA <dbl> | nFeature_RNA <int> | percent.mito <dbl> | integrated_snn_res.1.2 <fct> | seurat_clusters <fct> | sample_new <chr> | group <chr> | disease <chr> | hasnCoV <chr> | celltype <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|\n", + "| C51 | 5228 | 1635 | 1.5493497 | 2 | 2 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 5071 | 1385 | 1.7945178 | 12 | 12 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 5531 | 1729 | 1.6271922 | 2 | 2 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 5630 | 1639 | 1.9538188 | 2 | 2 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 7358 | 1722 | 2.7724925 | 6 | 6 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 4615 | 1312 | 1.4734561 | 2 | 2 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 4260 | 1307 | 2.4647887 | 4 | 4 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 10112 | 2211 | 1.5031646 | 13 | 13 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 6748 | 1859 | 1.8079431 | 13 | 13 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 6713 | 1835 | 2.2195740 | 13 | 13 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 4148 | 1385 | 1.4705882 | 2 | 2 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 5470 | 1673 | 2.4314442 | 18 | 18 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 8344 | 1843 | 1.6179291 | 6 | 6 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 3075 | 975 | 1.6585366 | 0 | 0 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 5684 | 1565 | 1.8472906 | 6 | 6 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 7632 | 1994 | 1.0089099 | 2 | 2 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 4865 | 1509 | 2.0143885 | 2 | 2 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 2381 | 747 | 1.3859723 | 23 | 23 | HC1 | HC | N | N | mDC |\n", + "| C51 | 6668 | 1795 | 1.7396521 | 6 | 6 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 5409 | 1657 | 2.1445739 | 2 | 2 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 6898 | 1786 | 2.0585677 | 6 | 6 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 5033 | 1391 | 2.1855752 | 4 | 4 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 2255 | 893 | 1.9955654 | 23 | 23 | HC1 | HC | N | N | mDC |\n", + "| C51 | 8487 | 2190 | 2.0030635 | 3 | 3 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 2984 | 942 | 2.9490617 | 4 | 4 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 4558 | 1264 | 1.9087319 | 3 | 3 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 1245 | 617 | 0.4819277 | 16 | 16 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 7031 | 1609 | 2.3609728 | 13 | 13 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 3392 | 1138 | 1.4150943 | 2 | 2 | HC1 | HC | N | N | Macrophages |\n", + "| C51 | 4839 | 1512 | 2.7071709 | 6 | 6 | HC1 | HC | N | N | Macrophages |\n", + "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", + "| C152 | 2300 | 537 | 7.304348 | 19 | 19 | S5 | S | Y | N | NA |\n", + "| C152 | 23879 | 4186 | 7.110851 | 15 | 15 | S5 | S | Y | N | NA |\n", + "| C152 | 1649 | 618 | 6.549424 | 19 | 19 | S5 | S | Y | N | NA |\n", + "| C152 | 4028 | 1769 | 3.301887 | 10 | 10 | S5 | S | Y | N | NA |\n", + "| C152 | 1354 | 660 | 6.129985 | 0 | 0 | S5 | S | Y | N | NA |\n", + "| C152 | 2706 | 1062 | 5.284553 | 0 | 0 | S5 | S | Y | N | NA |\n", + "| C152 | 1909 | 933 | 8.328968 | 15 | 15 | S5 | S | Y | N | NA |\n", + "| C152 | 6612 | 2491 | 5.051422 | 14 | 14 | S5 | S | Y | N | NA |\n", + "| C152 | 1951 | 834 | 7.124552 | 14 | 14 | S5 | S | Y | N | NA |\n", + "| C152 | 1174 | 494 | 3.662692 | 19 | 19 | S5 | S | Y | N | NA |\n", + "| C152 | 4034 | 1400 | 9.791770 | 5 | 5 | S5 | S | Y | N | NA |\n", + "| C152 | 22478 | 4601 | 5.222885 | 5 | 5 | S5 | S | Y | N | NA |\n", + "| C152 | 8040 | 2804 | 9.875622 | 21 | 21 | S5 | S | Y | N | NA |\n", + "| C152 | 21713 | 3566 | 2.574495 | 19 | 19 | S5 | S | Y | N | NA |\n", + "| C152 | 1624 | 510 | 9.236453 | 19 | 19 | S5 | S | Y | N | NA |\n", + "| C152 | 4789 | 1978 | 2.276049 | 10 | 10 | S5 | S | Y | N | NA |\n", + "| C152 | 1567 | 846 | 9.700064 | 14 | 14 | S5 | S | Y | N | NA |\n", + "| C152 | 11612 | 2679 | 2.239063 | 4 | 4 | S5 | S | Y | N | NA |\n", + "| C152 | 2591 | 1160 | 5.017368 | 0 | 0 | S5 | S | Y | N | NA |\n", + "| C152 | 1878 | 923 | 5.857295 | 0 | 0 | S5 | S | Y | N | NA |\n", + "| C152 | 6063 | 2097 | 3.282204 | 0 | 0 | S5 | S | Y | N | NA |\n", + "| C152 | 7536 | 2640 | 5.055732 | 14 | 14 | S5 | S | Y | N | NA |\n", + "| C152 | 4104 | 1450 | 7.090643 | 0 | 0 | S5 | S | Y | N | NA |\n", + "| C152 | 3916 | 1451 | 8.401430 | 0 | 0 | S5 | S | Y | N | NA |\n", + "| C152 | 19312 | 3783 | 5.944490 | 15 | 15 | S5 | S | Y | N | NA |\n", + "| C152 | 1116 | 641 | 8.602151 | 1 | 1 | S5 | S | Y | N | NA |\n", + "| C152 | 12869 | 3507 | 3.178180 | 14 | 14 | S5 | S | Y | N | NA |\n", + "| C152 | 1347 | 556 | 8.685969 | 19 | 19 | S5 | S | Y | N | NA |\n", + "| C152 | 21274 | 3373 | 4.474946 | 19 | 19 | S5 | S | Y | N | NA |\n", + "| C152 | 4301 | 1705 | 6.254359 | 5 | 5 | S5 | S | Y | N | NA |\n", + "\n" + ], + "text/plain": [ + " orig.ident nCount_RNA nFeature_RNA percent.mito integrated_snn_res.1.2\n", + "1 C51 5228 1635 1.5493497 2 \n", + "2 C51 5071 1385 1.7945178 12 \n", + "3 C51 5531 1729 1.6271922 2 \n", + "4 C51 5630 1639 1.9538188 2 \n", + "5 C51 7358 1722 2.7724925 6 \n", + "6 C51 4615 1312 1.4734561 2 \n", + "7 C51 4260 1307 2.4647887 4 \n", + "8 C51 10112 2211 1.5031646 13 \n", + "9 C51 6748 1859 1.8079431 13 \n", + "10 C51 6713 1835 2.2195740 13 \n", + "11 C51 4148 1385 1.4705882 2 \n", + "12 C51 5470 1673 2.4314442 18 \n", + "13 C51 8344 1843 1.6179291 6 \n", + "14 C51 3075 975 1.6585366 0 \n", + "15 C51 5684 1565 1.8472906 6 \n", + "16 C51 7632 1994 1.0089099 2 \n", + "17 C51 4865 1509 2.0143885 2 \n", + "18 C51 2381 747 1.3859723 23 \n", + "19 C51 6668 1795 1.7396521 6 \n", + "20 C51 5409 1657 2.1445739 2 \n", + "21 C51 6898 1786 2.0585677 6 \n", + "22 C51 5033 1391 2.1855752 4 \n", + "23 C51 2255 893 1.9955654 23 \n", + "24 C51 8487 2190 2.0030635 3 \n", + "25 C51 2984 942 2.9490617 4 \n", + "26 C51 4558 1264 1.9087319 3 \n", + "27 C51 1245 617 0.4819277 16 \n", + "28 C51 7031 1609 2.3609728 13 \n", + "29 C51 3392 1138 1.4150943 2 \n", + "30 C51 4839 1512 2.7071709 6 \n", + "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", + "63705 C152 2300 537 7.304348 19 \n", + "63706 C152 23879 4186 7.110851 15 \n", + "63707 C152 1649 618 6.549424 19 \n", + "63708 C152 4028 1769 3.301887 10 \n", + "63709 C152 1354 660 6.129985 0 \n", + "63710 C152 2706 1062 5.284553 0 \n", + "63711 C152 1909 933 8.328968 15 \n", + "63712 C152 6612 2491 5.051422 14 \n", + "63713 C152 1951 834 7.124552 14 \n", + "63714 C152 1174 494 3.662692 19 \n", + "63715 C152 4034 1400 9.791770 5 \n", + "63716 C152 22478 4601 5.222885 5 \n", + "63717 C152 8040 2804 9.875622 21 \n", + "63718 C152 21713 3566 2.574495 19 \n", + "63719 C152 1624 510 9.236453 19 \n", + "63720 C152 