Cluster analysis of cell hashing test experiment

workflowr

• Summary
• Checks
• Past versions

Last updated: 2021-05-19

Checks: 7 0

Knit directory: neural_scRNAseq/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it's best to always run the code in an empty environment.

Seed: set.seed(20200522)

The command set.seed(20200522) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 83f986d

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 83f986d. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    ._.DS_Store
    Ignored:    ._Filtered.pdf
    Ignored:    ._Rplots.pdf
    Ignored:    ._Unfiltered.pdf
    Ignored:    .__workflowr.yml
    Ignored:    ._coverage.pdf
    Ignored:    ._coverage_sashimi.pdf
    Ignored:    ._coverage_sashimi.png
    Ignored:    ._neural_scRNAseq.Rproj
    Ignored:    ._pbDS_cell_level.pdf
    Ignored:    ._pbDS_top_expr_umap.pdf
    Ignored:    ._pbDS_upset.pdf
    Ignored:    ._sashimi.pdf
    Ignored:    ._stmn2.pdf
    Ignored:    ._tdp.pdf
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/.Rhistory
    Ignored:    analysis/._.DS_Store
    Ignored:    analysis/._01-preprocessing.Rmd
    Ignored:    analysis/._01-preprocessing.html
    Ignored:    analysis/._02.1-SampleQC.Rmd
    Ignored:    analysis/._03-filtering.Rmd
    Ignored:    analysis/._04-clustering.Rmd
    Ignored:    analysis/._04-clustering.knit.md
    Ignored:    analysis/._04.1-cell_cycle.Rmd
    Ignored:    analysis/._05-annotation.Rmd
    Ignored:    analysis/._07-cluster-analysis-all-timepoints.Rmd
    Ignored:    analysis/._Lam-0-NSC_no_integration.Rmd
    Ignored:    analysis/._Lam-01-NSC_integration.Rmd
    Ignored:    analysis/._Lam-02-NSC_annotation.Rmd
    Ignored:    analysis/._NSC-1-clustering.Rmd
    Ignored:    analysis/._NSC-2-annotation.Rmd
    Ignored:    analysis/.__site.yml
    Ignored:    analysis/._additional_filtering.Rmd
    Ignored:    analysis/._additional_filtering_clustering.Rmd
    Ignored:    analysis/._index.Rmd
    Ignored:    analysis/._organoid-01-1-qualtiy-control.Rmd
    Ignored:    analysis/._organoid-01-clustering.Rmd
    Ignored:    analysis/._organoid-02-integration.Rmd
    Ignored:    analysis/._organoid-03-cluster_analysis.Rmd
    Ignored:    analysis/._organoid-04-group_integration.Rmd
    Ignored:    analysis/._organoid-04-stage_integration.Rmd
    Ignored:    analysis/._organoid-05-group_integration_cluster_analysis.Rmd
    Ignored:    analysis/._organoid-05-stage_integration_cluster_analysis.Rmd
    Ignored:    analysis/._organoid-06-1-prepare-sce.Rmd
    Ignored:    analysis/._organoid-06-conos-analysis-Seurat.Rmd
    Ignored:    analysis/._organoid-06-conos-analysis-function.Rmd
    Ignored:    analysis/._organoid-06-conos-analysis.Rmd
    Ignored:    analysis/._organoid-06-group-integration-conos-analysis.Rmd
    Ignored:    analysis/._organoid-07-conos-visualization.Rmd
    Ignored:    analysis/._organoid-07-group-integration-conos-visualization.Rmd
    Ignored:    analysis/._organoid-08-conos-comparison.Rmd
    Ignored:    analysis/._organoid-0x-sample_integration.Rmd
    Ignored:    analysis/01-preprocessing_cache/
    Ignored:    analysis/02-1-SampleQC_cache/
    Ignored:    analysis/02-quality_control_cache/
    Ignored:    analysis/02.1-SampleQC_cache/
    Ignored:    analysis/03-filtering_cache/
    Ignored:    analysis/04-clustering_cache/
    Ignored:    analysis/04.1-cell_cycle_cache/
    Ignored:    analysis/05-annotation_cache/
    Ignored:    analysis/06-clustering-all-timepoints_cache/
    Ignored:    analysis/07-cluster-analysis-all-timepoints_cache/
    Ignored:    analysis/CH-test-01-preprocessing_cache/
    Ignored:    analysis/CH-test-02-transgene-expression_cache/
    Ignored:    analysis/Lam-01-NSC_integration_cache/
    Ignored:    analysis/Lam-02-NSC_annotation_cache/
    Ignored:    analysis/NSC-1-clustering_cache/
    Ignored:    analysis/NSC-2-annotation_cache/
    Ignored:    analysis/TDP-01-preprocessing_cache/
    Ignored:    analysis/TDP-02-quality_control_cache/
    Ignored:    analysis/TDP-03-filtering_cache/
    Ignored:    analysis/TDP-04-clustering_cache/
    Ignored:    analysis/TDP-05-00-filtering-plasmid-QC_cache/
    Ignored:    analysis/TDP-05-plasmid_expression_cache/
    Ignored:    analysis/TDP-06-cluster_analysis_cache/
    Ignored:    analysis/TDP-07-01-STMN2_expression_cache/
    Ignored:    analysis/TDP-07-cluster_12_cache/
    Ignored:    analysis/TDP-08-00-clustering-HA-D96_cache/
    Ignored:    analysis/TDP-08-01-HA-D96-expression-changes_cache/
    Ignored:    analysis/TDP-08-02-TDP_target_genes_cache/
    Ignored:    analysis/TDP-08-clustering-timeline-HA_cache/
    Ignored:    analysis/additional_filtering_cache/
    Ignored:    analysis/additional_filtering_clustering_cache/
    Ignored:    analysis/organoid-01-1-qualtiy-control_cache/
    Ignored:    analysis/organoid-01-clustering_cache/
    Ignored:    analysis/organoid-02-integration_cache/
    Ignored:    analysis/organoid-03-cluster_analysis_cache/
    Ignored:    analysis/organoid-04-group_integration_cache/
    Ignored:    analysis/organoid-04-stage_integration_cache/
    Ignored:    analysis/organoid-05-group_integration_cluster_analysis_cache/
    Ignored:    analysis/organoid-05-stage_integration_cluster_analysis_cache/
    Ignored:    analysis/organoid-06-conos-analysis_cache/
    Ignored:    analysis/organoid-06-conos-analysis_test_cache/
    Ignored:    analysis/organoid-06-group-integration-conos-analysis_cache/
    Ignored:    analysis/organoid-07-conos-visualization_cache/
    Ignored:    analysis/organoid-07-group-integration-conos-visualization_cache/
    Ignored:    analysis/organoid-08-conos-comparison_cache/
    Ignored:    analysis/organoid-0x-sample_integration_cache/
    Ignored:    analysis/sample5_QC_cache/
    Ignored:    analysis/timepoints-01-organoid-integration_cache/
    Ignored:    analysis/timepoints-02-cluster-analysis_cache/
    Ignored:    data/.DS_Store
    Ignored:    data/._.DS_Store
    Ignored:    data/._.smbdeleteAAA17ed8b4b
    Ignored:    data/._Lam_figure2_markers.R
    Ignored:    data/._README.md
    Ignored:    data/._Reactive_astrocytes_markers.xlsx
    Ignored:    data/._known_NSC_markers.R
    Ignored:    data/._known_cell_type_markers.R
    Ignored:    data/._metadata.csv
    Ignored:    data/._virus_cell_tropism_markers.R
    Ignored:    data/._~$Reactive_astrocytes_markers.xlsx
    Ignored:    data/data_sushi/
    Ignored:    data/filtered_feature_matrices/
    Ignored:    output/.DS_Store
    Ignored:    output/._.DS_Store
    Ignored:    output/._NSC_cluster2_marker_genes.txt
    Ignored:    output/._TDP-06-no_integration_cluster12_marker_genes.txt
    Ignored:    output/._TDP-06-no_integration_cluster13_marker_genes.txt
    Ignored:    output/._organoid_integration_cluster1_marker_genes.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_0.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_1.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_10.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_11.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_12.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_13.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_14.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_5.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_7.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_8.txt
    Ignored:    output/._tbl_TDP-08-01-muscat_cluster_all.xlsx
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_0.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_1.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_10.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_11.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_12.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_13.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_14.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_5.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_7.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_8.txt
    Ignored:    output/._tbl_TDP-08-02-targets_hek_cluster_all.xlsx
    Ignored:    output/._~$tbl_TDP-08-02-targets_hek_cluster_all.xlsx
    Ignored:    output/CH-test-01-preprocessing.rds
    Ignored:    output/CH-test-01-preprocessing_singlets.rds
    Ignored:    output/CH-test-01-preprocessing_singlets_filtered.rds
    Ignored:    output/CH-test-01-preprocessing_so.rds
    Ignored:    output/CH-test-01-preprocessing_so_filtered.rds
    Ignored:    output/CH-test-03-cluster-analysis_so.rds
    Ignored:    output/CH-test-03_scran_markers.rds
    Ignored:    output/Lam-01-clustering.rds
    Ignored:    output/NSC_1_clustering.rds
    Ignored:    output/NSC_cluster1_marker_genes.txt
    Ignored:    output/NSC_cluster2_marker_genes.txt
    Ignored:    output/NSC_cluster3_marker_genes.txt
    Ignored:    output/NSC_cluster4_marker_genes.txt
    Ignored:    output/NSC_cluster5_marker_genes.txt
    Ignored:    output/NSC_cluster6_marker_genes.txt
    Ignored:    output/NSC_cluster7_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster0_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster10_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster11_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster12_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster13_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster14_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster15_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster16_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster17_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster1_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster2_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster3_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster4_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster5_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster6_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster7_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster8_marker_genes.txt
    Ignored:    output/TDP-06-no_integration_cluster9_marker_genes.txt
    Ignored:    output/TDP-06_scran_markers.rds
    Ignored:    output/additional_filtering.rds
    Ignored:    output/conos/
    Ignored:    output/conos_organoid-06-conos-analysis.rds
    Ignored:    output/conos_organoid-06-group-integration-conos-analysis.rds
    Ignored:    output/figures/
    Ignored:    output/organoid_integration_cluster10_marker_genes.txt
    Ignored:    output/organoid_integration_cluster11_marker_genes.txt
    Ignored:    output/organoid_integration_cluster12_marker_genes.txt
    Ignored:    output/organoid_integration_cluster13_marker_genes.txt
    Ignored:    output/organoid_integration_cluster14_marker_genes.txt
    Ignored:    output/organoid_integration_cluster15_marker_genes.txt
    Ignored:    output/organoid_integration_cluster16_marker_genes.txt
    Ignored:    output/organoid_integration_cluster17_marker_genes.txt
    Ignored:    output/organoid_integration_cluster1_marker_genes.txt
    Ignored:    output/organoid_integration_cluster2_marker_genes.txt
    Ignored:    output/organoid_integration_cluster3_marker_genes.txt
    Ignored:    output/organoid_integration_cluster4_marker_genes.txt
    Ignored:    output/organoid_integration_cluster5_marker_genes.txt
    Ignored:    output/organoid_integration_cluster6_marker_genes.txt
    Ignored:    output/organoid_integration_cluster7_marker_genes.txt
    Ignored:    output/organoid_integration_cluster8_marker_genes.txt
    Ignored:    output/organoid_integration_cluster9_marker_genes.txt
    Ignored:    output/res_TDP-08-01-muscat.rds
    Ignored:    output/sce_01_preprocessing.rds
    Ignored:    output/sce_02_quality_control.rds
    Ignored:    output/sce_03_filtering.rds
    Ignored:    output/sce_03_filtering_all_genes.rds
    Ignored:    output/sce_06-1-prepare-sce.rds
    Ignored:    output/sce_TDP-08-01-muscat.rds
    Ignored:    output/sce_TDP_01_preprocessing.rds
    Ignored:    output/sce_TDP_02_quality_control.rds
    Ignored:    output/sce_TDP_03_filtering.rds
    Ignored:    output/sce_TDP_03_filtering_all_genes.rds
    Ignored:    output/sce_organoid-01-clustering.rds
    Ignored:    output/sce_preprocessing.rds
    Ignored:    output/so_04-stage_integration.rds
    Ignored:    output/so_04_1_cell_cycle.rds
    Ignored:    output/so_04_clustering.rds
    Ignored:    output/so_06-clustering_all_timepoints.rds
    Ignored:    output/so_08-00_clustering_HA_D96.rds
    Ignored:    output/so_08-clustering_timeline_HA.rds
    Ignored:    output/so_0x-sample_integration.rds
    Ignored:    output/so_CH-test-02-transgene_expression.rds
    Ignored:    output/so_TDP-06-cluster-analysis.rds
    Ignored:    output/so_TDP_04_clustering.rds
    Ignored:    output/so_TDP_05_plasmid_expression.rds
    Ignored:    output/so_additional_filtering_clustering.rds
    Ignored:    output/so_integrated_organoid-02-integration.rds
    Ignored:    output/so_merged_organoid-02-integration.rds
    Ignored:    output/so_organoid-01-clustering.rds
    Ignored:    output/so_sample_organoid-01-clustering.rds
    Ignored:    output/so_timepoints-01-organoid_integration.rds
    Ignored:    output/tbl_TDP-08-01-muscat.rds
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_0.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_1.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_10.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_11.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_12.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_13.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_14.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_5.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_7.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_8.txt
    Ignored:    output/tbl_TDP-08-01-muscat_cluster_all.xlsx
    Ignored:    output/tbl_TDP-08-02-targets_hek.rds
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_0.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_1.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_10.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_11.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_12.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_13.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_14.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_5.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_7.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_8.txt
    Ignored:    output/tbl_TDP-08-02-targets_hek_cluster_all.xlsx
    Ignored:    output/~$tbl_TDP-08-02-targets_hek_cluster_all.xlsx
    Ignored:    scripts/.DS_Store
    Ignored:    scripts/._.DS_Store
    Ignored:    scripts/._bu_Rcode.R
    Ignored:    scripts/._plasmid_expression.sh
    Ignored:    scripts/._plasmid_expression_cell_hashing_test.sh
    Ignored:    scripts/._prepare_salmon_transcripts.R

Untracked files:
    Untracked:  Filtered.pdf
    Untracked:  Rplots.pdf
    Untracked:  Unfiltered
    Untracked:  Unfiltered.pdf
    Untracked:  analysis/Lam-0-NSC_no_integration.Rmd
    Untracked:  analysis/TDP-07-01-STMN2_expression copy.Rmd
    Untracked:  analysis/additional_filtering.Rmd
    Untracked:  analysis/additional_filtering_clustering.Rmd
    Untracked:  analysis/organoid-01-1-qualtiy-control.Rmd
    Untracked:  analysis/organoid-06-conos-analysis-Seurat.Rmd
    Untracked:  analysis/organoid-06-conos-analysis-function.Rmd
    Untracked:  analysis/organoid-07-conos-visualization.Rmd
    Untracked:  analysis/organoid-07-group-integration-conos-visualization.Rmd
    Untracked:  analysis/organoid-08-conos-comparison.Rmd
    Untracked:  analysis/organoid-0x-sample_integration.Rmd
    Untracked:  analysis/sample5_QC.Rmd
    Untracked:  coverage.pdf
    Untracked:  coverage_sashimi.pdf
    Untracked:  coverage_sashimi.png
    Untracked:  data/Homo_sapiens.GRCh38.98.sorted.gtf
    Untracked:  data/Kanton_et_al/
    Untracked:  data/Lam_et_al/
    Untracked:  data/Sep2020/
    Untracked:  data/cell_hashing_test/
    Untracked:  data/cell_hashing_test_FB/
    Untracked:  data/reference/
    Untracked:  data/virus_cell_tropism_markers.R
    Untracked:  data/~$Reactive_astrocytes_markers.xlsx
    Untracked:  pbDS_cell_level.pdf
    Untracked:  pbDS_heatmap.pdf
    Untracked:  pbDS_top_expr_umap.pdf
    Untracked:  pbDS_upset.pdf
    Untracked:  sashimi.pdf
    Untracked:  scripts/bu_Rcode.R
    Untracked:  scripts/bu_code.Rmd
    Untracked:  scripts/plasmid_expression_cell_hashing_test.sh
    Untracked:  scripts/prepare_salmon_transcripts_cell_hashing_test.R
    Untracked:  scripts/salmon-latest_linux_x86_64/
    Untracked:  stmn2.pdf
    Untracked:  tdp.pdf

Unstaged changes:
    Modified:   analysis/05-annotation.Rmd
    Modified:   analysis/TDP-04-clustering.Rmd
    Modified:   analysis/TDP-08-01-HA-D96-expression-changes.Rmd
    Modified:   analysis/_site.yml
    Modified:   analysis/organoid-02-integration.Rmd
    Modified:   analysis/organoid-04-group_integration.Rmd
    Modified:   analysis/organoid-06-conos-analysis.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/CH-test-03-cluster-analysis.Rmd) and HTML (docs/CH-test-03-cluster-analysis.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	83f986d	khembach	2021-05-19	add heatmap of neuronal clusters and cluster 12 marker gene expression
Rmd	29caa75	khembach	2021-05-19	Merge branch 'master' of https://github.com/khembach/neural_scRNAseq
html	c425894	khembach	2021-05-19	Build site.
Rmd	f04b0df	khembach	2021-05-19	add heatmap with relative sample abundance per cluster
Rmd	7f5ea7a	khembach	2021-05-19	add heatmap with relative sample abundance per cluster
html	d55a2c2	khembach	2021-05-19	Build site.
Rmd	ab048ce	khembach	2021-05-19	analyze clusters of cell hashing test experiment

---

Load packages

library(ComplexHeatmap)
library(cowplot)
library(ggplot2)
library(dplyr)
library(muscat)
library(purrr)
library(RColorBrewer)
library(viridis)
library(scran)
library(Seurat)
library(SingleCellExperiment)
library(stringr)

Load data & convert to SCE

so <- readRDS(file.path("output", "so_CH-test-02-transgene_expression.rds"))
sce <- as.SingleCellExperiment(so, assay = "RNA")
colData(sce) <- as.data.frame(colData(sce)) %>% 
    mutate_if(is.character, as.factor) %>% 
    DataFrame(row.names = colnames(sce))

Number of clusters by resolution

cluster_cols <- grep("res.[0-9]", colnames(colData(sce)), value = TRUE)
sapply(colData(sce)[cluster_cols], nlevels)

RNA_snn_res.0.6 RNA_snn_res.0.2 RNA_snn_res.0.4 RNA_snn_res.0.8   RNA_snn_res.1 
             10               6              11              16              16 

Cluster-sample counts

# set cluster IDs to resolution 0.4 clustering
so <- SetIdent(so, value = "RNA_snn_res.0.8")
so@meta.data$cluster_id <- Idents(so)
sce$cluster_id <- Idents(so)
(n_cells <- table(sce$cluster_id, sce$sample_id))

    
     GA50-EGFP HA-GA50 TDP-43-HA
  0        280     246       285
  1        155     135       212
  2        185     134       149
  3        208     138        63
  4         85      98       121
  5         93      93        98
  6         84      89        99
  7         96      66       108
  8        111      68        83
  9        115      61        41
  10       139      62         3
  11        53      50        51
  12        33      37        53
  13        39      34         7
  14         8      14        46
  15        15       4         5

Relative cluster-abundances

fqs <- prop.table(n_cells, margin = 2)
mat <- round(as.matrix(unclass(fqs))*100, 2)
colfunc <- colorRampPalette(c("ghostwhite", "deepskyblue4"))
Heatmap(mat,
    col = colfunc(10),
    name = "Percentage\nof cells",
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_names_side = "left",
    row_title = "cluster ID",
    column_title = "sample ID",
    column_title_side = "bottom",
    rect_gp = gpar(col = "white"),
    cell_fun = function(i, j, x, y, width, height, fill)
        grid.text(mat[j, i], x = x, y = y, 
            gp = gpar(col = "black", fontsize = 10)))

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
c425894	khembach	2021-05-19
d55a2c2	khembach	2021-05-19

(n_cells <- table(sce$sample_id, sce$cluster_id))

           
              0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15
  GA50-EGFP 280 155 185 208  85  93  84  96 111 115 139  53  33  39   8  15
  HA-GA50   246 135 134 138  98  93  89  66  68  61  62  50  37  34  14   4
  TDP-43-HA 285 212 149  63 121  98  99 108  83  41   3  51  53   7  46   5

fqs <- prop.table(n_cells, margin = 2)
mat <- round(as.matrix(unclass(fqs))*100, 2)
Heatmap(mat,
    col = colfunc(10),
    name = "Percentage\nof cells",
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_names_side = "left",
    row_title = "sample ID",
    column_title = "cluster ID",
    column_title_side = "bottom",
    rect_gp = gpar(col = "white"),
    cell_fun = function(i, j, x, y, width, height, fill)
        grid.text(mat[j, i], x = x, y = y, 
            gp = gpar(col = "black", fontsize = 10)))

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
c425894	khembach	2021-05-19

DR colored by cluster ID

.plot_dr <- function(so, dr, id)
    DimPlot(so, group.by = id, reduction = dr, pt.size = 0.4) +
        guides(col = guide_legend(nrow = 11, 
            override.aes = list(size = 3, alpha = 1))) +
        theme_void() + theme(aspect.ratio = 1)
ids <- c("cluster_id", "sample_id")
for (id in ids) {
    cat("## ", id, "\n")
    p1 <- .plot_dr(so, "tsne", id)
    lgd <- get_legend(p1)
    p1 <- p1 + theme(legend.position = "none")
    p2 <- .plot_dr(so, "umap", id) + theme(legend.position = "none")
    ps <- plot_grid(plotlist = list(p1, p2), nrow = 1)
    p <- plot_grid(ps, lgd, nrow = 1, rel_widths = c(1, 0.2))
    print(p)
    cat("\n\n")
}

cluster_id

Past versions of dim-red-1.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

sample_id

Past versions of dim-red-2.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

Find markers using scran

Markers

We identify candidate marker genes for each cluster that enable a separation of that group from any other group. The null hypothesis is that the log FC between a cluster and the compared cluster is 2.

scran_markers <- findMarkers(sce, 
    groups = sce$cluster_id, direction = "up", lfc = 2, 
    full.stats = TRUE, log.p = FALSE)

Heatmap of mean marker-exprs. by cluster

We aggregate the cells to pseudobulks and plot the average expression of the condidate marker genes in each of the clusters.

## including marker genes of rank 1 and 2
gs <- lapply(scran_markers, function(u) rownames(u)[u$Top %in% 1:2])
## candidate cluster markers
lapply(gs, function(x) {
  y <- str_split(x, pattern = "\\.", simplify = TRUE)[,2]
  y[which(y == "")] <- x[which(y == "")]
  y
  })

$`0`
 [1] "RTN4"         "VGF"          "STMN2"        "RPLP1"        "TMSB4X"      
 [6] "MT-RNR2"      "GAP43"        "TAC1"         "RPS24"        "FTH1"        
[11] "MT-ATP6"      "STMN2-alevin"

$`1`
[1] "VGF"     "WSB1"    "MT-RNR2" "SCG2"    "DST"     "STMN2"   "ANK3"   
[8] "MT-RNR1" "MT-ATP6"

$`2`
 [1] "VGF"     "STMN2"   "VIM"     "FTL"     "MT-RNR2" "MT-ATP6" "MT-ND5" 
 [8] "RPL32"   "RPL34"   "MT-RNR1" "MT-ND1"  "MT-CO3" 

$`3`
 [1] "TUBB"         "CLU"          "DYNC2H1"      "CST3"         "PCP4"        
 [6] "STMN2-alevin" "NSG2"         "NPTX2"        "STMN2"        "RPL7A"       
[11] "RPLP1"       

$`4`
 [1] "PNOC"         "RTN1"         "CRABP1"       "STMN2-alevin" "MLLT11"      
 [6] "SPP1"         "SCRG1"        "STMN2"        "RPS13"        "TUBA1A"      
[11] "PCP4"        

$`5`
[1] "S100A10"      "IGFBP5"       "STMN2-alevin" "SH3BGRL3"     "TAC1"        
[6] "STMN2"        "B2M"          "MT-RNR2"     

$`6`
 [1] "UTS2"         "FABP7"        "CRABP1"       "STMN2-alevin" "RPS8"        
 [6] "SCG2"         "TAC1"         "STMN2"        "TUBA1A"       "RTN1"        

$`7`
[1] "C1orf61" "SPARCL1" "SPP1"    "VIM"     "GFAP"    "FABP5"  

$`8`
[1] "C1orf61"      "STMN2"        "VIM"          "MT-RNR2"      "MT-ATP6"     
[6] "STMN2-alevin"

$`9`
 [1] "MAP2"     "DST"      "PLCG2"    "WSB1"     "MT-RNR2"  "MT-CO2"  
 [7] "MT-ATP6"  "TDP43-HA" "PCED1A"   "MT-RNR1"  "MT-ND2"   "MT-ND4"  

$`10`
[1] "SPOCK3"       "CLU"          "RPS13"        "TDP43-HA"     "STMN2-alevin"
[6] "SELENOK"      "STMN2"        "DYNC2H1"      "TAC3"        

$`11`
[1] "SH3BGRL3"     "TAC1"         "CRH"          "STMN2-alevin" "STMN2"       
[6] "TUBA1A"      

$`12`
[1] "PTGDS"  "VIM"    "IFITM3" "DLK1"   "B2M"    "FTL"   

$`13`
[1] "S100A16" "CLU"     "TAC3"    "CALB1"   "APOE"   

$`14`
[1] "PLCG2"    "RSRP1"    "CKS2"     "MT-RNR2"  "TDP43-HA"

$`15`
[1] "PTN"     "VIM"     "PCLAF"   "S100A10" "C1orf61" "DBI"     "HMGB2"  
[8] "TUBA1B" 

sub <- sce[unique(unlist(gs)), ]
pbs <- aggregateData(sub, assay = "logcounts", by = "cluster_id", fun = "mean")
mat <- t(muscat:::.scale(assay(pbs)))
## remove the Ensembl ID from the gene names
cnames <- colnames(mat)
colnames(mat) <- str_split(cnames, pattern = "\\.", simplify = TRUE)[,2] 
colnames(mat)[which(colnames(mat) == "")] <- cnames[which(colnames(mat) == "")]

Heatmap(mat,
    name = "scaled avg.\nexpression",
    col = viridis(10),
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_names_side = "left",
    row_title = "cluster_id",
    rect_gp = gpar(col = "white"))

Past versions of scran-markers-heatmap-1.png

Version	Author	Date
c425894	khembach	2021-05-19
d55a2c2	khembach	2021-05-19

Known marker genes

Apart from the usual marker genes, we also want to analyse the expression of Casein Kinase 1 Epsilon (CSNK1E).

## source file with list of known marker genes
source(file.path("data", "known_cell_type_markers.R"))
fs[["kinase"]] <- "CSNK1E"

fs <- lapply(fs, sapply, function(g)
    grep(pattern = paste0("\\.", g, "$"), rownames(sce), value = TRUE)
  )
fs <- lapply(fs, function(x) unlist(x[lengths(x) !=0]) )

gs <- gsub(".*\\.", "", unlist(fs))
ns <- vapply(fs, length, numeric(1))
ks <- rep.int(names(fs), ns)
labs <- lapply(fs, function(x) gsub(".*\\.", "",x))

Heatmap of mean marker-exprs. by cluster

n_cells <- table(sce$cluster_id, sce$sample_id)
# split cells by cluster
cs_by_k <- split(colnames(sce), sce$cluster_id)
# compute cluster-marker means
ms_by_cluster <- lapply(fs, function(gs) vapply(cs_by_k, function(i)
        Matrix::rowMeans(logcounts(sce)[gs, i, drop = FALSE]), 
        numeric(length(gs))))
# prep. for plotting & scale b/w 0 and 1
mat <- do.call("rbind", ms_by_cluster)
mat <- muscat:::.scale(mat)
rownames(mat) <- gs
cols <- muscat:::.cluster_colors[seq_along(fs)]
cols <- setNames(cols, names(fs))
row_anno <- rowAnnotation(
    df = data.frame(label = factor(ks, levels = names(fs))),
    col = list(label = cols), gp = gpar(col = "white"))
# percentage of cells from each of the samples per cluster
sample_props <- prop.table(n_cells, margin = 1)
col_mat <- as.matrix(unclass(sample_props))
sample_cols <- c("#882255", "#11588A",  "#117733")
sample_cols <- setNames(sample_cols, colnames(col_mat))
col_anno <- HeatmapAnnotation(
    perc_sample = anno_barplot(col_mat, gp = gpar(fill = sample_cols), 
                               height = unit(2, "cm"),
                               border = FALSE),
    annotation_label = "fraction of sample\nin cluster",
    gap = unit(10, "points"))
col_lgd <- Legend(labels = names(sample_cols),
       title = "sample",
       legend_gp = gpar(fill = sample_cols))

hm <- Heatmap(mat,
    name = "scaled avg.\nexpression",
    col = viridis(10),
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_names_side = "left",
    column_title = "cluster_id",
    column_title_side = "bottom",
    column_names_side = "bottom",
    column_names_rot = 0, 
    column_names_centered = TRUE,
    rect_gp = gpar(col = "white"),
    left_annotation = row_anno,
    top_annotation = col_anno)
draw(hm, annotation_legend_list = list(col_lgd))

Past versions of known-markers-heatmap-1.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

DR colored by marker expression

# UMAPs colored by marker-expression
for (m in seq_along(fs)) {
    cat("## ", names(fs)[m], "\n")
    ps <- lapply(seq_along(fs[[m]]), function(i) {
        if (!fs[[m]][i] %in% rownames(so)) return(NULL)
        FeaturePlot(so, features = fs[[m]][i], reduction = "umap", 
                    pt.size = 0.4) +
            theme(aspect.ratio = 1, legend.position = "none") +
            ggtitle(labs[[m]][i]) + theme_void() + theme(aspect.ratio = 1)
    })
    # arrange plots in grid
    ps <- ps[!vapply(ps, is.null, logical(1))]
    p <- plot_grid(plotlist = ps, ncol = 4, label_size = 10)
    print(p)
    cat("\n\n")
}

NSC

Past versions of dim-red-expr-1.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

proliferating

Past versions of dim-red-expr-2.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

neuronal

Past versions of dim-red-expr-3.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

mature_astrocytes

Past versions of dim-red-expr-4.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

glial_astrocytic

Past versions of dim-red-expr-5.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

radial_glia

Past versions of dim-red-expr-6.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

oligodendrocyte

Past versions of dim-red-expr-7.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

GABAergic_neurons

Past versions of dim-red-expr-8.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

glycinergic_neurons

Past versions of dim-red-expr-9.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

glutaminergic_neurons

Past versions of dim-red-expr-10.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

dopaminergic_neurons

Past versions of dim-red-expr-11.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

apoptotic

Past versions of dim-red-expr-12.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

kinase

Past versions of dim-red-expr-13.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

## plot the expression of the endogenous TDP-43 and TDP-HA
transg <- c("ENSG00000120948.TARDBP", "ENSG00000120948.TARDBP-alevin", "TDP43-HA", 
         "GA50-EGFP", "HA-GA50")
names(transg) <- c("TARDBP", "TARDBP-alevin", "TDP-HA", "GA50-EGFP", "HA-GA50")
cat("## transgenes\n")

transgenes

ps <- lapply(seq_along(transg), function(i) {
    if (!transg[i] %in% rownames(so)) return(NULL)
    FeaturePlot(so, features = transg[i], reduction = "umap", pt.size = 0.4) +
        theme(aspect.ratio = 1, legend.position = "none") +
        ggtitle(names(transg)[i]) + theme_void() + theme(aspect.ratio = 1)
})
# arrange plots in grid
ps <- ps[!vapply(ps, is.null, logical(1))]
p <- plot_grid(plotlist = ps, ncol = 4, label_size = 10)
print(p)

Past versions of dim-red-expr-14.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

cat("\n\n")

Reactive astrocyte markers

## source file with list of known marker genes
source(file.path("data", "reactive_astrocyte_markers.R"))
fs <- lapply(fs, sapply, function(g)
    grep(pattern = paste0("\\.", g, "$"), rownames(sce), value = TRUE)
  )
fs <- lapply(fs, function(x) unlist(x[lengths(x) !=0]) )

gs <- gsub(".*\\.", "", unlist(fs))
ns <- vapply(fs, length, numeric(1))
ks <- rep.int(names(fs), ns)
labs <- lapply(fs, function(x) gsub(".*\\.", "",x))

Heatmap of reactive astrocyte markers

# compute cluster-marker means
ms_by_cluster <- lapply(fs, function(gs) vapply(cs_by_k, function(i)
        Matrix::rowMeans(logcounts(sce)[gs, i, drop = FALSE]), 
        numeric(length(gs))))
# prep. for plotting & scale b/w 0 and 1
mat <- do.call("rbind", ms_by_cluster)
mat <- muscat:::.scale(mat)
rownames(mat) <- gs
cols <- muscat:::.cluster_colors[seq_along(fs)]
cols <- setNames(cols, names(fs))
row_anno <- rowAnnotation(
    df = data.frame(label = factor(ks, levels = names(fs))),
    col = list(label = cols), gp = gpar(col = "white"))
# percentage of cells from each of the samples per cluster
sample_props <- prop.table(n_cells, margin = 1)
col_mat <- as.matrix(unclass(sample_props))
sample_cols <- c("#882255", "#11588A",  "#117733")
sample_cols <- setNames(sample_cols, colnames(col_mat))
col_anno <- HeatmapAnnotation(
    perc_sample = anno_barplot(col_mat, gp = gpar(fill = sample_cols), 
                               height = unit(2, "cm"),
                               border = FALSE),
    annotation_label = "fraction of sample\nin cluster",
    gap = unit(10, "points"))
col_lgd <- Legend(labels = names(sample_cols),
       title = "sample",
       legend_gp = gpar(fill = sample_cols))

hm <- Heatmap(mat,
    name = "scaled avg.\nexpression",
    col = viridis(10),
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_names_side = "left",
    column_title = "cluster_id",
    column_title_side = "bottom",
    column_names_side = "bottom",
    column_names_rot = 0, 
    column_names_centered = TRUE,
    rect_gp = gpar(col = "white"),
    left_annotation = row_anno,
    top_annotation = col_anno)
draw(hm, annotation_legend_list = list(col_lgd))

Past versions of astrocyte-markers-heatmap-1.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

Marker genes for virus cell tropism

## source file with list of known marker genes
source(file.path("data", "virus_cell_tropism_markers.R"))
fs <- lapply(fs, sapply, function(g)
    grep(pattern = paste0("\\.", g, "$"), rownames(sce), value = TRUE)
  )
fs <- lapply(fs, function(x) unlist(x[lengths(x) !=0]) )

gs <- gsub(".*\\.", "", unlist(fs))
ns <- vapply(fs, length, numeric(1))
ks <- rep.int(names(fs), ns)
labs <- lapply(fs, function(x) gsub(".*\\.", "",x))

Heatmap of virus cell tropism markers

# compute cluster-marker means
ms_by_cluster <- lapply(fs, function(gs) vapply(cs_by_k, function(i)
        Matrix::rowMeans(logcounts(sce)[gs, i, drop = FALSE]), 
        numeric(length(gs))))
# prep. for plotting & scale b/w 0 and 1
mat <- do.call("rbind", ms_by_cluster)
mat <- muscat:::.scale(mat)
rownames(mat) <- gs
cols <- muscat:::.cluster_colors[seq_along(fs)]
cols <- setNames(cols, names(fs))
row_anno <- rowAnnotation(
    df = data.frame(label = factor(ks, levels = names(fs))),
    col = list(label = cols), gp = gpar(col = "white"))
# percentage of cells from each of the samples per cluster
sample_props <- prop.table(n_cells, margin = 1)
col_mat <- as.matrix(unclass(sample_props))
sample_cols <- c("#882255", "#11588A",  "#117733")
sample_cols <- setNames(sample_cols, colnames(col_mat))
col_anno <- HeatmapAnnotation(
    perc_sample = anno_barplot(col_mat, gp = gpar(fill = sample_cols), 
                               height = unit(2, "cm"),
                               border = FALSE),
    annotation_label = "fraction of sample\nin cluster",
    gap = unit(10, "points"))
col_lgd <- Legend(labels = names(sample_cols),
       title = "sample",
       legend_gp = gpar(fill = sample_cols))

hm <- Heatmap(mat,
    name = "scaled avg.\nexpression",
    col = viridis(10),
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_names_side = "left",
    column_title = "cluster_id",
    column_title_side = "bottom",
    column_names_side = "bottom",
    column_names_rot = 0, 
    column_names_centered = TRUE,
    rect_gp = gpar(col = "white"),
    left_annotation = row_anno,
    top_annotation = col_anno)
draw(hm, annotation_legend_list = list(col_lgd))

Past versions of virus-markers-heatmap-1.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

Expression of cluster 12 markers genes (changed in TDP-HA expressing cells)

fs <- list(up = c("NPTX2", "FGF18", "TDP43-HA", "PCED1A", "MEF2A", "DYNC2H1", 
                   "APOE", "GADD45A", "BCAM", "DDIT3"),
           down = c("VGF", "SCG2", "GAP43", "C4orf48", "HINT1", "LY6H", 
                     "TUBA1A", "TMSB4X", "TUBB2B", "STMN2"))
fs <- lapply(fs, sapply, function(g)
    grep(pattern = paste0("\\.", g, "$"), rownames(sce), value = TRUE)
  )
fs[["up"]]["TDP43-HA"] <- "TDP43-HA"
fs <- lapply(fs, function(x) unlist(x[lengths(x) !=0]) )

gs <- gsub(".*\\.", "", unlist(fs))
ns <- vapply(fs, length, numeric(1))
ks <- rep.int(names(fs), ns)
labs <- lapply(fs, function(x) gsub(".*\\.", "",x))

Heatmap of cluster 12 markers

# compute cluster-marker means
ms_by_cluster <- lapply(fs, function(gs) vapply(cs_by_k, function(i)
        Matrix::rowMeans(logcounts(sce)[gs, i, drop = FALSE]), 
        numeric(length(gs))))
# prep. for plotting & scale b/w 0 and 1
mat <- do.call("rbind", ms_by_cluster)
mat <- muscat:::.scale(mat)
rownames(mat) <- gs
cols <- muscat:::.cluster_colors[seq_along(fs)]
cols <- setNames(cols, names(fs))
row_anno <- rowAnnotation(
    df = data.frame(label = factor(ks, levels = names(fs))),
    col = list(label = cols), gp = gpar(col = "white"))
# percentage of cells from each of the samples per cluster
sample_props <- prop.table(n_cells, margin = 1)
col_mat <- as.matrix(unclass(sample_props))
sample_cols <- c("#882255", "#11588A",  "#117733")
sample_cols <- setNames(sample_cols, colnames(col_mat))
col_anno <- HeatmapAnnotation(
    perc_sample = anno_barplot(col_mat, gp = gpar(fill = sample_cols), 
                               height = unit(2, "cm"),
                               border = FALSE),
    annotation_label = "fraction of sample\nin cluster",
    gap = unit(10, "points"))
col_lgd <- Legend(labels = names(sample_cols),
       title = "sample",
       legend_gp = gpar(fill = sample_cols))

hm <- Heatmap(mat,
    name = "scaled avg.\nexpression",
    col = viridis(10),
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_names_side = "left",
    column_title = "cluster_id",
    column_title_side = "bottom",
    column_names_side = "bottom",
    column_names_rot = 0, 
    column_names_centered = TRUE,
    rect_gp = gpar(col = "white"),
    left_annotation = row_anno,
    top_annotation = col_anno)
draw(hm, annotation_legend_list = list(col_lgd))

Past versions of cluster12-markers-heatmap-1.png

Version	Author	Date
d55a2c2	khembach	2021-05-19

In neuronal clusters:

## subset to neuronal clusters
## astrocytes: 7-8, 12
## small cluster with weird cells: 15
## low quality cells: 0-1
subs <- as.character(c(2:6, 9:11, 13:14))
cs_by_k_sub <- cs_by_k[subs]

# compute cluster-marker means
ms_by_cluster <- lapply(fs, function(gs) vapply(cs_by_k_sub, function(i)
        Matrix::rowMeans(logcounts(sce)[gs, i, drop = FALSE]), 
        numeric(length(gs))))
# prep. for plotting & scale b/w 0 and 1
mat <- do.call("rbind", ms_by_cluster)
mat <- muscat:::.scale(mat)
rownames(mat) <- gs
cols <- muscat:::.cluster_colors[seq_along(fs)]
cols <- setNames(cols, names(fs))
row_anno <- rowAnnotation(
    df = data.frame(label = factor(ks, levels = names(fs))),
    col = list(label = cols), gp = gpar(col = "white"))
# percentage of cells from each of the samples per cluster
sample_props <- prop.table(n_cells, margin = 1)
col_mat <- as.matrix(unclass(sample_props[subs,]))
sample_cols <- c("#882255", "#11588A",  "#117733")
sample_cols <- setNames(sample_cols, colnames(col_mat))
col_anno <- HeatmapAnnotation(
    perc_sample = anno_barplot(col_mat, gp = gpar(fill = sample_cols), 
                               height = unit(2, "cm"),
                               border = FALSE),
    annotation_label = "fraction of sample\nin cluster",
    gap = unit(10, "points"))
col_lgd <- Legend(labels = names(sample_cols),
       title = "sample",
       legend_gp = gpar(fill = sample_cols))

hm <- Heatmap(mat,
    name = "scaled avg.\nexpression",
    col = viridis(10),
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    row_names_side = "left",
    column_title = "cluster_id",
    column_title_side = "bottom",
    column_names_side = "bottom",
    column_names_rot = 0, 
    column_names_centered = TRUE,
    rect_gp = gpar(col = "white"),
    left_annotation = row_anno,
    top_annotation = col_anno)
draw(hm, annotation_legend_list = list(col_lgd))

Save cluster markers to RDS

saveRDS(scran_markers, file.path("output", "CH-test-03_scran_markers.rds"))
saveRDS(so, file.path("output", "CH-test-03-cluster-analysis_so.rds"))

Session information

sessionInfo()

R version 4.0.5 (2021-03-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/local/R/R-4.0.5/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.5/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] parallel  stats4    grid      stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] stringr_1.4.0               SeuratObject_4.0.1         
 [3] Seurat_4.0.1                scran_1.16.0               
 [5] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
 [7] DelayedArray_0.14.0         matrixStats_0.56.0         
 [9] Biobase_2.48.0              GenomicRanges_1.40.0       
[11] GenomeInfoDb_1.24.2         IRanges_2.22.2             
[13] S4Vectors_0.26.1            BiocGenerics_0.34.0        
[15] viridis_0.5.1               viridisLite_0.3.0          
[17] RColorBrewer_1.1-2          purrr_0.3.4                
[19] muscat_1.2.1                dplyr_1.0.2                
[21] ggplot2_3.3.2               cowplot_1.0.0              
[23] ComplexHeatmap_2.4.2        workflowr_1.6.2            

loaded via a namespace (and not attached):
  [1] reticulate_1.16           tidyselect_1.1.0         
  [3] lme4_1.1-23               RSQLite_2.2.0            
  [5] AnnotationDbi_1.50.1      htmlwidgets_1.5.1        
  [7] BiocParallel_1.22.0       Rtsne_0.15               
  [9] munsell_0.5.0             codetools_0.2-16         
 [11] ica_1.0-2                 statmod_1.4.34           
 [13] future_1.17.0             miniUI_0.1.1.1           
 [15] withr_2.4.1               colorspace_1.4-1         
 [17] knitr_1.29                ROCR_1.0-11              
 [19] tensor_1.5                listenv_0.8.0            
 [21] labeling_0.3              git2r_0.27.1             
 [23] GenomeInfoDbData_1.2.3    polyclip_1.10-0          
 [25] farver_2.0.3              bit64_0.9-7              
 [27] glmmTMB_1.0.2.1           rprojroot_1.3-2          
 [29] vctrs_0.3.4               generics_0.0.2           
 [31] xfun_0.15                 R6_2.4.1                 
 [33] doParallel_1.0.15         ggbeeswarm_0.6.0         
 [35] clue_0.3-57               rsvd_1.0.3               
 [37] locfit_1.5-9.4            spatstat.utils_2.1-0     
 [39] bitops_1.0-6              cachem_1.0.4             
 [41] promises_1.1.1            scales_1.1.1             
 [43] beeswarm_0.2.3            gtable_0.3.0             
 [45] globals_0.12.5            goftest_1.2-2            
 [47] rlang_0.4.10              genefilter_1.70.0        
 [49] GlobalOptions_0.1.2       splines_4.0.5            
 [51] TMB_1.7.16                lazyeval_0.2.2           
 [53] spatstat.geom_2.1-0       abind_1.4-5              
 [55] yaml_2.2.1                reshape2_1.4.4           
 [57] backports_1.1.9           httpuv_1.5.4             
 [59] tools_4.0.5               spatstat.core_2.1-2      
 [61] ellipsis_0.3.1            gplots_3.0.4             
 [63] ggridges_0.5.2            Rcpp_1.0.5               
 [65] plyr_1.8.6                progress_1.2.2           
 [67] zlibbioc_1.34.0           RCurl_1.98-1.3           
 [69] prettyunits_1.1.1         rpart_4.1-15             
 [71] deldir_0.2-10             pbapply_1.4-2            
 [73] GetoptLong_1.0.1          zoo_1.8-8                
 [75] ggrepel_0.8.2             cluster_2.1.0            
 [77] colorRamps_2.3            fs_1.5.0                 
 [79] variancePartition_1.18.2  magrittr_1.5             
 [81] data.table_1.12.8         scattermore_0.7          
 [83] lmerTest_3.1-2            circlize_0.4.10          
 [85] lmtest_0.9-37             RANN_2.6.1               
 [87] whisker_0.4               fitdistrplus_1.1-1       
 [89] hms_0.5.3                 patchwork_1.0.1          
 [91] mime_0.9                  evaluate_0.14            
 [93] xtable_1.8-4              pbkrtest_0.4-8.6         
 [95] XML_3.99-0.4              gridExtra_2.3            
 [97] shape_1.4.4               compiler_4.0.5           
 [99] scater_1.16.2             tibble_3.0.3             
[101] KernSmooth_2.23-17        crayon_1.3.4             
[103] minqa_1.2.4               htmltools_0.5.0          
[105] mgcv_1.8-31               later_1.1.0.1            
[107] tidyr_1.1.0               geneplotter_1.66.0       
[109] DBI_1.1.0                 MASS_7.3-51.6            
[111] rappdirs_0.3.1            boot_1.3-25              
[113] Matrix_1.3-3              gdata_2.18.0             
[115] igraph_1.2.5              pkgconfig_2.0.3          
[117] numDeriv_2016.8-1.1       spatstat.sparse_2.0-0    
[119] plotly_4.9.2.1            foreach_1.5.0            
[121] annotate_1.66.0           vipor_0.4.5              
[123] dqrng_0.2.1               blme_1.0-4               
[125] XVector_0.28.0            digest_0.6.25            
[127] sctransform_0.3.2         RcppAnnoy_0.0.18         
[129] spatstat.data_2.1-0       rmarkdown_2.3            
[131] leiden_0.3.3              uwot_0.1.10              
[133] edgeR_3.30.3              DelayedMatrixStats_1.10.1
[135] shiny_1.5.0               gtools_3.8.2             
[137] rjson_0.2.20              nloptr_1.2.2.2           
[139] lifecycle_1.0.0           nlme_3.1-148             
[141] jsonlite_1.7.2            BiocNeighbors_1.6.0      
[143] limma_3.44.3              pillar_1.4.6             
[145] lattice_0.20-41           fastmap_1.0.1            
[147] httr_1.4.2                survival_3.2-3           
[149] glue_1.4.2                png_0.1-7                
[151] iterators_1.0.12          bit_1.1-15.2             
[153] stringi_1.4.6             blob_1.2.1               
[155] DESeq2_1.28.1             BiocSingular_1.4.0       
[157] caTools_1.18.0            memoise_2.0.0            
[159] irlba_2.3.3               future.apply_1.6.0