From 608e050ca60f25b672228c3ec7420e782c50c381 Mon Sep 17 00:00:00 2001 From: Zethson Date: Tue, 5 Sep 2023 10:10:51 +0000 Subject: [PATCH] deploy: 6e23eae9d573a35356a51b6c554b2730b6a43301 --- _sources/air_repertoire/clonotype.ipynb | 10 +++++++++- _sources/air_repertoire/ir_profiling.ipynb | 10 +++++++++- _sources/air_repertoire/multimodal_integration.ipynb | 10 +++++++++- _sources/air_repertoire/specificity.ipynb | 10 +++++++++- _sources/conditions/perturbation_modeling.ipynb | 2 +- _sources/introduction/raw_data_processing.md | 2 +- .../preprocessing_visualization/normalization.ipynb | 2 +- _static/pygments.css | 1 + air_repertoire/clonotype.html | 7 +++++++ air_repertoire/ir_profiling.html | 7 +++++++ air_repertoire/multimodal_integration.html | 7 +++++++ air_repertoire/specificity.html | 7 +++++++ conditions/perturbation_modeling.html | 2 +- introduction/raw_data_processing.html | 2 +- preprocessing_visualization/normalization.html | 6 +++--- searchindex.js | 2 +- 16 files changed, 74 insertions(+), 13 deletions(-) diff --git a/_sources/air_repertoire/clonotype.ipynb b/_sources/air_repertoire/clonotype.ipynb index 7915f367..aeece56e 100644 --- a/_sources/air_repertoire/clonotype.ipynb +++ b/_sources/air_repertoire/clonotype.ipynb @@ -47,7 +47,15 @@ "id": "3adf7e04", "metadata": {}, "source": [ - "Here, as well as in the pre-processing step, we will use the utilities from the *Scirpy* library to perform the analysis and locate the results in the *AnnData* object." + "Here, as well as in the pre-processing step, we will use the utilities from the *Scirpy* library to perform the analysis and locate the results in the *AnnData* object.\n", + "\n", + ":::{warning}\n", + "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n", + "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n", + "\n", + "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n", + "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n", + ":::" ] }, { diff --git a/_sources/air_repertoire/ir_profiling.ipynb b/_sources/air_repertoire/ir_profiling.ipynb index 813878db..8caf1046 100644 --- a/_sources/air_repertoire/ir_profiling.ipynb +++ b/_sources/air_repertoire/ir_profiling.ipynb @@ -199,7 +199,15 @@ "- **Scanpy**: general package for single cell analysis (https://github.com/theislab/scanpy, {cite}`wolf2018scanpy`)\n", "- **Scirpy**: scanpy extension for immune receptor analysis (https://github.com/scverse/scirpy, {cite}`sturm2020scirpy`)\n", "\n", - "Here, we only showcase IR analysis with Scirpy. However, there exist several tools with similar functionality such as immunarch(R, {cite}`immunomind2019`), scRepertoire (R, {cite}`borcherding2020screpertoire`), and dandelion (R, {cite}`stephenson2021single`), and Platypus (R, {cite}`yermanos2021platypus`) reviewed in {cite}`valkiers2022recent`. \n" + "Here, we only showcase IR analysis with Scirpy. However, there exist several tools with similar functionality such as immunarch(R, {cite}`immunomind2019`), scRepertoire (R, {cite}`borcherding2020screpertoire`), and dandelion (R, {cite}`stephenson2021single`), and Platypus (R, {cite}`yermanos2021platypus`) reviewed in {cite}`valkiers2022recent`. \n", + "\n", + ":::{warning}\n", + "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n", + "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n", + "\n", + "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n", + "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n", + ":::\n" ] }, { diff --git a/_sources/air_repertoire/multimodal_integration.ipynb b/_sources/air_repertoire/multimodal_integration.ipynb index ded9afe6..aaf87344 100644 --- a/_sources/air_repertoire/multimodal_integration.ipynb +++ b/_sources/air_repertoire/multimodal_integration.ipynb @@ -25,7 +25,15 @@ "source": [ "## Data Preparation\n", "\n", - "First, we will combine the GEX with AIR data. Note, that we can do this also at earlier stages of single cell analysis: e.g. we can fuse both modalities already before preprocessing to filter GEX doublets. When fusing GEX and AIR data, a left join is often performed. I.e., only cells with GEX are kept for analysis. Therefore, the order of fusing and filtering is not of great importance. However, it can be convenient to visualize the AIR information in an GEX UMAP in early stages. E.g. missing AIR information in clusters can help during cell type assignment." + "First, we will combine the GEX with AIR data. Note, that we can do this also at earlier stages of single cell analysis: e.g. we can fuse both modalities already before preprocessing to filter GEX doublets. When fusing GEX and AIR data, a left join is often performed. I.e., only cells with GEX are kept for analysis. Therefore, the order of fusing and filtering is not of great importance. However, it can be convenient to visualize the AIR information in an GEX UMAP in early stages. E.g. missing AIR information in clusters can help during cell type assignment.\n", + "\n", + ":::{warning}\n", + "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n", + "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n", + "\n", + "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n", + "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n", + ":::" ] }, { diff --git a/_sources/air_repertoire/specificity.ipynb b/_sources/air_repertoire/specificity.ipynb index f0a3627f..db76ca46 100644 --- a/_sources/air_repertoire/specificity.ipynb +++ b/_sources/air_repertoire/specificity.ipynb @@ -41,7 +41,15 @@ "\n", "CDR3β > V- and J-gene > CDR3α > MHC > cell type\n", "\n", - "We can assume that this importance is similar not only for prediction, but also for querying, clustering, and distance calculation." + "We can assume that this importance is similar not only for prediction, but also for querying, clustering, and distance calculation.\n", + "\n", + ":::{warning}\n", + "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n", + "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n", + "\n", + "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n", + "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n", + ":::" ] }, { diff --git a/_sources/conditions/perturbation_modeling.ipynb b/_sources/conditions/perturbation_modeling.ipynb index 009691e2..4c770c80 100644 --- a/_sources/conditions/perturbation_modeling.ipynb +++ b/_sources/conditions/perturbation_modeling.ipynb @@ -2355,7 +2355,7 @@ } ], "source": [ - "pt.pl.ms.barplot(mdata[\"rna\"])" + "pt.pl.ms.barplot(mdata[\"rna\"], guide_rna_column=\"guide_ID\")" ] }, { diff --git a/_sources/introduction/raw_data_processing.md b/_sources/introduction/raw_data_processing.md index 181cd7c9..f71c7217 100644 --- a/_sources/introduction/raw_data_processing.md +++ b/_sources/introduction/raw_data_processing.md @@ -499,7 +499,7 @@ When running `simpleaf index`, if a genome FASTA file (`-f`) and a gene annotati ```bash # simpleaf needs the environment variable ALEVIN_FRY_HOME to store configuration and data. # For example, the paths to the underlying programs it uses and the CB permit list -mkdir alevin_fry_home & export ALEVIN_FRY_HOME='alevin_fry_home' +mkdir alevin_fry_home && export ALEVIN_FRY_HOME='alevin_fry_home' # the simpleaf set-paths command finds the path to the required tools and write a configuration JSON file in the ALEVIN_FRY_HOME folder. simpleaf set-paths diff --git a/_sources/preprocessing_visualization/normalization.ipynb b/_sources/preprocessing_visualization/normalization.ipynb index 23b7c18b..331bb660 100644 --- a/_sources/preprocessing_visualization/normalization.ipynb +++ b/_sources/preprocessing_visualization/normalization.ipynb @@ -31,7 +31,7 @@ "\n", "The preprocessing step of \"normalization\" aims to adjust the raw counts in the dataset for variable sampling effects by scaling the observable variance to a specified range. Several normalization techniques are used in practice varying in complexity. They are mostly designed in such a way that subsequent analysis tasks and their underlying statistical methods are applicable. \n", "\n", - "A recent benchmark published by Ahlmann-Eltze and Huber{cite}`Ahlmann-Eltze2021.06.24.449781` compared 22 different transformations for single-cell data. The benchmark compared the performance of the different normalization techniques based on the cell graph overlap with the ground truth. We would like to highlight that a complete benchmark which also compares the impact of the normalization on a variety of different downstream analysis tasks is still outstanding. We advise analysts to choose the normalization carefully and always depend on the subsequent analysis task. \n", + "A recent benchmark published by Ahlmann-Eltze and Huber{cite}`Ahlmann-Eltze2023` compared 22 different transformations for single-cell data. The benchmark compared the performance of the different normalization techniques based on the cell graph overlap with the ground truth. We would like to highlight that a complete benchmark which also compares the impact of the normalization on a variety of different downstream analysis tasks is still outstanding. We advise analysts to choose the normalization carefully and always depend on the subsequent analysis task. \n", "\n", "This chapter will introduce the reader to three different normalization techniques, the shifted logarithm transformation, scran normalization and analytic approximation of Pearson residuals. The shifted logarithm works beneficial for stabilizing variance for subsequent dimensionality reduction and identification of differentially expressed genes. Scran was extensively tested and used for batch correction tasks and analytic Pearson residuals are well suited for selecting biologically variable genes and identification of rare cell types. \n", "\n", diff --git a/_static/pygments.css b/_static/pygments.css index 691aeb82..0d49244e 100644 --- a/_static/pygments.css +++ b/_static/pygments.css @@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: .highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */ .highlight .gd { color: #A00000 } /* Generic.Deleted */ .highlight .ge { font-style: italic } /* Generic.Emph */ +.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */ .highlight .gr { color: #FF0000 } /* Generic.Error */ .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */ .highlight .gi { color: #00A000 } /* Generic.Inserted */ diff --git a/air_repertoire/clonotype.html b/air_repertoire/clonotype.html index 33d92ea2..706d2ed5 100644 --- a/air_repertoire/clonotype.html +++ b/air_repertoire/clonotype.html @@ -910,6 +910,13 @@

42.2. Gene segment usage and spectratype

42.3. TCR data preparation#

Here, as well as in the pre-processing step, we will use the utilities from the Scirpy library to perform the analysis and locate the results in the AnnData object.

+
+

Warning

+

Scirpy changed the format of its datastructure +with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore.

+

See the scirpy release notes for more details about this change. +Until we update this chapter, please also refer to the official scirpy documentation.

+
import warnings
diff --git a/air_repertoire/ir_profiling.html b/air_repertoire/ir_profiling.html
index 1290eeba..67d86a83 100644
--- a/air_repertoire/ir_profiling.html
+++ b/air_repertoire/ir_profiling.html
@@ -1045,6 +1045,13 @@ 
41.7. Load data
Scirpy: scanpy extension for immune receptor analysis (https://github.com/scverse/scirpy, [Sturm et al., 2020])

 
 
Here, we only showcase IR analysis with Scirpy. However, there exist several tools with similar functionality such as immunarch(R, [ImmunoMind Team, 2019]), scRepertoire (R, [Borcherding et al., 2020]), and dandelion (R, [Stephenson et al., 2021]), and Platypus (R, [Yermanos et al., 2021]) reviewed in [Valkiers et al., 2022].

+

+
Warning

+
Scirpy changed the format of its datastructure
+with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore.

+
See the scirpy release notes for more details about this change.
+Until we update this chapter, please also refer to the official scirpy documentation.

+

 

 

 
import warnings
diff --git a/air_repertoire/multimodal_integration.html b/air_repertoire/multimodal_integration.html
index a1307a47..745e853a 100644
--- a/air_repertoire/multimodal_integration.html
+++ b/air_repertoire/multimodal_integration.html
@@ -974,6 +974,13 @@ 
47.1. Motivation
 
47.2. Data Preparation#

 
First, we will combine the GEX with AIR data. Note, that we can do this also at earlier stages of single cell analysis: e.g. we can fuse both modalities already before preprocessing to filter GEX doublets. When fusing GEX and AIR data, a left join is often performed. I.e., only cells with GEX are kept for analysis. Therefore, the order of fusing and filtering is not of great importance. However, it can be convenient to visualize the AIR information in an GEX UMAP in early stages. E.g. missing AIR information in clusters can help during cell type assignment.

+

+
Warning

+
Scirpy changed the format of its datastructure
+with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore.

+
See the scirpy release notes for more details about this change.
+Until we update this chapter, please also refer to the official scirpy documentation.

+

 

 

 
import warnings
diff --git a/air_repertoire/specificity.html b/air_repertoire/specificity.html
index 32697490..1d94693a 100644
--- a/air_repertoire/specificity.html
+++ b/air_repertoire/specificity.html
@@ -947,6 +947,13 @@ 
46.2. TCR specificity analysisGlanville et al., 2017, Davis and Bjorkman, 1988, Rudolph et al., 2006] showed that the TCR is in close contact with the pMHC at the CDR3, specifically, of the β-chain. This is in line with the regions of high variability of the TCR sequence. In [Springer et al., 2021] by Springer et al., the importance of different information (elements of the TCR and MHC type) was reported for training a sequence-based classifier, which are mostly in line with these findings:

 
CDR3β > V- and J-gene > CDR3α > MHC > cell type

 
We can assume that this importance is similar not only for prediction, but also for querying, clustering, and distance calculation.

+

+
Warning

+
Scirpy changed the format of its datastructure
+with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore.

+
See the scirpy release notes for more details about this change.
+Until we update this chapter, please also refer to the official scirpy documentation.

+

 

 

 
import warnings
diff --git a/conditions/perturbation_modeling.html b/conditions/perturbation_modeling.html
index aceaa55d..7d33ea59 100644
--- a/conditions/perturbation_modeling.html
+++ b/conditions/perturbation_modeling.html
@@ -2060,7 +2060,7 @@ 
19.4.4. Identifying cells with no detect
 
We can now plot the class distributions for all 111 gRNAs.

 

 

-
pt.pl.ms.barplot(mdata["rna"])
+
pt.pl.ms.barplot(mdata["rna"], guide_rna_column="guide_ID")
 

 

 

diff --git a/introduction/raw_data_processing.html b/introduction/raw_data_processing.html
index 4c6e1a5b..253310ed 100644
--- a/introduction/raw_data_processing.html
+++ b/introduction/raw_data_processing.html
@@ -1417,7 +1417,7 @@ 
3.7. Brief discussionsimpleaf index, if a genome FASTA file (-f) and a gene annotation GTF file(-g) are provided, it will gererate a splici reference and index it; if only a transcriptome FASTA file is provided (--refseq), it will directly index it. Currently, we recommend the splici index.

 
# simpleaf needs the environment variable ALEVIN_FRY_HOME to store configuration and data.
 # For example, the paths to the underlying programs it uses and the CB permit list
-mkdir alevin_fry_home & export ALEVIN_FRY_HOME='alevin_fry_home'
+mkdir alevin_fry_home && export ALEVIN_FRY_HOME='alevin_fry_home'
 
 # the simpleaf set-paths command finds the path to the required tools and write a configuration JSON file in the ALEVIN_FRY_HOME folder.
 simpleaf set-paths
diff --git a/preprocessing_visualization/normalization.html b/preprocessing_visualization/normalization.html
index 5aaff028..35b772d7 100644
--- a/preprocessing_visualization/normalization.html
+++ b/preprocessing_visualization/normalization.html
@@ -793,7 +793,7 @@ 
7.1. MotivationA theoretically and empirically established model for UMI data is the Gamma-Poisson distribution which implies a quadratic mean-variance relation with \(Var[Y] = \mu + \alpha \mu^2\) with mean \(\mu\) and overdispersion \(\alpha\). For \(\alpha=0\) this is the Poisson distribution and \(\alpha\) describes the additional variance on top of the Poisson.

 

 
The preprocessing step of “normalization” aims to adjust the raw counts in the dataset for variable sampling effects by scaling the observable variance to a specified range. Several normalization techniques are used in practice varying in complexity. They are mostly designed in such a way that subsequent analysis tasks and their underlying statistical methods are applicable.

-
A recent benchmark published by Ahlmann-Eltze and Huber[Ahlmann-Eltze and Huber, 2022] compared 22 different transformations for single-cell data. The benchmark compared the performance of the different normalization techniques based on the cell graph overlap with the ground truth. We would like to highlight that a complete benchmark which also compares the impact of the normalization on a variety of different downstream analysis tasks is still outstanding. We advise analysts to choose the normalization carefully and always depend on the subsequent analysis task.

+
A recent benchmark published by Ahlmann-Eltze and Huber[Ahlmann-Eltze and Huber, 2023] compared 22 different transformations for single-cell data. The benchmark compared the performance of the different normalization techniques based on the cell graph overlap with the ground truth. We would like to highlight that a complete benchmark which also compares the impact of the normalization on a variety of different downstream analysis tasks is still outstanding. We advise analysts to choose the normalization carefully and always depend on the subsequent analysis task.

 
This chapter will introduce the reader to three different normalization techniques, the shifted logarithm transformation, scran normalization and analytic approximation of Pearson residuals. The shifted logarithm works beneficial for stabilizing variance for subsequent dimensionality reduction and identification of differentially expressed genes. Scran was extensively tested and used for batch correction tasks and analytic Pearson residuals are well suited for selecting biologically variable genes and identification of rare cell types.

 
We first import all required Python packages and load the dataset for which we filtered low quality cells, removed ambient RNA and scored doublets.

 

@@ -1033,8 +1033,8 @@ 
7.3. Analytic Pearson residuals7.4. References#

 

 

-
normAEH22

-
Constantin Ahlmann-Eltze and Wolfgang Huber. Comparison of transformations for single-cell rna-seq data. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/11/12/2021.06.24.449781, doi:10.1101/2021.06.24.449781.

+
normAEH23

+
Constantin Ahlmann-Eltze and Wolfgang Huber. Comparison of transformations for single-cell RNA-seq data. Nature Methods, 2023. URL: https://www.nature.com/articles/s41592-023-01814-1, arXiv:https://www.nature.com/articles/s41592-023-01814-1.pdf, doi:10.1038/s41592-023-01814-1.

 

 
normDor38

 
RA Dorfman. A note on the! d-method for finding variance formulae. Biometric Bulletin, 1938.

diff --git a/searchindex.js b/searchindex.js
index 208afe3c..d7eda93d 100644
--- a/searchindex.js
+++ b/searchindex.js
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md_bcr_embed:3,cmd_beniss:3,cmd_conga:3,cmd_conga_pp:3,cmd_ergo:4,cmd_tcrmatch:4,cmd_tessa:3,cmp:5,cmv:4,cner_1:8,cnt:2,co:[0,4,16,17,25,26,38,47],co_occurr:40,coach:9,coars:[5,6,30,33],coarser:[5,6],coarsest:23,coat:24,cobll1:[5,12],cobo:17,cocain:16,cocktail:2,coda:13,code:[1,3,4,5,7,9,10,11,13,14,18,22,23,24,28,38,48,50],codebas:48,codetool:7,codetools_0:[8,25,27,28],codex:39,codi:23,codon:[1,2,24],coef:14,coeffici:[8,13,14,16,32],cog:5,coher:48,cohort:7,coi:17,coin:[23,49],coisb:[25,49],col4a1:38,col4a4:[5,12],col:[4,14],col_attribut:26,col_index:[45,46,47],col_linkag:4,colab:3,coldata:[7,8,14,17,34],cole:[5,7,23,43,48],coleman:[37,41],coli:26,colin:[2,23],collabor:[15,30],collado:26,collaps:23,collat:[7,23],colleagu:[24,48],collect:[2,3,4,13,17,19,23,24,26,30,36,41,50],collection_dai:2,collier:24,collin:[2,5,7,23,49],collis:23,colnam:[7,8,14,15,25,34],colom:[7,28],color:[1,2,3,4,5,6,7,8,11,13,14,15,16,17,19,23,25,26,27,28,31,32,34,36,37,38,41,42,43,44,45,48,49,50],color_map:[33,36,49],color_schem:1,colorama:[1,7,15,25],colorbar_posit:36,colorbar_shap:36,colormap:[5,7,25,36,42],colorpalett:8,colorspac:7,colorspace_2:[8,25,27,28],colour:[7,8,25],colourmap:38,colq:8,colsum:14,column:[1,2,3,4,5,7,8,11,13,14,15,16,17,19,23,25,26,27,28,32,34,36,41,47,48,50],column_cdr3a:3,column_cdr3b:3,column_names_gp:8,columnam:10,com:[1,2,3,4,5,6,7,8,9,11,13,14,15,16,17,19,23,24,25,26,27,28,30,31,32,33,34,36,37,38,39,40,49],combat:7,combin:[1,2,3,4,5,7,9,11,14,15,16,17,23,24,27,28,30,31,36,37,40,47],combinatori:[2,16,48],combiz:[19,29],come:[1,5,7,14,15,16,19,23,24,25,36,48],comet:3,comet_workspac:3,comfort:16,comma:[2,23],command:[2,3,4,23,27,36],comment:3,commerci:[9,23,24,36,39],commod:1,common:[1,2,4,5,6,7,8,9,11,13,14,15,16,17,18,22,23,24,25,27,28,30,34,36,39,48,49,50],common_idx:[27,28],commoni:13,commonli:[3,4,13,14,15,16,17,18,19,23,24,25,33,44],commun:[0,1,2,3,5,6,9,13,14,15,16,17,18,19,23,24,26,29,30,31,34,36,37,39,41,46,48,49,50],comorbid:17,compact:[1,9],compait82:13,compani:24,compar:[1,2,3,4,5,6,7,9,11,13,14,15,16,17,19,22,23,24,25,26,28,31,33,34,36,37,38,41,47,48,49,50],comparison:[4,5,6,8,11,13,14,17,18,23,24,25,26,31,33,36,38,48],comparisson:4,compart:[1,17,26],compartment:24,compartmentalis:24,compat:[1,2,5,13,15,16,23,28],compbah19:13,compbst:13,compbuttneromul:13,compclo:13,compdht:13,compet:1,competiiton:27,competit:[1,7,15,27],compfrm:13,compgmpge17:13,comphbr:13,compil:[5,26],compiler_4:[8,15,25,27,28],complement:[2,23],complementari:[2,8,22,24,25],complet:[2,3,4,5,7,13,16,17,19,22,24,27,29,30,33,48],complex:[1,2,4,9,13,14,16,18,19,23,24,25,30,31,33,36,41,42,48],complexheatmap:8,complexheatmap_2:[8,25],compliant:4,complic:[1,5,23],complimentari:25,complp20:13,complrm17:13,compmgc21:13,compocm21:13,compon:[2,5,6,9,14,16,17,19,23,25,31,33,34,39,41,44,48,49],compos:[1,7,16,23,24,25,36],composit:[1,3,7,17,24,34,36,40,41,46,48],composition:13,compound:[16,48],comprehens:[7,9,14,15,17,22,23,24,25,26,28],compress:[3,7,13,19,23,47],compris:[16,19,22,23],compro10:13,compromis:[15,23],compssh:13,comput:[0,1,2,3,4,5,6,7,8,10,11,13,15,16,17,18,23,24,25,26,27,28,30,31,32,33,34,36,37,39,40,41,44,49,50],computation:[1,4,11,15,19,23,24,26,31,34,38,48],compute_empirical_indel_prior:48,compute_expansion_pvalu:48,compute_expected_per_cell_typ:36,compute_motif:1,compute_pal_motif:3,compute_percent_indel:48,compute_percent_uncut:48,computesumfactor:33,compzjl:13,conatin:31,concat:[5,7,15,27,28,38,47],concat_batch:27,concaten:[5,7,14,15,16,19,27,28,47],concatent:14,concentr:[13,25,47],concept:[1,9,17,19,23,24,25,33,36,37,48],conceptu:[16,23,25,26],concern:[15,16,23,24,25,30,48],conchola:5,concis:23,conclud:[15,23,49],conclus:[14,18,24,50],concord:[15,16,25],cond:[5,40],conda21:[42,43],conda:[1,3,4,7,8,15,17,19,23,25,27,28],conda_prefix:3,conda_subdir:23,condit:[1,2,4,7,14,15,16,17,24,25,27,28,29,30,40,45,48],condition_col:25,condition_color:[13,15,25],condition_ind:15,condition_kei:14,condition_label:16,conditioncontrol:13,conditionsalmonella:13,conditiont:13,conduct:[3,4,13,14,15,16,17,19,24,34,36,48],conf_dict:13,conf_scor:5,confer:[7,11,23,27,28,34,47,48],confid:[2,4,5,13,16,23,24,25,26,36,48,50],config:[16,23],configur:[2,23],configuration_valid:[28,36],configure_dataset:27,confimr:26,confin:1,confirm:[7,17,26,40,43],confluenc:48,conform:1,confound:[7,13,14,16,18,33,34],confront:[14,16],confus:48,conga_clone_fil:3,conga_gex:3,conga_results_summari:3,conga_tcr:3,conjug:[2,47],conjunct:25,conlon:7,connect:[2,3,4,5,6,7,13,15,19,23,25,26,27,28,36,37,38,40,48,49,50],connectionplot:3,connectom:25,connor:1,conor:7,conquer:48,conrad:[11,17,24,26,40],consecut:49,consensu:[2,5,14,23,41],consensus_count:[1,2],consequ:[1,8,15,23,25,38,48,49,50],conserv:[1,4,7,13,23,28],consid:[1,3,4,5,7,8,11,13,14,15,17,19,23,26,30,34,36,37,47,48,49,50],consider:[1,2,3,11,14,23,26,48],consist:[1,2,5,6,7,13,15,17,18,19,23,24,25,26,27,37,38,40,41,47,48,50],consol:[1,11,15,17],consortia:26,consortium:[7,18],constant:[1,4,13,18,23,32,36,50],constant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m:24,expda:24,expdsz:24,expect:[1,3,4,5,6,11,13,14,15,16,17,23,24,25,28,29,33,36,37,38,39,40,46,47,48,50],expected_dict:36,expected_orient:23,expens:[1,4,11,17,24,39],experi:[0,1,3,5,6,7,11,13,14,15,16,17,18,19,23,24,27,28,30,32,33,34,36,46,47,48,50],experiment:[1,3,4,7,13,14,16,17,19,24,25,26,28,33,39,44,48,49,50],experimentalist:48,expert:[5,7,27,30],expflu22:24,expgch:24,exphhs87:24,exphtt17:24,expir:2,expiry_1:8,expizj:24,expjhyf72:24,expjopa16:24,expkgn:24,expkma:24,expkvaharautiok:24,explain:[1,3,4,8,9,13,14,15,19,22,23,24,26,36,49],explan:[2,3,4,15,22,23,40],explanatori:1,explicit:[27,38],explicitli:[7,11,13,14,19,23,33],explmbw20:24,explmph18:24,exploit:1,explor:[1,3,5,8,9,13,14,26,33,43],exploratori:[8,14,19,24],explore_3:[25,28],expm1:3,expmb:24,expmlm:24,exponenti:48,export_posterior:36,expos:23,exposur:4,exppwt:24,expr:[7,11,15],expr_prod:25,expr_prop:25,expres:14,express:[2,3,6,7,8,9,13,16,17,18,19,23,24,25,26,27,28,30,31,32,33,34,39,41,42,43,44,45,46,47,48,49,50],expressed_gen:25,expressed_ligand:25,expressed_receptor:25,expression_level:38,expression_mtx_fnam:26,expressionset:17,expsaec:24,expsnl:24,exptyg:24,expwhz:24,expzll:24,expztb:24,expzvp:24,ext:27,extend:[3,5,13,19,23,27,29,36,37,48],extend_txom:23,extens:[2,3,5,7,11,13,16,17,19,22,23,24,25,30,33,36,48,50],extent:[5,7,13,19],extern:[1,16,23,25,26,28,30,48],extra:[2,7,23,25,34],extra_chain:2,extract:[1,2,3,4,8,9,10,11,15,16,17,19,23,24,25,28,36,37,41,48,50],extract_edge_weight:1,extrapol:16,extrem:[5,11,19,48,49,50],extremli:49,ey:48,eyal:36,eytan:50,f1000research:[2,5,6,7,11,14,15],f:[1,2,3,4,5,6,7,11,13,14,15,16,19,23,25,26,27,28,32,33,34,36,37,38,46,47,48,49,50],f_j:38,fa:[23,27],fabian:[2,3,5,6,7,9,11,14,16,17,19,22,23,25,27,28,29,34,36,37,40,41,47,49,50],fabiana:8,fabio:[2,24],fac:[2,5,13,17,24],face:[30,38],facecolor:[6,19,25,26,31,32,33,34,36,37,38,40,41],facetgrid:[13,47],facil:[7,24],facilit:[1,16,19,48],fact:[1,7,13,15,16,23,24,25,31,38,46,47,50],factor:[4,7,8,9,11,13,14,15,16,17,18,19,23,24,25,26,33,48],factor_nam:36,factor_names_:36,factoris:36,fail:[1,5,14,16,17,23,27,28,47,49,50],failur:[5,16,18,23,50],fairli:25,faith:16,faithfulli:50,faiz:5,falai:[36,38],falco:23,falk:[5,7,49],fall:[1,5,13,16,23,26,27,28,36,38,39,47],fals:[1,2,3,4,5,6,7,8,11,13,14,15,16,17,19,24,25,26,27,28,31,32,33,34,36,37,38,42,45,47,48,50],fam138a:[19,36],famili:[7,8,13,14,23],familiar:[13,15,30],famou:[16,48],fan:[5,7,15,23,43,49,50],fanci:[19,29],fanelli:38,fang:[9,26,36,38,48],fangm:9,fanni:7,fansi:7,fansi_1:[8,25,27,28],far:[1,5,11,23,24,30,32,40,45,50],faraz:23,farber:5,fardeep:17,farouni:23,farrar:23,farrel:7,farver_2:[8,27],fashion:[3,34,36],fasouli:5,fast:[5,6,7,15,16,19,23,24,29,33,34,39,41,43,48],fasta:23,fastdummies_1:27,fasten:1,faster:[1,2,8,11,13,15,16,18,23,38,48],fastest:7,fastmap:7,fastmap_1:[8,25,27,28],fastmnn:7,fastq:[18,23],fastq_dir:23,fastqc:23,fate:[3,47,48,49,50],fatemeh:[14,23],fault:16,faust:17,favor:[14,23,24],favour:[7,33],faw:3,fb:48,fbxo31:41,fc:[2,14],fcer1a:[5,12],fcer1g:[5,12],fcer2:27,fceria:12,fcgr3a:[5,12,15,16,25,27],fcgr3a_monocyt:14,fcn1:[5,12],fcrl1:[5,12],fct:10,fd:26,fdim:48,fdr:[13,14,15],fdrtool_1:25,fearghal:50,feasibl:4,feat:27,feather:26,feather_0:8,featu:10,featur:[1,3,4,5,10,13,16,17,18,19,23,24,25,26,27,28,30,31,34,36,37,38,39,41,44,46,47,48,49],feature_biotyp:36,feature_id:36,feature_is_filt:36,feature_nam:[13,36],feature_refer:36,feature_typ:[7,10,11,19,26,27,28,34,36,37,42,45,46,47],featureplot:10,feb:[13,14,17,19,22,24,25,30,37,40,41,47],februari:[5,7,9,23,24,36,41],fed:27,federico:22,fedrigo:24,feedback:30,feel:[5,11,16,30,38],feet:24,fei:[15,16,36],feiyang:17,fel81:48,felicia:47,felix:[3,4,14,17,23,26],felsenstein:48,femal:2,feng:[24,26,37,39,41,48],fengcui:26,fennel:24,ferequ:23,ferguson:[2,24],fernand:[13,47,49],fernandez:27,fernando:[1,2,48],ferri:23,fertil:[1,48],fetal:[38,49],fetch:[11,16,19,23],few:[1,5,7,11,13,14,15,16,19,23,26,28,31,34,36,37,38,39,41,42,43,48,49,50],fewer:[6,7,14,34,37,48],fgene:[13,23,31],fgsae:15,fgsea:15,fhl2:15,fibroblast:[5,14,36],ficht:48,field:[0,2,5,7,8,14,15,17,22,24,25,29,30,37,38,48,49,50],fier:24,fifth:[7,11,27,28,34,47,49],fig:[4,5,7,11,13,14,23,25,26,33,36,38,48],fig_kw:[1,3],fight:[2,5],figr_0:8,figshar:[2,3,5,11,15,25,31,32,33,34,36,38,49],figsiz:[1,3,4,5,11,13,15,16,25,26,33,36,37,38,40,45,48],figueroa:16,figur:[4,5,9,11,13,16,23,24,25,36,42,48],figure_s:25,filament:24,filbi:49,file:[1,2,3,4,5,8,10,11,14,15,18,19,23,24,25,26,27,28,31,32,33,34,36,38,47,48,49],file_nam:49,file_path:50,filelock_1:8,filenam:[2,3,5,19,23,31,32,33,34,36,38,49],files_list:47,fill:[4,11,19],fillna:4,film:2,filt:8,filter:[3,4,5,7,8,14,16,17,19,23,25,33,36,37,38,43,44,45,46,47,49,50],filter_and_norm:50,filter_cel:[14,19,25,47],filter_gen:[7,14,19,25,34,36,37,49],filter_intbcs_final_lineag:48,filter_lambda:25,filter_ob:[11,19],filter_rank_genes_group:5,filter_var:[11,19,44],filterbi:25,filterbyexpr:[14,15],filtered_:47,filtered_contig_annot:2,filtered_contig_annotations_csvfil:3,f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Acknowledgements","42. Clonotype analysis","41. Immune Receptor Profiling","47. Integrating AIR and transcriptomics","46. Specificity analysis","11. Annotation","10. Clustering","12. Data integration","25. Gene regulatory networks using chromatin accessibility","23. Single-cell ATAC sequencing","Conversion of muon objects to Seurat objects to use in downstream analysis","24. Quality Control","Markers for cluster annotations acros modalities","17. Compositional analysis","16. Differential gene expression analysis","18. Gene set enrichment and pathway analysis","19. Perturbation modeling","22. Bulk deconvolution","53. Glossary","4. Analysis frameworks and tools","Data infrastructure","5. Interoperability","1. Prior art","3. Raw data processing","2. Single-cell RNA sequencing","21. Cell-cell communication","20. Gene regulatory networks","49. Advanced integration","48. Paired integration","51. Outlook","Single-cell best practices","9. Dimensionality Reduction","8. Feature selection","7. Normalization","6. Quality Control","50. Reproducibility","30. Spatial deconvolution","28. Spatial domains","31. Imputation","26. Single-cell data resolved in space","27. Neighborhood analysis","29. Spatially variable genes","40. Annotation","39. Batch correction","38. Dimensionality Reduction","37. Doublet detection","36. Normalization","35. Quality control","15. Lineage tracing","13. Pseudotemporal ordering","14. RNA 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,34,37,47,48],certainli:23,certainti:5,certifi:1,cesaro:25,cesgratitmivvtdldyw:2,cetin:24,cf:37,cffi:[1,7,15,25],cg22:27,cg:36,cgata:48,cgccg:48,cggag:48,cggaggatat:48,cgttaacaggtgtcca:7,cgtwdsslsawvf:2,cha:25,chad:24,chaffin:7,chaichoompu:[7,28],chain:[1,2,3,4,15,17,18,23,24,48,49],chain_pair:[1,2,4],chain_qc:2,chain_statu:1,chaisson:23,chakraborti:42,challeng:[5,7,11,13,14,16,17,24,25,28,29,30,33,34,39,46,47,48,49,50],chamber:24,chan:[5,26,48],chanc:23,chang:[1,2,3,4,5,6,7,8,9,13,14,15,16,17,19,23,24,25,26,28,29,30,33,34,36,37,38,39,41,42,45,46,48,49,50],changeo:1,changeo_clone_id:1,changeo_clone_id_s:1,changeo_clone_id_size_max_50:1,channel:19,chantip:[17,26],chao:[23,26],chaohan:31,chapman:40,chapter:[0,1,2,3,4,5,6,7,8,9,11,13,14,15,19,22,23,24,25,26,28,29,30,33,34,41,42,43,44,46,47,48],charact:[10,48],character:[1,4,9,13,14,15,16,19,23,24,25,39,48,49],character_matrix:48,character_matrix_filt:48,characterist:[2,3,11,13,23,24,25,37,49,50],chardet:1,chardon:48,charg:[2,4,26],chariti:[14,15],charl:[5,38,48],charlott:[4,5,14,17,19,22,23,24,25,26,27,28,50],charset_norm:1,chart:7,chase:[3,4,5,7,48,49],chattopadhyai:[9,16,46,47,49],chatzidimitri:23,chaudhuri:17,chauv:23,chauvet:48,chavez:5,chazarra:7,cheaper:[18,24],cheapest:24,check:[1,2,3,4,5,7,8,10,11,13,14,15,23,25,26,27,37,38,42,43,50],check_graph:27,check_random_st:15,checkmate_2:25,checkpoint:[16,27,47],checksum:19,chee:37,cheimona:1,chelsea:23,chemic:[3,16,18,24,48],chemistri:[23,24,48],chen2022:37,chen:[3,5,7,11,15,16,23,24,25,26,27,28,31,34,36,38,47,48,49,50],chenchen:26,chenfei:25,cheng:[7,13,17,26,36,37,38,48],chengzh:5,chenl:[7,48],chenqu:49,cheol:1,chernyshev:1,chevrier:7,chevron:23,chew:8,chex:7,chichelnitskii:[5,7,49],chih:17,child:13,chimenti:14,chiou:7,chip:[2,23,24],chisq:14,chisto:5,chlo:[7,24,48,49],chloe:[7,11,14,27,28,34,47],cho:[2,26,48],choi:[25,48],choic:[7,9,13,15,16,19,23,24,25,31,48,49],choli:48,chomatin:9,chong:5,choos:[1,3,4,5,11,23,25,29,32,33,37,48],chose:[11,14],chosen:[1,2,4,5,13,15,16,32,34,47,49,50],choudhari:27,choudhri:9,chow:48,chozen_isoform:38,chr10:11,chr11:11,chr12:11,chr13:11,chr14:11,chr15:11,chr16:11,chr17:11,chr18:11,chr19:11,chr1:[10,11],chr20:11,chr21:11,chr22:11,chr2:11,chr3:11,chr4:11,chr5:11,chr6:11,chr7:11,chr8:11,chr9:11,chr:[8,25],chri:[16,23,24,30,48],christiaen:[8,9],christian:[5,13,15,17,24,25,26,40],christiansen:[16,23],christin:[5,7,17,24,26,49],christina:[3,4,19,23,37,40,41],christlei:4,christof:[17,24,26],christoph:[3,5,7,9,10,11,13,15,16,17,22,23,24,26,27,28,34,36,46,47,48,49],chrm:11,chrom_assai:10,chromain:9,chromatin:[7,9,11,18,19,26,28,30,39,47,49],chromatinassai:10,chromatograph:2,chromatographi:24,chromatographu:2,chromium:[9,23,24,48],chromiumv3:23,chromosom:[8,10,11,23,24],chromvar:8,chromvar_1:8,chronist:4,chrx:11,chry:11,chrysa:2,chuan:[5,7,49],chuanchuan:4,chuang:[15,36,38],chun:[15,23],chuner:48,chung:[5,7,49],chunk:23,chunlin:3,chunquan:26,chunyu:15,church:48,ci:[8,9,26],ciara:5,cibersortx:17,ciccon:7,cicek:11,cigar:[23,48],circl:[1,13],circle_diamet:36,circlize_0:[8,25],circular:36,circumv:[14,50],ciro:[5,7,42,43,44,45,46,47,49,50],cisassign:8,ciscorr:8,cistarget:26,cistop:9,cistopic_0:8,cistopic_bkp_path:8,cistopicobject:8,cite:[4,13,16,19,25,27,30,36,37,46,47,49],cite_query_batch:27,cite_reference_batch:27,citeseq_pp:[44,45,46,47],cittaro:13,ciup:1,ciyu:16,ckch21:1,cl_annot:5,clade:48,clade_color:48,cladogram:48,clair:[4,48,49],clang:[7,15],clark:[5,7,15,49],clash:42,class_7:25,classic:[5,14,36,48,49,50],classif:[5,13,15,34,49],classifi:[4,13,16,18,23,25,42,45,48],clatworthi:5,claudia:[14,16,17,26],clean:[14,28],cleanli:48,clear:[1,3,5,7,13,14,23,34,36,37,43,48,50],clearer:4,cleari:[16,48],clearli:[3,6,7,13,14,15,16],cleavag:[1,9],clec10a:[5,12],clec12a:5,clec4c:27,clec9a:[5,12],clecl1:15,clemen:[3,4],clemenc:48,clement:11,clementin:5,clever:49,cli:7,cli_3:[8,25,27,28],click:[7,15,25],client:16,clinic:[17,47],clip:23,clip_at:1,clisi:[7,28],clivio:7,clonal:[2,3,4,24,48],clonal_expans:1,clone:[1,3,4,24,48],clone_annot:3,clone_df:4,clone_fil:3,clone_id:3,clone_kei:1,clone_network:1,clone_s:1,clones_fil:3,clonotyp:[3,4],clonotype10:2,clonotype10_consensus_1:2,clonotype11:2,clonotype11_consensus_1:2,clonotype9:2,clonotype9_consensus_1:2,clonotype9_consensus_2:2,clonotype_network:[1,4],clonotypes_top10:3,close:[1,4,5,9,11,12,13,14,15,16,19,23,24,26,28,32,36,37,38,40,41,47,48],closer:[1,11,25],closest:[11,48],cloudpickl:1,clp:49,clr:[16,27,46],clue_0:[8,25],clust_col:36,clust_label:36,cluster:[1,2,4,7,8,9,10,11,14,16,17,18,19,23,24,25,26,27,28,31,33,34,36,37,38,40,41,42,43,44,48,49,50],cluster_2:[8,25,27,28],cluster_co_occurr:40,cluster_color:37,cluster_interact:40,cluster_kei:[13,40],cluster_labels_r:17,cluster_nhood_enrich:40,cluster_numb:3,clustergrid:[1,26],clustermap:[4,26],clusters_coars:50,clusters_coarse_color:50,clusters_color:[49,50],clusters_top10:3,clustifyr:5,cm:7,cm_nsbm_level_0:13,cm_nsbm_level_1:13,cm_nsbm_level_2:13,cm_nsbm_level_3:13,cm_nsbm_level_4:13,cm_nsbm_level_5:1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s:[3,4],expansion_pvalu:48,exparc:24,expawsh16:24,expbhm:24,expda:24,expdsz:24,expect:[1,3,4,5,6,11,13,14,15,16,17,23,24,25,28,29,33,36,37,38,39,40,46,47,48,50],expected_dict:36,expected_orient:23,expens:[1,4,11,17,24,39],experi:[0,1,3,5,6,7,11,13,14,15,16,17,18,19,23,24,27,28,30,32,33,34,36,46,47,48,50],experiment:[1,3,4,7,13,14,16,17,19,24,25,26,28,33,39,44,48,49,50],experimentalist:48,expert:[5,7,27,30],expflu22:24,expgch:24,exphhs87:24,exphtt17:24,expir:2,expiry_1:8,expizj:24,expjhyf72:24,expjopa16:24,expkgn:24,expkma:24,expkvaharautiok:24,explain:[1,3,4,8,9,13,14,15,19,22,23,24,26,36,49],explan:[2,3,4,15,22,23,40],explanatori:1,explicit:[27,38],explicitli:[7,11,13,14,19,23,33],explmbw20:24,explmph18:24,exploit:1,explor:[1,3,5,8,9,13,14,26,33,43],exploratori:[8,14,19,24],explore_3:[25,28],expm1:3,expmb:24,expmlm:24,exponenti:48,export_posterior:36,expos:23,exposur:4,exppwt:24,expr:[7,11,15],expr_prod:25,expr_prop:25,expres:14,express:[2,3,6,7,8,9,13,16,17,18,19,23,24,25,26,27,28,30,31,32,33,34,39,41,42,43,44,45,46,47,48,49,50],expressed_gen:25,expressed_ligand:25,expressed_receptor:25,expression_level:38,expression_mtx_fnam:26,expressionset:17,expsaec:24,expsnl:24,exptyg:24,expwhz:24,expzll:24,expztb:24,expzvp:24,ext:27,extend:[3,5,13,19,23,27,29,36,37,48],extend_txom:23,extens:[2,3,5,7,11,13,16,17,19,22,23,24,25,30,33,36,48,50],extent:[5,7,13,19],extern:[1,16,23,25,26,28,30,48],extra:[2,7,23,25,34],extra_chain:2,extract:[1,2,3,4,8,9,10,11,15,16,17,19,23,24,25,28,36,37,41,48,50],extract_edge_weight:1,extrapol:16,extrem:[5,11,19,48,49,50],extremli:49,ey:48,eyal:36,eytan:50,f1000research:[2,5,6,7,11,14,15],f:[1,2,3,4,5,6,7,11,13,14,15,16,19,23,25,26,27,28,32,33,34,36,37,38,46,47,48,49,50],f_j:38,fa:[23,27],fabian:[2,3,5,6,7,9,11,14,16,17,19,22,23,25,27,28,29,34,36,37,40,41,47,49,50],fabiana:8,fabio:[2,24],fac:[2,5,13,17,24],face