4789 1978 2.276049 10 \n", + "63721 C152 1567 846 9.700064 14 \n", + "63722 C152 11612 2679 2.239063 4 \n", + "63723 C152 2591 1160 5.017368 0 \n", + "63724 C152 1878 923 5.857295 0 \n", + "63725 C152 6063 2097 3.282204 0 \n", + "63726 C152 7536 2640 5.055732 14 \n", + "63727 C152 4104 1450 7.090643 0 \n", + "63728 C152 3916 1451 8.401430 0 \n", + "63729 C152 19312 3783 5.944490 15 \n", + "63730 C152 1116 641 8.602151 1 \n", + "63731 C152 12869 3507 3.178180 14 \n", + "63732 C152 1347 556 8.685969 19 \n", + "63733 C152 21274 3373 4.474946 19 \n", + "63734 C152 4301 1705 6.254359 5 \n", + " seurat_clusters sample_new group disease hasnCoV celltype \n", + "1 2 HC1 HC N N Macrophages\n", + "2 12 HC1 HC N N Macrophages\n", + "3 2 HC1 HC N N Macrophages\n", + "4 2 HC1 HC N N Macrophages\n", + "5 6 HC1 HC N N Macrophages\n", + "6 2 HC1 HC N N Macrophages\n", + "7 4 HC1 HC N N Macrophages\n", + "8 13 HC1 HC N N Macrophages\n", + "9 13 HC1 HC N N Macrophages\n", + "10 13 HC1 HC N N Macrophages\n", + "11 2 HC1 HC N N Macrophages\n", + "12 18 HC1 HC N N Macrophages\n", + "13 6 HC1 HC N N Macrophages\n", + "14 0 HC1 HC N N Macrophages\n", + "15 6 HC1 HC N N Macrophages\n", + "16 2 HC1 HC N N Macrophages\n", + "17 2 HC1 HC N N Macrophages\n", + "18 23 HC1 HC N N mDC \n", + "19 6 HC1 HC N N Macrophages\n", + "20 2 HC1 HC N N Macrophages\n", + "21 6 HC1 HC N N Macrophages\n", + "22 4 HC1 HC N N Macrophages\n", + "23 23 HC1 HC N N mDC \n", + "24 3 HC1 HC N N Macrophages\n", + "25 4 HC1 HC N N Macrophages\n", + "26 3 HC1 HC N N Macrophages\n", + "27 16 HC1 HC N N Macrophages\n", + "28 13 HC1 HC N N Macrophages\n", + "29 2 HC1 HC N N Macrophages\n", + "30 6 HC1 HC N N Macrophages\n", + "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", + "63705 19 S5 S Y N NA \n", + "63706 15 S5 S Y N NA \n", + "63707 19 S5 S Y N NA \n", + "63708 10 S5 S Y N NA \n", + "63709 0 S5 S Y N NA \n", + "63710 0 S5 S Y N NA \n", + "63711 15 S5 S Y N NA \n", + "63712 14 S5 S Y N NA \n", + "63713 14 S5 S Y N NA \n", + "63714 19 S5 S Y N NA \n", + "63715 5 S5 S Y N NA \n", + "63716 5 S5 S Y N NA \n", + "63717 21 S5 S Y N NA \n", + "63718 19 S5 S Y N NA \n", + "63719 19 S5 S Y N NA \n", + "63720 10 S5 S Y N NA \n", + "63721 14 S5 S Y N NA \n", + "63722 4 S5 S Y N NA \n", + "63723 0 S5 S Y N NA \n", + "63724 0 S5 S Y N NA \n", + "63725 0 S5 S Y N NA \n", + "63726 14 S5 S Y N NA \n", + "63727 0 S5 S Y N NA \n", + "63728 0 S5 S Y N NA \n", + "63729 15 S5 S Y N NA \n", + "63730 1 S5 S Y N NA \n", + "63731 14 S5 S Y N NA \n", + "63732 19 S5 S Y N NA \n", + "63733 19 S5 S Y N NA \n", + "63734 5 S5 S Y N NA " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nCoV.integrated@meta.data" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "82f183dd-04d3-43d3-a72f-33241502ac5e", + "metadata": {}, + "outputs": [], + "source": [ + "nCoV.integrated <- AddMetaData(object = nCoV.integrated,\n", + " metadata = good)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "42f82c63-926d-4763-97b6-b4f8a4099a26", + "metadata": {}, + "outputs": [], + "source": [ + "nCoV.integrated@meta.data$ID <- NULL" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "091cdd58-c3d5-4db0-b774-148edd261e73", + "metadata": {}, + "outputs": [], + "source": [ + "nCoV.integrated@meta.data$celltype <- NULL" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "dd3b2352-7bab-4966-8b4a-e85c3a9c2669", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“Removed 29 rows containing missing values (geom_text).”