diff --git a/docs/articles/SpaTopic.html b/docs/articles/SpaTopic.html index a3aa9f5..125e34b 100644 --- a/docs/articles/SpaTopic.html +++ b/docs/articles/SpaTopic.html @@ -162,7 +162,7 @@

Gibbs Sampling#> [1] "Output model perplexity.." #> [1] 11.31563 #> user system elapsed -#> 85.02 0.30 85.51 +#> 82.82 0.28 83.22

Topic Content and Distribution diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index b199ec5..1292141 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -5,5 +5,5 @@ articles: Model_Selection: Model_Selection.html SpaTopic: SpaTopic.html Intro_SpaTopic: Intro_SpaTopic.html -last_built: 2023-12-19T20:56Z +last_built: 2023-12-19T21:07Z diff --git a/docs/search.json b/docs/search.json index a2ca6ee..e5b2159 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"/articles/Intro_SpaTopic.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"SpaTopic Basics","text":"Recent advancements multiplexed tissue imaging allow examination tissue microenvironments great detail. cutting-edge technologies offer invaluable insights cellular heterogeneity spatial architectures, playing crucial role decoding mechanisms treatment response disease progression. However, gaining deep understanding complex spatial patterns remains challenging. SpaTopic implements novel spatial topic model integrate cell type spatial information identify complex spatial tissue structures without human intervention. Collapsed Gibbs sampling algorithm used model inference. Contrasting computationally intensive K-nearest-neighbor-based cell neighborhood analysis approaches, SpaTopic scalable large-scale image datasets without extracting neighborhood information every single cell. SpaTopic can applied either single image across multiple images.","code":""},{"path":"/articles/Intro_SpaTopic.html","id":"simple-usage","dir":"Articles","previous_headings":"","what":"Simple Usage","title":"SpaTopic Basics","text":"required input SpaTopic data frame containing cells within single image list data frames multiple images. data frame consists four columns: image: Image ID X, Y: X, Y cell coordinate type: cell type information Run Gibbs Sampling Check output SpaTopic detailed usage SpaTopic interprete output SpaTopic, please check complete tutorial SpaTopic Home Page. also provide function prepare input Seurat v5 object.","code":"library(SpaTopic) ## The input can be a data frame or a list of data frames data(\"lung5\") head(lung5) #> image X Y type #> 1_1 image1 4215.889 158847.7 Dendritic #> 2_1 image1 6092.889 158834.7 Macrophage #> 3_1 image1 7214.889 158843.7 Neuroendocrine #> 4_1 image1 7418.889 158813.7 Macrophage #> 5_1 image1 7446.889 158845.7 Macrophage #> 6_1 image1 3254.889 158838.7 CD4 T ## Gibbs sampling gibbs.res<-SpaTopic_inference(lung5, ntopics = 7, sigma = 50, region_radius = 400) #> [1] \"number of cells per image:\" #> #> image1 #> 100149 #> [1] \"Initialization....\" #> [1] \"Numer of Initializations:\" #> [1] 10 #> [1] \"Min perplexity during initialization:\" #> [1] 11.63023 #> [1] \"number of region centers selected:\" #> [1] 971 #> [1] \"number of cells per region on average:\" #> [1] 103.1401 #> [1] \"Finish initialization. Start Gibbs sampling....\" #> [1] \"Output model perplexity..\" #> [1] 11.31563 str(gibbs.res) #> List of 8 #> $ Perplexity : num 11.3 #> $ Deviance : num 485960 #> $ loglikelihood: num -242980 #> $ Beta : num [1:38, 1:7] 0.03587 0.02539 0.00755 0.01858 0.02585 ... #> $ Theta : num [1:971, 1:7] 0.855601 0.000232 0.999269 0.99889 0.998725 ... #> $ Ndk : int [1:971, 1:7] 107 0 82 54 47 72 100 0 0 0 ... #> $ Nwk : int [1:38, 1:7] 390 276 82 202 281 505 697 522 29 58 ... #> $ Z.trace : int [1:100149, 1:7] 13 173 27 164 157 4 20 21 15 19 ..."},{"path":"/articles/Model_Selection.html","id":"simple-usage","dir":"Articles","previous_headings":"","what":"Simple Usage","title":"Model Selection","text":"","code":"library(SpaTopic)"},{"path":[]},{"path":[]},{"path":[]},{"path":"/articles/SpaTopic.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to SpaTopic","text":"Recent advancements multiplexed tissue imaging allow examination tissue microenvironments great detail. cutting-edge technologies offer invaluable insights cellular heterogeneity spatial architectures, playing crucial role decoding mechanisms treatment response disease progression. However, gaining deep understanding complex spatial patterns remains challenging. SpaTopic implements novel spatial topic model integrate cell type spatial information identify complex spatial tissue structures without human intervention. Collapsed Gibbs sampling algorithm used model inference. Contrasting computationally intensive K-nearest-neighbor-based cell neighborhood analysis approaches, SpaTopic scalable large-scale image datasets without extracting neighborhood information every single cell. SpaTopic can applied either single image across multiple images.","code":""},{"path":"/articles/SpaTopic.html","id":"set-up","dir":"Articles","previous_headings":"","what":"Set-up","title":"Introduction to SpaTopic","text":"use non-small cell lung cancer image illustrate use SpaTopic. data object can download , original public resources available nanostring website. images generated using 960-plex CoxMx RNA panel Nanostring CoxMx Spatial Molecular Imager platform. selected Lung5-1 sample annotated cells using Azimuth based human lung reference v1.0. Lung5-1 sample contains 38 annotated cell types. Since used healthy lung tissue reference, tumor cells labeled ’basal’ cells. informaion can found . can use Seurat function ImageDimPlot visualize distribution cell types image.","code":"## We use Seurat v5 package to visualize the results. ## If you still use Seurat v4, you will have the error library(Seurat, quietly = TRUE);packageVersion(\"Seurat\") #> [1] '4.9.9.9050' ## Load the Seurat object for the image load(\"~/github/SpaTopic_test/data/nanostring_example.rdata\") ## for large dataset options(future.globals.maxSize = 1e9) library(ggplot2) celltype.plot <-ImageDimPlot(nano.obj, fov = \"lung5.rep1\", axes = TRUE, cols = \"glasbey\",dark.background = T) celltype.plot+theme(legend.position = \"bottom\",legend.direction = \"vertical\")"},{"path":"/articles/SpaTopic.html","id":"topic-inference-on-a-single-image","dir":"Articles","previous_headings":"","what":"Topic Inference on a Single Image","title":"Introduction to SpaTopic","text":"Now, data ready. show example use SpaTopic identify tissue architectures multiplexed images.","code":""},{"path":"/articles/SpaTopic.html","id":"input","dir":"Articles","previous_headings":"Topic Inference on a Single Image","what":"Input","title":"Introduction to SpaTopic","text":"required input SpaTopic data frame containing cells within single image list data frames multiple images. data frame consists four columns: image ID, X, Y cell coordinates, cell type. may use function Seurat5obj_to_SpaTopic() extract input data typical Seurat v5 object. column name cell type information need provided via option group..","code":"library(SpaTopic);packageVersion(\"SpaTopic\") #> [1] '0.99' ## Prepare input from Seurat Object dataset<-Seurat5obj_to_SpaTopic(object = nano.obj, group.by = \"predicted.annotation.l1\",image = \"image1\") head(dataset) #> image X Y type #> 1_1 image1 4215.889 158847.7 Dendritic #> 2_1 image1 6092.889 158834.7 Macrophage #> 3_1 image1 7214.889 158843.7 Neuroendocrine #> 4_1 image1 7418.889 158813.7 Macrophage #> 5_1 image1 7446.889 158845.7 Macrophage #> 6_1 image1 3254.889 158838.7 CD4 T"},{"path":"/articles/SpaTopic.html","id":"gibbs-sampling","dir":"Articles","previous_headings":"Topic Inference on a Single Image","what":"Gibbs Sampling","title":"Introduction to SpaTopic","text":"step takes around 90 seconds regular laptop","code":"## Gibbs sampling for SpaTopic system.time(gibbs.res<-SpaTopic_inference(dataset, ntopics = 7, sigma = 50, region_radius = 400)) #> [1] \"number of cells per image:\" #> #> image1 #> 100149 #> [1] \"Initialization....\" #> [1] \"Numer of Initializations:\" #> [1] 10 #> Warning: package 'RANN' was built under R version 4.1.3 #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 7.2.1; sf_use_s2() is TRUE #> [1] \"Min perplexity during initialization:\" #> [1] 11.63023 #> [1] \"number of region centers selected:\" #> [1] 971 #> [1] \"number of cells per region on average:\" #> [1] 103.1401 #> [1] \"Finish initialization. Start Gibbs sampling....\" #> [1] \"Output model perplexity..\" #> [1] 11.31563 #> user system elapsed #> 85.02 0.30 85.51"},{"path":"/articles/SpaTopic.html","id":"topic-content-and-distribution","dir":"Articles","previous_headings":"Topic Inference on a Single Image","what":"Topic Content and Distribution","title":"Introduction to SpaTopic","text":"SpaTopic identify seven topics image. use heatmap show cell type composition within topic. assign cell topic highest posterior probability visualize distribution cell topics image.","code":"library(pheatmap) #> Warning: package 'pheatmap' was built under R version 4.1.3 m <- as.data.frame(gibbs.res$Beta) colnames(m)<-paste0(\"topic\",1:7) rownames(m)<-levels(as.factor(nano.obj$predicted.annotation.l1)) pheatmap::pheatmap(t(m)) prob<-as.matrix(gibbs.res$Z.trace) nano.obj$Topic<-as.factor(apply(prob,1,which.max)) library(ggplot2) ImageDimPlot(nano.obj, fov = \"lung5.rep1\", group.by = \"Topic\", axes = TRUE, dark.background = T,cols = \"glasbey\") + ggtitle(\"Topic\")"},{"path":"/articles/SpaTopic.html","id":"compare-to-buildnicheassay-in-seurat-v5","dir":"Articles","previous_headings":"Topic Inference on a Single Image","what":"Compare to BuildNicheAssay() in Seurat v5","title":"Introduction to SpaTopic","text":"compare SpaTopic function BuildNicheAssay() Seurat v5. took around 5 min laptop. also visualize distribution seven niches image.","code":"### NOT RUN!! We use the pre-computed result system.time(nano.obj <- BuildNicheAssay(object = nano.obj, \"lung5.rep1\", group.by = \"predicted.annotation.l1\", niches.k = 7, neighbors.k = 100)) ImageDimPlot(nano.obj, fov = \"lung5.rep1\", group.by = \"niches\", axes = TRUE, dark.background = T,cols = \"glasbey\") + ggtitle(\"Niches\")"},{"path":"/articles/SpaTopic.html","id":"topic-inference-on-multiple-images","dir":"Articles","previous_headings":"","what":"Topic Inference on Multiple Images","title":"Introduction to SpaTopic","text":"SpaTopic can identify common tissue patterns across multiple images. input list data frames. See example (run). Please check examples SpaTopic Home Page.","code":"## tissue1, tissue2 are data frames of two different images. gibbs.res<-SpaTopic_inference(list(A = tissue1, B = tissue2), ntopics = 7, sigma = 50, region_radius = 400)"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Xiyu Peng. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Peng X (2023). SpaTopic: Topic Inference Identify Tissue Architecture Multiplexed Images. R package version 1.0, https://github.com/xiyupeng/SpaTopic.","code":"@Manual{, title = {SpaTopic: Topic Inference to Identify Tissue Architecture in Multiplexed Images}, author = {Xiyu Peng}, year = {2023}, note = {R package version 1.0}, url = {https://github.com/xiyupeng/SpaTopic}, }"},{"path":"/index.html","id":"spatopic","dir":"","previous_headings":"","what":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"R package fast topic inference identify tissue architecture multiplexed images. implements spatial topic model identify immunologic topics across multiplexed images, given cell location cell type information input. Collapsed Gibbs Sampling algorithm used model inference. Compared KNN-based methods (KNN-kmeans, default Seurat v5 R package), SpaTopic runs much faster large-scale image dataset.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"SpaTopic can installed GitHub repository using devtools package.","code":"# install.packages(\"devtools\") devtools::install_github(\"xiyupeng/SpaTopic\")"},{"path":"/index.html","id":"dependency","dir":"","previous_headings":"","what":"Dependency","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"SpaTopic requires dependency following R packages: Rcpp C++ codes RcppProgress C++ codes RcppArmadillo C++ codes RANN fast KNN foreach parallel computing sf spatial analysis","code":""},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"required input SpaTopic data frame containing cells within single image list data frames multiple images. data frame consists four columns: image ID, X, Y cell coordinates, cell type information. detailed usage SpaTopic, please check tutorial.","code":"library(SpaTopic) ## The input can be a data frame or a list of data frames data(\"lung5\") head(lung5) # image X Y type #1_1 image1 4215.889 158847.7 Dendritic #2_1 image1 6092.889 158834.7 Macrophage #3_1 image1 7214.889 158843.7 Neuroendocrine #4_1 image1 7418.889 158813.7 Macrophage #5_1 image1 7446.889 158845.7 Macrophage #6_1 image1 3254.889 158838.7 CD4 T gibbs.res<-SpaTopic_inference(lung5, ntopics = 7, sigma = 50, region_radius = 400)"},{"path":"/index.html","id":"data","dir":"","previous_headings":"","what":"Data","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"example image used tutorial can downloaded . stored Seurat v5 object.","code":""},{"path":"/index.html","id":"output","dir":"","previous_headings":"","what":"Output","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"","code":"str(gibbs.res) #List of 8 # $ Perplexity : num 11.3 # $ Deviance : num 485960 # $ loglikelihood: num -242980 # $ Beta : num [1:38, 1:7] 0.03587 0.02539 0.00755 0.01858 0.02585 ... # $ Theta : num [1:971, 1:7] 0.855601 0.000232 0.999269 0.99889 0.998725 ... # $ Ndk : int [1:971, 1:7] 107 0 82 54 47 72 100 0 0 0 ... # $ Nwk : int [1:38, 1:7] 390 276 82 202 281 505 697 522 29 58 ... # $ Z.trace : int [1:100149, 1:7] 13 173 27 164 157 4 20 21 15 19 ..."},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"Coming soon……","code":""},{"path":"/index.html","id":"contact","dir":"","previous_headings":"","what":"Contact","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"problems, please contact: Xiyu Peng (pansypeng124@gmail.com, pengx1@mskcc.org)","code":""},{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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END TERMS CONDITIONS","code":""},{"path":"/reference/gibbs.res-class.html","id":null,"dir":"Reference","previous_headings":"","what":"A class of the output from SpaTopic — gibbs.res-class","title":"A class of the output from SpaTopic — gibbs.res-class","text":"Outputs function SpaTopic_inference. list contains following members: $Perplexity. perplexity training data. Let N total number cells across images. \\(Perplexity = exp(-loglikelihood/N)\\) $Deviance. \\(Deviance = -2loglikelihood\\). $loglikelihood. model log-likelihood. $loglike.trace. log-likelihood every collected posterior sample. NULL trace = FALSE. $Beta. Topic content matrix rows celltypes columns topics $Theta. Topic prevalent matrix rows regions columns topics $Ndk. Number cells per topic (col) per region (row). $Nwk. Number cells per topic (col) per celltype (row). $Z.trace. Number times cell assigned topic across posterior samples. can compute posterior distributions Z (topic assignment) individual cells. $doc.trace. Ndk every collected posterior sample. NULL trace = FALSE. $word.trace. Nwk every collected posterior sample. NULL trace = FALSE.","code":""},{"path":[]},{"path":"/reference/lung5.html","id":null,"dir":"Reference","previous_headings":"","what":"Example input data for SpaTopic — lung5","title":"Example input data for SpaTopic — lung5","text":"multiplexed image data tumor tissue sample non small cell lung cancer patient","code":""},{"path":"/reference/lung5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example input data for SpaTopic — lung5","text":"","code":"lung5"},{"path":"/reference/lung5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example input data for SpaTopic — lung5","text":"## `lung5` data frame 100149 rows 4 columns: image Image ID X X coordinate cell Y Y coordinate cell type cell type","code":""},{"path":"/reference/lung5.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example input data for SpaTopic — lung5","text":"","code":""},{"path":[]},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"Prepare SpaTopic input one Seurat v5 object","code":""},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"","code":"Seurat5obj_to_SpaTopic(object, group.by, image = \"image1\")"},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"object Seurat v5 object group.character. name column contains celltype information Seurat object. image character. name image. Default \"image1\".","code":""},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"Return data frame input SpaTopic","code":""},{"path":[]},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"","code":"## nano.obj is a Seurat v5 object #dataset<-Seurat5obj_to_SpaTopic(object = nano.obj, # group.by = \"predicted.annotation.l1\",image = \"image1\") ## Expect output data(\"lung5\")"},{"path":"/reference/SpaTopic-Package.html","id":null,"dir":"Reference","previous_headings":"","what":"SpaTopic R package — SpaTopic-Package","title":"SpaTopic R package — SpaTopic-Package","text":"SpaTopic R package centered around SpaTopic algorithm infer spatial tissue architectures multiplexed images.","code":""},{"path":"/reference/SpaTopic-Package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SpaTopic R package — SpaTopic-Package","text":"package implements Collapsed Gibbs sampling algorithm infer topics, corresponding distinct tissue microenvironments across multiple tissue images. Without obtaining cell neighborhood info every single cell, SpaTopic runs much faster KNN-based methods large-scale images. main functions SpaTopic package Prepare input Seurat5obj_to_SpaTopic Model Infernece SpaTopic_inference Interpret outputs","code":""},{"path":"/reference/SpaTopic-Package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"SpaTopic R package — SpaTopic-Package","text":"Xiyu Peng pansypeng124@gmail.com","code":""},{"path":"/reference/SpaTopic_inference.html","id":null,"dir":"Reference","previous_headings":"","what":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"main function SpaTopic, implementing Collapsed Gibbs Sampling algorithm learn topics, referred different tissue microenvironments, across multiple multiplexed tissue images. function takes input cell labels coordinates tissue images returns inferred topic labels topic contents, distribution celltypes. function recovers spatial tissue architectures across images, well cell-cell interactions.","code":""},{"path":"/reference/SpaTopic_inference.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"","code":"SpaTopic_inference( tissue, ntopics, sigma = 50, region_radius = 400, kneigh = 5, npoints_selected = 1, ini_LDA = TRUE, ninit = 10, niter_init = 100, beta = 0.05, alpha = 0.01, trace = FALSE, seed = 123, thin = 20, burnin = 1000, niter = 200, display_progress = TRUE, do.parallel = FALSE, n.cores = 1, axis = \"2D\" )"},{"path":"/reference/SpaTopic_inference.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"tissue (Required). data frame list data frames. One image. row represent cell image ID, X, Y coordinates image, celltype, column names (image, X, Y, type), respectively. may add another column Y2 3D tissue image. ntopics (Required). Number topics. Topics obtained distribution sigma Default 50. lengthscale Nearest-neighbor Exponential Kernel. Sigma controls strength decay correlation distance kernel function. Please check paper information. Need adjusted based image resolution region_radius Default 400. radius grid square sampling region centers image. Need adjusted based resolution complexity images. kneigh Default 5. consider top 5 closest region centers cell. npoints_selected Default 1. Number points sampled grid square sampling region centers image. Used region_radius. ini_LDA Default TRUE. Use warm start strategy initialization choose best one continue. 0, simply just uses first initialization. ninit Default 10. Number initialization. retain initialization highest log likelihood (perplexity). niter_init Default 100. Warm start 100 iterations Gibbs sampling initialization. beta Default 0.05. hyperparameter control sparsity topic content (topic-celltype) matrix Beta. smaller value introduces sparse Beta. alpha Default 0.01. hyperparameter control sparsity document (region) content (region-topic) matrix Theta. application, keep small sparsity Theta. trace Default FALSE. Compute save log likelihood, Ndk, Nwk every posterior samples. Useful want use DIC select number topics, time consuming compute likelihood every posterior samples. seed Default 123. Random seed. thin Default 20. Key parameter Gibbs sampling. Collect posterior sample every 20 iterations. burnin Default 1000. Key parameter Gibbs sampling. Start collect posterior samples 1000 iterations. may increase number iterations burn-highly complex tissue images. niter Default 200. Key parameter Gibbs sampling. Number posterior samples collected inference. display_progress Default TRUE. Display progress bar. .parallel Default FALSE. Use parallel computing R package foreach. n.cores Default 1. Number cores used parallel computing. axis Default \"2D\". may switch \"3D\" 3D tissue images. However, model inference 3D tissue still test.","code":""},{"path":"/reference/SpaTopic_inference.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"Return gibbs.res-class object. list outputs Gibbs sampling.","code":""},{"path":[]},{"path":"/reference/SpaTopic_inference.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"","code":"## tissue is a data frame containing cellular information from one image or ## multiple data frames from multiple images. data(\"lung5\") ## NOT RUN, it takes about 90s #gibbs.res<-SpaTopic_inference(lung5, ntopics = 7, # sigma = 50, region_radius = 400) ## generate a fake image 2 and make an example for multiple images ## NOT RUN #lung6<-lung5 #lung6$image<-\"image2\" ## The image ID of two images should be different #gibbs.res<-SpaTopic_inference(list(A = lung5, B = lung6), # ntopics = 7, sigma = 50, region_radius = 400)"},{"path":"/reference/stratified_sampling_sf.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatially stratified random sample points from an image. — stratified_sampling_sf","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"Spatially stratified random sample points image R package sf","code":""},{"path":"/reference/stratified_sampling_sf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"","code":"stratified_sampling_sf( points, cellsize = c(600, 600), num_samples_per_stratum = 1 )"},{"path":"/reference/stratified_sampling_sf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"points data frame contains points image X, Y coordinates. cellsize vector length 2 contains size grid square. Default c(600,600). num_samples_per_stratum number point selected grid square. Default 1.","code":""},{"path":"/reference/stratified_sampling_sf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"Return vector contains index sampled points.","code":""},{"path":"/reference/stratified_sampling_sf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"","code":"data(\"lung5\") pt_idx<-stratified_sampling_sf(lung5, cellsize = c(600,600))"},{"path":"/news/index.html","id":"spatopic-10","dir":"Changelog","previous_headings":"","what":"SpaTopic 1.0","title":"SpaTopic 1.0","text":"Initial submission CRAN","code":""},{"path":"/news/index.html","id":"spatopic-099","dir":"Changelog","previous_headings":"","what":"SpaTopic 0.99","title":"SpaTopic 0.99","text":"Version development","code":""}] +[{"path":"/articles/Intro_SpaTopic.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"SpaTopic Basics","text":"Recent advancements multiplexed tissue imaging allow examination tissue microenvironments great detail. cutting-edge technologies offer invaluable insights cellular heterogeneity spatial architectures, playing crucial role decoding mechanisms treatment response disease progression. However, gaining deep understanding complex spatial patterns remains challenging. SpaTopic implements novel spatial topic model integrate cell type spatial information identify complex spatial tissue structures without human intervention. Collapsed Gibbs sampling algorithm used model inference. Contrasting computationally intensive K-nearest-neighbor-based cell neighborhood analysis approaches, SpaTopic scalable large-scale image datasets without extracting neighborhood information every single cell. SpaTopic can applied either single image across multiple images.","code":""},{"path":"/articles/Intro_SpaTopic.html","id":"simple-usage","dir":"Articles","previous_headings":"","what":"Simple Usage","title":"SpaTopic Basics","text":"required input SpaTopic data frame containing cells within single image list data frames multiple images. data frame consists four columns: image: Image ID X, Y: X, Y cell coordinate type: cell type information Run Gibbs Sampling Check output SpaTopic detailed usage SpaTopic interprete output SpaTopic, please check complete tutorial SpaTopic Home Page. also provide function prepare input Seurat v5 object.","code":"library(SpaTopic) ## The input can be a data frame or a list of data frames data(\"lung5\") head(lung5) #> image X Y type #> 1_1 image1 4215.889 158847.7 Dendritic #> 2_1 image1 6092.889 158834.7 Macrophage #> 3_1 image1 7214.889 158843.7 Neuroendocrine #> 4_1 image1 7418.889 158813.7 Macrophage #> 5_1 image1 7446.889 158845.7 Macrophage #> 6_1 image1 3254.889 158838.7 CD4 T ## Gibbs sampling gibbs.res<-SpaTopic_inference(lung5, ntopics = 7, sigma = 50, region_radius = 400) #> [1] \"number of cells per image:\" #> #> image1 #> 100149 #> [1] \"Initialization....\" #> [1] \"Numer of Initializations:\" #> [1] 10 #> [1] \"Min perplexity during initialization:\" #> [1] 11.63023 #> [1] \"number of region centers selected:\" #> [1] 971 #> [1] \"number of cells per region on average:\" #> [1] 103.1401 #> [1] \"Finish initialization. Start Gibbs sampling....\" #> [1] \"Output model perplexity..\" #> [1] 11.31563 str(gibbs.res) #> List of 8 #> $ Perplexity : num 11.3 #> $ Deviance : num 485960 #> $ loglikelihood: num -242980 #> $ Beta : num [1:38, 1:7] 0.03587 0.02539 0.00755 0.01858 0.02585 ... #> $ Theta : num [1:971, 1:7] 0.855601 0.000232 0.999269 0.99889 0.998725 ... #> $ Ndk : int [1:971, 1:7] 107 0 82 54 47 72 100 0 0 0 ... #> $ Nwk : int [1:38, 1:7] 390 276 82 202 281 505 697 522 29 58 ... #> $ Z.trace : int [1:100149, 1:7] 13 173 27 164 157 4 20 21 15 19 ..."},{"path":"/articles/Model_Selection.html","id":"simple-usage","dir":"Articles","previous_headings":"","what":"Simple Usage","title":"Model Selection","text":"","code":"library(SpaTopic)"},{"path":[]},{"path":[]},{"path":[]},{"path":"/articles/SpaTopic.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to SpaTopic","text":"Recent advancements multiplexed tissue imaging allow examination tissue microenvironments great detail. cutting-edge technologies offer invaluable insights cellular heterogeneity spatial architectures, playing crucial role decoding mechanisms treatment response disease progression. However, gaining deep understanding complex spatial patterns remains challenging. SpaTopic implements novel spatial topic model integrate cell type spatial information identify complex spatial tissue structures without human intervention. Collapsed Gibbs sampling algorithm used model inference. Contrasting computationally intensive K-nearest-neighbor-based cell neighborhood analysis approaches, SpaTopic scalable large-scale image datasets without extracting neighborhood information every single cell. SpaTopic can applied either single image across multiple images.","code":""},{"path":"/articles/SpaTopic.html","id":"set-up","dir":"Articles","previous_headings":"","what":"Set-up","title":"Introduction to SpaTopic","text":"use non-small cell lung cancer image illustrate use SpaTopic. data object can download , original public resources available nanostring website. images generated using 960-plex CoxMx RNA panel Nanostring CoxMx Spatial Molecular Imager platform. selected Lung5-1 sample annotated cells using Azimuth based human lung reference v1.0. Lung5-1 sample contains 38 annotated cell types. Since used healthy lung tissue reference, tumor cells labeled ’basal’ cells. informaion can found . can use Seurat function ImageDimPlot visualize distribution cell types image.","code":"## We use Seurat v5 package to visualize the results. ## If you still use Seurat v4, you will have the error library(Seurat, quietly = TRUE);packageVersion(\"Seurat\") #> [1] '4.9.9.9050' ## Load the Seurat object for the image load(\"~/github/SpaTopic_test/data/nanostring_example.rdata\") ## for large dataset options(future.globals.maxSize = 1e9) library(ggplot2) celltype.plot <-ImageDimPlot(nano.obj, fov = \"lung5.rep1\", axes = TRUE, cols = \"glasbey\",dark.background = T) celltype.plot+theme(legend.position = \"bottom\",legend.direction = \"vertical\")"},{"path":"/articles/SpaTopic.html","id":"topic-inference-on-a-single-image","dir":"Articles","previous_headings":"","what":"Topic Inference on a Single Image","title":"Introduction to SpaTopic","text":"Now, data ready. show example use SpaTopic identify tissue architectures multiplexed images.","code":""},{"path":"/articles/SpaTopic.html","id":"input","dir":"Articles","previous_headings":"Topic Inference on a Single Image","what":"Input","title":"Introduction to SpaTopic","text":"required input SpaTopic data frame containing cells within single image list data frames multiple images. data frame consists four columns: image ID, X, Y cell coordinates, cell type. may use function Seurat5obj_to_SpaTopic() extract input data typical Seurat v5 object. column name cell type information need provided via option group..","code":"library(SpaTopic);packageVersion(\"SpaTopic\") #> [1] '0.99' ## Prepare input from Seurat Object dataset<-Seurat5obj_to_SpaTopic(object = nano.obj, group.by = \"predicted.annotation.l1\",image = \"image1\") head(dataset) #> image X Y type #> 1_1 image1 4215.889 158847.7 Dendritic #> 2_1 image1 6092.889 158834.7 Macrophage #> 3_1 image1 7214.889 158843.7 Neuroendocrine #> 4_1 image1 7418.889 158813.7 Macrophage #> 5_1 image1 7446.889 158845.7 Macrophage #> 6_1 image1 3254.889 158838.7 CD4 T"},{"path":"/articles/SpaTopic.html","id":"gibbs-sampling","dir":"Articles","previous_headings":"Topic Inference on a Single Image","what":"Gibbs Sampling","title":"Introduction to SpaTopic","text":"step takes around 90 seconds regular laptop","code":"## Gibbs sampling for SpaTopic system.time(gibbs.res<-SpaTopic_inference(dataset, ntopics = 7, sigma = 50, region_radius = 400)) #> [1] \"number of cells per image:\" #> #> image1 #> 100149 #> [1] \"Initialization....\" #> [1] \"Numer of Initializations:\" #> [1] 10 #> Warning: package 'RANN' was built under R version 4.1.3 #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 7.2.1; sf_use_s2() is TRUE #> [1] \"Min perplexity during initialization:\" #> [1] 11.63023 #> [1] \"number of region centers selected:\" #> [1] 971 #> [1] \"number of cells per region on average:\" #> [1] 103.1401 #> [1] \"Finish initialization. Start Gibbs sampling....\" #> [1] \"Output model perplexity..\" #> [1] 11.31563 #> user system elapsed #> 82.82 0.28 83.22"},{"path":"/articles/SpaTopic.html","id":"topic-content-and-distribution","dir":"Articles","previous_headings":"Topic Inference on a Single Image","what":"Topic Content and Distribution","title":"Introduction to SpaTopic","text":"SpaTopic identify seven topics image. use heatmap show cell type composition within topic. assign cell topic highest posterior probability visualize distribution cell topics image.","code":"library(pheatmap) #> Warning: package 'pheatmap' was built under R version 4.1.3 m <- as.data.frame(gibbs.res$Beta) colnames(m)<-paste0(\"topic\",1:7) rownames(m)<-levels(as.factor(nano.obj$predicted.annotation.l1)) pheatmap::pheatmap(t(m)) prob<-as.matrix(gibbs.res$Z.trace) nano.obj$Topic<-as.factor(apply(prob,1,which.max)) library(ggplot2) ImageDimPlot(nano.obj, fov = \"lung5.rep1\", group.by = \"Topic\", axes = TRUE, dark.background = T,cols = \"glasbey\") + ggtitle(\"Topic\")"},{"path":"/articles/SpaTopic.html","id":"compare-to-buildnicheassay-in-seurat-v5","dir":"Articles","previous_headings":"Topic Inference on a Single Image","what":"Compare to BuildNicheAssay() in Seurat v5","title":"Introduction to SpaTopic","text":"compare SpaTopic function BuildNicheAssay() Seurat v5. took around 5 min laptop. also visualize distribution seven niches image.","code":"### NOT RUN!! We use the pre-computed result system.time(nano.obj <- BuildNicheAssay(object = nano.obj, \"lung5.rep1\", group.by = \"predicted.annotation.l1\", niches.k = 7, neighbors.k = 100)) ImageDimPlot(nano.obj, fov = \"lung5.rep1\", group.by = \"niches\", axes = TRUE, dark.background = T,cols = \"glasbey\") + ggtitle(\"Niches\")"},{"path":"/articles/SpaTopic.html","id":"topic-inference-on-multiple-images","dir":"Articles","previous_headings":"","what":"Topic Inference on Multiple Images","title":"Introduction to SpaTopic","text":"SpaTopic can identify common tissue patterns across multiple images. input list data frames. See example (run). Please check examples SpaTopic Home Page.","code":"## tissue1, tissue2 are data frames of two different images. gibbs.res<-SpaTopic_inference(list(A = tissue1, B = tissue2), ntopics = 7, sigma = 50, region_radius = 400)"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Xiyu Peng. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Peng X (2023). SpaTopic: Topic Inference Identify Tissue Architecture Multiplexed Images. R package version 1.0, https://github.com/xiyupeng/SpaTopic.","code":"@Manual{, title = {SpaTopic: Topic Inference to Identify Tissue Architecture in Multiplexed Images}, author = {Xiyu Peng}, year = {2023}, note = {R package version 1.0}, url = {https://github.com/xiyupeng/SpaTopic}, }"},{"path":"/index.html","id":"spatopic","dir":"","previous_headings":"","what":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"R package fast topic inference identify tissue architecture multiplexed images. implements spatial topic model identify immunologic topics across multiplexed images, given cell location cell type information input. Collapsed Gibbs Sampling algorithm used model inference. Compared KNN-based methods (KNN-kmeans, default Seurat v5 R package), SpaTopic runs much faster large-scale image dataset.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"SpaTopic can installed GitHub repository using devtools package.","code":"# install.packages(\"devtools\") devtools::install_github(\"xiyupeng/SpaTopic\")"},{"path":"/index.html","id":"dependency","dir":"","previous_headings":"","what":"Dependency","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"SpaTopic requires dependency following R packages: Rcpp C++ codes RcppProgress C++ codes RcppArmadillo C++ codes RANN fast KNN foreach parallel computing sf spatial analysis","code":""},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"required input SpaTopic data frame containing cells within single image list data frames multiple images. data frame consists four columns: image ID, X, Y cell coordinates, cell type information. detailed usage SpaTopic, please check tutorial.","code":"library(SpaTopic) ## The input can be a data frame or a list of data frames data(\"lung5\") head(lung5) # image X Y type #1_1 image1 4215.889 158847.7 Dendritic #2_1 image1 6092.889 158834.7 Macrophage #3_1 image1 7214.889 158843.7 Neuroendocrine #4_1 image1 7418.889 158813.7 Macrophage #5_1 image1 7446.889 158845.7 Macrophage #6_1 image1 3254.889 158838.7 CD4 T gibbs.res<-SpaTopic_inference(lung5, ntopics = 7, sigma = 50, region_radius = 400)"},{"path":"/index.html","id":"data","dir":"","previous_headings":"","what":"Data","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"example image used tutorial can downloaded . stored Seurat v5 object.","code":""},{"path":"/index.html","id":"output","dir":"","previous_headings":"","what":"Output","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"","code":"str(gibbs.res) #List of 8 # $ Perplexity : num 11.3 # $ Deviance : num 485960 # $ loglikelihood: num -242980 # $ Beta : num [1:38, 1:7] 0.03587 0.02539 0.00755 0.01858 0.02585 ... # $ Theta : num [1:971, 1:7] 0.855601 0.000232 0.999269 0.99889 0.998725 ... # $ Ndk : int [1:971, 1:7] 107 0 82 54 47 72 100 0 0 0 ... # $ Nwk : int [1:38, 1:7] 390 276 82 202 281 505 697 522 29 58 ... # $ Z.trace : int [1:100149, 1:7] 13 173 27 164 157 4 20 21 15 19 ..."},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"Coming soon……","code":""},{"path":"/index.html","id":"contact","dir":"","previous_headings":"","what":"Contact","title":"Topic Inference to Identify Tissue Architecture in Multiplexed Images","text":"problems, please contact: Xiyu Peng (pansypeng124@gmail.com, pengx1@mskcc.org)","code":""},{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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END TERMS CONDITIONS","code":""},{"path":"/reference/gibbs.res-class.html","id":null,"dir":"Reference","previous_headings":"","what":"A class of the output from SpaTopic — gibbs.res-class","title":"A class of the output from SpaTopic — gibbs.res-class","text":"Outputs function SpaTopic_inference. list contains following members: $Perplexity. perplexity training data. Let N total number cells across images. \\(Perplexity = exp(-loglikelihood/N)\\) $Deviance. \\(Deviance = -2loglikelihood\\). $loglikelihood. model log-likelihood. $loglike.trace. log-likelihood every collected posterior sample. NULL trace = FALSE. $Beta. Topic content matrix rows celltypes columns topics $Theta. Topic prevalent matrix rows regions columns topics $Ndk. Number cells per topic (col) per region (row). $Nwk. Number cells per topic (col) per celltype (row). $Z.trace. Number times cell assigned topic across posterior samples. can compute posterior distributions Z (topic assignment) individual cells. $doc.trace. Ndk every collected posterior sample. NULL trace = FALSE. $word.trace. Nwk every collected posterior sample. NULL trace = FALSE.","code":""},{"path":[]},{"path":"/reference/lung5.html","id":null,"dir":"Reference","previous_headings":"","what":"Example input data for SpaTopic — lung5","title":"Example input data for SpaTopic — lung5","text":"multiplexed image data tumor tissue sample non small cell lung cancer patient","code":""},{"path":"/reference/lung5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example input data for SpaTopic — lung5","text":"","code":"lung5"},{"path":"/reference/lung5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example input data for SpaTopic — lung5","text":"## `lung5` data frame 100149 rows 4 columns: image Image ID X X coordinate cell Y Y coordinate cell type cell type","code":""},{"path":"/reference/lung5.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example input data for SpaTopic — lung5","text":"","code":""},{"path":[]},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"Prepare SpaTopic input one Seurat v5 object","code":""},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"","code":"Seurat5obj_to_SpaTopic(object, group.by, image = \"image1\")"},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"object Seurat v5 object group.character. name column contains celltype information Seurat object. image character. name image. Default \"image1\".","code":""},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"Return data frame input SpaTopic","code":""},{"path":[]},{"path":"/reference/Seurat5obj_to_SpaTopic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Seurat v5 object as the input of SpaTopic — Seurat5obj_to_SpaTopic","text":"","code":"## nano.obj is a Seurat v5 object #dataset<-Seurat5obj_to_SpaTopic(object = nano.obj, # group.by = \"predicted.annotation.l1\",image = \"image1\") ## Expect output data(\"lung5\")"},{"path":"/reference/SpaTopic-Package.html","id":null,"dir":"Reference","previous_headings":"","what":"SpaTopic R package — SpaTopic-Package","title":"SpaTopic R package — SpaTopic-Package","text":"SpaTopic R package centered around SpaTopic algorithm infer spatial tissue architectures multiplexed images.","code":""},{"path":"/reference/SpaTopic-Package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SpaTopic R package — SpaTopic-Package","text":"package implements Collapsed Gibbs sampling algorithm infer topics, corresponding distinct tissue microenvironments across multiple tissue images. Without obtaining cell neighborhood info every single cell, SpaTopic runs much faster KNN-based methods large-scale images. main functions SpaTopic package Prepare input Seurat5obj_to_SpaTopic Model Infernece SpaTopic_inference Interpret outputs","code":""},{"path":"/reference/SpaTopic-Package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"SpaTopic R package — SpaTopic-Package","text":"Xiyu Peng pansypeng124@gmail.com","code":""},{"path":"/reference/SpaTopic_inference.html","id":null,"dir":"Reference","previous_headings":"","what":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"main function SpaTopic, implementing Collapsed Gibbs Sampling algorithm learn topics, referred different tissue microenvironments, across multiple multiplexed tissue images. function takes input cell labels coordinates tissue images returns inferred topic labels topic contents, distribution celltypes. function recovers spatial tissue architectures across images, well cell-cell interactions.","code":""},{"path":"/reference/SpaTopic_inference.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"","code":"SpaTopic_inference( tissue, ntopics, sigma = 50, region_radius = 400, kneigh = 5, npoints_selected = 1, ini_LDA = TRUE, ninit = 10, niter_init = 100, beta = 0.05, alpha = 0.01, trace = FALSE, seed = 123, thin = 20, burnin = 1000, niter = 200, display_progress = TRUE, do.parallel = FALSE, n.cores = 1, axis = \"2D\" )"},{"path":"/reference/SpaTopic_inference.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"tissue (Required). data frame list data frames. One image. row represent cell image ID, X, Y coordinates image, celltype, column names (image, X, Y, type), respectively. may add another column Y2 3D tissue image. ntopics (Required). Number topics. Topics obtained distribution sigma Default 50. lengthscale Nearest-neighbor Exponential Kernel. Sigma controls strength decay correlation distance kernel function. Please check paper information. Need adjusted based image resolution region_radius Default 400. radius grid square sampling region centers image. Need adjusted based resolution complexity images. kneigh Default 5. consider top 5 closest region centers cell. npoints_selected Default 1. Number points sampled grid square sampling region centers image. Used region_radius. ini_LDA Default TRUE. Use warm start strategy initialization choose best one continue. 0, simply just uses first initialization. ninit Default 10. Number initialization. retain initialization highest log likelihood (perplexity). niter_init Default 100. Warm start 100 iterations Gibbs sampling initialization. beta Default 0.05. hyperparameter control sparsity topic content (topic-celltype) matrix Beta. smaller value introduces sparse Beta. alpha Default 0.01. hyperparameter control sparsity document (region) content (region-topic) matrix Theta. application, keep small sparsity Theta. trace Default FALSE. Compute save log likelihood, Ndk, Nwk every posterior samples. Useful want use DIC select number topics, time consuming compute likelihood every posterior samples. seed Default 123. Random seed. thin Default 20. Key parameter Gibbs sampling. Collect posterior sample every 20 iterations. burnin Default 1000. Key parameter Gibbs sampling. Start collect posterior samples 1000 iterations. may increase number iterations burn-highly complex tissue images. niter Default 200. Key parameter Gibbs sampling. Number posterior samples collected inference. display_progress Default TRUE. Display progress bar. .parallel Default FALSE. Use parallel computing R package foreach. n.cores Default 1. Number cores used parallel computing. axis Default \"2D\". may switch \"3D\" 3D tissue images. However, model inference 3D tissue still test.","code":""},{"path":"/reference/SpaTopic_inference.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"Return gibbs.res-class object. list outputs Gibbs sampling.","code":""},{"path":[]},{"path":"/reference/SpaTopic_inference.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SpaTopic: fast topic inference to identify tissue architecture in multiplexed images — SpaTopic_inference","text":"","code":"## tissue is a data frame containing cellular information from one image or ## multiple data frames from multiple images. data(\"lung5\") ## NOT RUN, it takes about 90s #gibbs.res<-SpaTopic_inference(lung5, ntopics = 7, # sigma = 50, region_radius = 400) ## generate a fake image 2 and make an example for multiple images ## NOT RUN #lung6<-lung5 #lung6$image<-\"image2\" ## The image ID of two images should be different #gibbs.res<-SpaTopic_inference(list(A = lung5, B = lung6), # ntopics = 7, sigma = 50, region_radius = 400)"},{"path":"/reference/stratified_sampling_sf.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatially stratified random sample points from an image. — stratified_sampling_sf","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"Spatially stratified random sample points image R package sf","code":""},{"path":"/reference/stratified_sampling_sf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"","code":"stratified_sampling_sf( points, cellsize = c(600, 600), num_samples_per_stratum = 1 )"},{"path":"/reference/stratified_sampling_sf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"points data frame contains points image X, Y coordinates. cellsize vector length 2 contains size grid square. Default c(600,600). num_samples_per_stratum number point selected grid square. Default 1.","code":""},{"path":"/reference/stratified_sampling_sf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"Return vector contains index sampled points.","code":""},{"path":"/reference/stratified_sampling_sf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spatially stratified random sample points from an image. — stratified_sampling_sf","text":"","code":"data(\"lung5\") pt_idx<-stratified_sampling_sf(lung5, cellsize = c(600,600))"},{"path":"/news/index.html","id":"spatopic-10","dir":"Changelog","previous_headings":"","what":"SpaTopic 1.0","title":"SpaTopic 1.0","text":"Initial submission CRAN","code":""},{"path":"/news/index.html","id":"spatopic-099","dir":"Changelog","previous_headings":"","what":"SpaTopic 0.99","title":"SpaTopic 0.99","text":"Version development","code":""}] diff --git a/vignettes/articles/SpaTopic.Rmd b/vignettes/articles/SpaTopic.Rmd index da347cc..f86435f 100644 --- a/vignettes/articles/SpaTopic.Rmd +++ b/vignettes/articles/SpaTopic.Rmd @@ -2,9 +2,6 @@ title: "Introduction to SpaTopic" author: "Xiyu Peng" date: 2023-12-20 - -output: - rmarkdown::html_vignette --- ```{r, include = FALSE}