:[30,38],facecolor:[6,19,25,26,31,32,33,34,36,37,38,40,41],facetgrid:[13,47],facil:[7,24],facilit:[1,16,19,48],fact:[1,7,13,15,16,23,24,25,31,38,46,47,50],factor:[4,7,8,9,11,13,14,15,16,17,18,19,23,24,25,26,33,48],factor_nam:36,factor_names_:36,factoris:36,fail:[1,5,14,16,17,23,27,28,47,49,50],failur:[5,16,18,23,50],fairli:25,faith:16,faithfulli:50,faiz:5,falai:[36,38],falco:23,falk:[5,7,49],fall:[1,5,13,16,23,26,27,28,36,38,39,47],fals:[1,2,3,4,5,6,7,8,11,13,14,15,16,17,19,24,25,26,27,28,31,32,33,34,36,37,38,42,45,47,48,50],fam138a:[19,36],famili:[7,8,13,14,23],familiar:[13,15,30],famou:[16,48],fan:[5,7,15,23,43,49,50],fanci:[19,29],fanelli:38,fang:[9,26,36,38,48],fangm:9,fanni:7,fansi:7,fansi_1:[8,25,27,28],far:[1,5,11,23,24,30,32,40,45,50],faraz:23,farber:5,fardeep:17,farouni:23,farrar:23,farrel:7,farver_2:[8,27],fashion:[3,34,36],fasouli:5,fast:[5,6,7,15,16,19,23,24,29,33,34,39,41,43,48],fasta:23,fastdummies_1:27,fasten:1,faster:[1,2,8,11,13,15,16,18,23,38,48],fastest:7,fastmap:7,fastmap_1:[8,25,27,28],fastmnn:7,fastq:[18,23],fastq_dir:23,fastqc:23,fate:[3,47,48,49,50],fatemeh:[14,23],fault:16,faust:17,favor:[14,23,24],favour:[7,33],faw:3,fb:48,fbxo31:41,fc:[2,14],fcer1a:[5,12],fcer1g:[5,12],fcer2:27,fceria:12,fcgr3a:[5,12,15,16,25,27],fcgr3a_monocyt:14,fcn1:[5,12],fcrl1:[5,12],fct:10,fd:26,fdim:48,fdr:[13,14,15],fdrtool_1:25,fearghal:50,feasibl:4,feat:27,feather:26,feather_0:8,featu:10,featur:[1,3,4,5,10,13,16,17,18,19,23,24,25,26,27,28,30,31,34,36,37,38,39,41,44,46,47,48,49],feature_biotyp:36,feature_id:36,feature_is_filt:36,feature_nam:[13,36],feature_refer:36,feature_typ:[7,10,11,19,26,27,28,34,36,37,42,45,46,47],featureplot:10,feb:[13,14,17,19,22,24,25,30,37,40,41,47],februari:[5,7,9,23,24,36,41],fed:27,federico:22,fedrigo:24,feedback:30,feel:[5,11,16,30,38],feet:24,fei:[15,16,36],feiyang:17,fel81:48,felicia:47,felix:[3,4,14,17,23,26],felsenstein:48,femal:2,feng:[24,26,37,39,41,48],fengcui:26,fennel:24,ferequ:23,ferguson:[2,24],fernand:[13,47,49],fernandez:27,fernando:[1,2,48],ferri:23,fertil:[1,48],fetal:[38,49],fetch:[11,16,19,23],few:[1,5,7,11,13,14,15,16,19,23,26,28,31,34,36,37,38,39,41,42,43,48,49,50],fewer:[6,7,14,34,37,48],fgene:[13,23,31],fgsae:15,fgsea:15,fhl2:15,fibroblast:[5,14,36],ficht:48,field:[0,2,5,7,8,14,15,17,22,24,25,29,30,37,38,48,49,50],fier:24,fifth:[7,11,27,28,34,47,49],fig:[4,5,7,11,13,14,23,25,26,33,36,38,48],fig_kw:[1,3],fight:[2,5],figr_0:8,figshar:[2,3,5,11,15,25,31,32,33,34,36,38,49],figsiz:[1,3,4,5,11,13,15,16,25,26,33,36,37,38,40,45,48],figueroa:16,figur:[4,5,9,11,13,16,23,24,25,36,42,48],figure_s:25,filament:24,filbi:49,file:[1,2,3,4,5,8,10,11,14,15,18,19,23,24,25,26,27,28,31,32,33,34,36,38,47,48,49],file_nam:49,file_path:50,filelock_1:8,filenam:[2,3,5,19,23,31,32,33,34,36,38,49],files_list:47,fill:[4,11,19],fillna:4,film:2,filt:8,filter:[3,4,5,7,8,14,16,17,19,23,25,33,36,37,38,43,44,45,46,47,49,50],filter_and_norm:50,filter_cel:[14,19,25,47],filter_gen:[7,14,19,25,34,36,37,49],filter_intbcs_final_lineag:48,filter_lambda:25,filter_ob:[11,19],filter_rank_genes_group:5,filter_var:[11,19,44],filterbi:25,filterbyexpr:[14,15],filtered_:47,filtere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Acknowledgements","42. Clonotype analysis","41. Immune Receptor Profiling","47. Integrating AIR and transcriptomics","46. Specificity analysis","11. Annotation","10. Clustering","12. Data integration","25. Gene regulatory networks using chromatin accessibility","23. Single-cell ATAC sequencing","Conversion of muon objects to Seurat objects to use in downstream analysis","24. Quality Control","Markers for cluster annotations acros modalities","17. Compositional analysis","16. Differential gene expression analysis","18. Gene set enrichment and pathway analysis","19. Perturbation modeling","22. Bulk deconvolution","53. Glossary","4. Analysis frameworks and tools","Data infrastructure","5. Interoperability","1. Prior art","3. Raw data processing","2. Single-cell RNA sequencing","21. Cell-cell communication","20. Gene regulatory networks","49. Advanced integration","48. Paired integration","51. Outlook","Single-cell best practices","9. Dimensionality Reduction","8. Feature selection","7. Normalization","6. Quality Control","50. Reproducibility","30. Spatial deconvolution","28. Spatial domains","31. Imputation","26. Single-cell data resolved in space","27. Neighborhood analysis","29. Spatially variable genes","40. Annotation","39. Batch correction","38. Dimensionality Reduction","37. Doublet detection","36. Normalization","35. Quality control","15. Lineage tracing","13. Pseudotemporal ordering","14. RNA 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