\n" + ] + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“Removed 29 rows containing missing values (geom_text).”\n" + ] + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“Removed 29 rows containing missing values (geom_text).”\n" + ] + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dpi <- 300\n", + "png(file=\"nCoV-umap-group-sample.png\", width = dpi*8, height = dpi*6, units = \"px\",res = dpi,type='cairo')\n", + "DimPlot(object = nCoV.integrated, reduction = 'umap',label = FALSE, group.by = 'sample_new')\n", + "dev.off()\n", + "\n", + "png(file=\"nCoV-umap-split-sample.png\", width = dpi*16, height = dpi*16, units = \"px\",res = dpi,type='cairo')\n", + "DimPlot(object = nCoV.integrated, reduction = 'umap',label = TRUE, split.by = 'sample_new', ncol = 4)\n", + "dev.off()\n", + "\n", + "png(file=\"nCoV-umap-group-group.png\", width = dpi*8, height = dpi*6, units = \"px\",res = dpi,type='cairo')\n", + "DimPlot(object = nCoV.integrated, reduction = 'umap',label = FALSE, group.by = 'group')\n", + "dev.off()\n", + "\n", + "png(file=\"nCoV-umap-split-group.png\", width = dpi*12, height = dpi*4, units = \"px\",res = dpi,type='cairo')\n", + "DimPlot(object = nCoV.integrated, reduction = 'umap',label = TRUE, split.by = 'group', ncol = 3)\n", + "dev.off()\n", + "#\n", + "png(file=\"nCoV-umap-group-disease.png\", width = dpi*8, height = dpi*6, units = \"px\",res = dpi,type='cairo')\n", + "DimPlot(object = nCoV.integrated, reduction = 'umap',label = FALSE, group.by = 'disease')\n", + "dev.off()\n", + "#\n", + "png(file=\"nCoV-umap-split-disease.png\", width = dpi*10, height = dpi*4.5, units = \"px\",res = dpi,type='cairo')\n", + "DimPlot(object = nCoV.integrated, reduction = 'umap',label = TRUE, split.by = 'disease', ncol = 2)\n", + "dev.off()" + ] + }, + { + "cell_type": "markdown", + "id": "84a6235a-d497-44cc-8765-75f3c9c2a42c", + "metadata": {}, + "source": [ + "### After integration and sample annotation, we do the EDAs on the distinct cluster" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd907003-6329-40bc-b2cc-de6d299675d5", + "metadata": {}, + "outputs": [], + "source": [ + "nCoV.integrated$disease" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "id": "b3355d8a-21a9-4386-b1ee-70c445c4699c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dpi <- 300\n", + "pt <- table(nCoV.integrated$seurat_clusters, nCoV.integrated$disease)\n", + "pt <- as.data.frame(pt)\n", + "pt$Var1 <- as.character(pt$Var1)\n", + "\n", + "png(file=\"proportion_disease.png\", width = dpi*7, height = dpi*5, units = \"px\",res = dpi,type='cairo')\n", + "ggplot(pt, aes(x = Var2, y = Freq, fill = Var1)) +\n", + " theme_bw(base_size = 15) +\n", + " geom_col(position = \"fill\", width = 0.5) +\n", + " xlab(\"Disease\") +\n", + " ylab(\"Proportion\") +\n", + " scale_colour_brewer() +\n", + " theme(legend.title = element_blank())\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "id": "600be108-aaa1-43ea-b820-130144d07685", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Removing cells with NA for 1 or more grouping variables\n", + "\n", + "Warning message:\n", + "“The following arguments are not used: row.names”\n", + "Centering and scaling data matrix\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "'Average'" + ], + "text/latex": [ + "'Average'" + ], + "text/markdown": [ + "'Average'" + ], + "text/plain": [ + "[1] \"Average\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "