Cluster annotation
Katharina Hembach
5/29/2020
Last updated: 2020-06-15
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 47f9578 | khembach | 2020-06-15 | fix tabset |
| html | d04635a | khembach | 2020-06-12 | Build site. |
| Rmd | f376d76 | khembach | 2020-06-12 | add barplot with sample fraction to heatmap |
| html | 34b86b8 | khembach | 2020-06-12 | Build site. |
| Rmd | 38c33d8 | khembach | 2020-06-12 | do not print marker ids |
| html | 98d3f0d | khembach | 2020-06-12 | Build site. |
| Rmd | a8f31cd | khembach | 2020-06-12 | analyze known marker genes |
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| html | 419ac73 | khembach | 2020-06-09 | Build site. |
| html | a4d0e04 | khembach | 2020-05-29 | Build site. |
| Rmd | 97d5a52 | khembach | 2020-05-29 | cluster analysis |
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)
library(RCurl)Load data & convert to SCE
so <- readRDS(file.path("output", "so_04_clustering.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))Nb. of clusters by resolution
cluster_cols <- grep("res.[0-9]", colnames(colData(sce)), value = TRUE)
sapply(colData(sce)[cluster_cols], nlevels)integrated_snn_res.0.1 integrated_snn_res.0.2 integrated_snn_res.0.4
8 12 17
integrated_snn_res.0.8 integrated_snn_res.1 integrated_snn_res.1.2
24 29 31
integrated_snn_res.2
39 Cluster-sample counts
# set cluster IDs to resolution 0.4 clustering
so <- SetIdent(so, value = "integrated_snn_res.0.4")
so@meta.data$cluster_id <- Idents(so)
sce$cluster_id <- Idents(so)
(n_cells <- table(sce$cluster_id, sce$sample_id))
1NSC 2NSC 3NC52 4NC52 5NC96 6NC96
0 4850 5041 186 98 308 82
1 0 0 1552 1279 492 627
2 1037 1011 337 288 563 391
3 12 10 572 383 1806 372
4 1351 1221 69 56 76 16
5 0 0 1017 847 380 415
6 2 9 628 620 528 774
7 253 236 577 606 376 369
8 0 0 1007 867 250 285
9 0 0 924 764 327 379
10 688 716 130 121 188 119
11 3 3 582 524 211 248
12 0 0 365 281 186 235
13 0 0 339 247 141 174
14 1 1 205 260 210 194
15 51 70 148 153 64 89
16 83 90 49 44 83 24Relative cluster-abundances
fqs <- prop.table(n_cells, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
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(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 8)))
Cell cycle
We assign each cell a cell cycle scores and visualize them in the DR plots. We use known G2/M and S phase markers that come with the Seurat package. The markers are anticorrelated and cells that to not express the markers should be in G1 phase.
We compute cell cycle phase using the 2000 anchor genes of the integrated data.
# A list of cell cycle markers, from Tirosh et al, 2015
cc_file <- getURL("https://raw.githubusercontent.com/hbc/tinyatlas/master/cell_cycle/Homo_sapiens.csv")
cc_genes <- read.csv(text = cc_file)
# match the marker genes to the features
m <- match(cc_genes$geneID[cc_genes$phase == "S"],
str_split(rownames(GetAssayData(so)),
pattern = "\\.", simplify = TRUE)[,1])
s_genes <- rownames(GetAssayData(so))[m]
(s_genes <- s_genes[!is.na(s_genes)]) [1] "ENSG00000051180.RAD51" "ENSG00000092853.CLSPN"
[3] "ENSG00000093009.CDC45" "ENSG00000094804.CDC6"
[5] "ENSG00000111247.RAD51AP1" "ENSG00000112312.GMNN"
[7] "ENSG00000119969.HELLS" "ENSG00000131153.GINS2"
[9] "ENSG00000132646.PCNA" "ENSG00000143476.DTL"
[11] "ENSG00000151725.CENPU" "ENSG00000156802.ATAD2"
[13] "ENSG00000162607.USP1" "ENSG00000171848.RRM2"
[15] "ENSG00000176890.TYMS" m <- match(cc_genes$geneID[cc_genes$phase == "G2/M"],
str_split(rownames(GetAssayData(so)),
pattern = "\\.", simplify = TRUE)[,1])
g2m_genes <- rownames(GetAssayData(so))[m]
(g2m_genes <- g2m_genes[!is.na(g2m_genes)]) [1] "ENSG00000010292.NCAPD2" "ENSG00000011426.ANLN" "ENSG00000013810.TACC3"
[4] "ENSG00000072571.HMMR" "ENSG00000075218.GTSE1" "ENSG00000080986.NDC80"
[7] "ENSG00000087586.AURKA" "ENSG00000088325.TPX2" "ENSG00000089685.BIRC5"
[10] "ENSG00000111665.CDCA3" "ENSG00000112742.TTK" "ENSG00000113810.SMC4"
[13] "ENSG00000114346.ECT2" "ENSG00000115163.CENPA" "ENSG00000117399.CDC20"
[16] "ENSG00000117650.NEK2" "ENSG00000117724.CENPF" "ENSG00000123485.HJURP"
[19] "ENSG00000123975.CKS2" "ENSG00000126787.DLGAP5" "ENSG00000129195.PIMREG"
[22] "ENSG00000131747.TOP2A" "ENSG00000134222.PSRC1" "ENSG00000134690.CDCA8"
[25] "ENSG00000136108.CKAP2" "ENSG00000137804.NUSAP1" "ENSG00000137807.KIF23"
[28] "ENSG00000138160.KIF11" "ENSG00000138182.KIF20B" "ENSG00000138778.CENPE"
[31] "ENSG00000139354.GAS2L3" "ENSG00000142945.KIF2C" "ENSG00000143228.NUF2"
[34] "ENSG00000143401.ANP32E" "ENSG00000148773.MKI67" "ENSG00000157456.CCNB2"
[37] "ENSG00000158402.CDC25C" "ENSG00000164104.HMGB2" "ENSG00000169607.CKAP2L"
[40] "ENSG00000169679.BUB1" "ENSG00000170312.CDK1" "ENSG00000173207.CKS1B"
[43] "ENSG00000175063.UBE2C" "ENSG00000178999.AURKB" "ENSG00000184661.CDCA2"
[46] "ENSG00000188229.TUBB4B" "ENSG00000189159.JPT1" so <- CellCycleScoring(so, s.features = s_genes, g2m.features = g2m_genes,
set.ident = TRUE)DR colored by cluster ID
cs <- sample(colnames(so), 5e3)
.plot_dr <- function(so, dr, id)
DimPlot(so, cells = cs, 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", "group_id", "sample_id", "Phase")
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
group_id
sample_id
Find markers using scran
We identify candidate marker genes for each cluster that enable a separation of that group from all other groups.
scran_markers <- findMarkers(sce,
groups = sce$cluster_id, block = sce$sample_id,
direction = "up", lfc = 2, full.stats = TRUE)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.
gs <- lapply(scran_markers, function(u) rownames(u)[u$Top == 1])
## candidate cluster markers
lapply(gs, function(x) str_split(x, pattern = "\\.", simplify = TRUE)[,2])$`0`
[1] "SAMD11" "NEFL" "VIM" "CKB" "TTYH1"
$`1`
[1] "SAMD11" "STMN2" "CRABP1" "ZFHX3" "HOXB8" "MT-CO2"
$`2`
[1] "S100A10" "S100A11" "CLU" "VIM" "TAGLN" "CKB" "TPM1"
$`3`
[1] "SAMD11" "STMN2" "SNCG" "RTN1" "HOXB5" "ONECUT2" "PCP4"
[8] "MT-CO2"
$`4`
[1] "HMGB2" "VIM" "CKB" "TOP2A"
$`5`
[1] "STMN2" "RTN1" "MEIS2" "HOXB9" "PCP4" "MT-ND3"
$`6`
[1] "C1orf61" "SPP1" "FABP5" "VIM" "HOXB9" "TTYH1" "MT-ND4"
$`7`
[1] "CLU" "LY6H" "VIM" "TAGLN" "DLK1" "CKB" "METRN"
$`8`
[1] "SAMD11" "TAC1" "STMN2" "ZFHX3" "HOXB8" "LAMP5" "MT-CO2"
$`9`
[1] "SAMD11" "FOXP1" "STMN2" "PCP4"
$`10`
[1] "SAMD11" "VGF" "EIF4EBP1" "ANXA1" "VIM" "CKB" "FTL"
$`11`
[1] "ENC1" "STMN2" "NFIB" "HOXB8" "HOXB9" "MT-RNR2"
$`12`
[1] "TAC1" "STMN2"
$`13`
[1] "STMN2" "SNCG" "MPPED2" "PCP4"
$`14`
[1] "STMN2" "DDIT3"
$`15`
[1] "C1orf61" "HES6" "SOX2" "VIM" "CKB"
$`16`
[1] "S100A11" "COL3A1" "COL1A1" sub <- sce[unique(unlist(gs)), ]
pbs <- aggregateData(sub, assay = "logcounts", by = "cluster_id", fun = "mean")
mat <- t(muscat:::.scale(assay(pbs)))
## remove the Ensembl ID from the gene names
colnames(mat) <- str_split(colnames(mat), pattern = "\\.", simplify = TRUE)[,2]
Heatmap(mat,
name = "scaled avg.\nexpression",
col = viridis(10),
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster_id",
rect_gp = gpar(col = "white"))
| Version | Author | Date |
|---|---|---|
| f116d0f | khembach | 2020-06-10 |
Known marker genes
## source file with list of known marker genes
source(file.path("data", "known_cell_type_markers.R"))
fs <- lapply(fs, sapply, function(g)
grep(pattern = paste0("\\.", g, "$"), 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 mean marker-exprs. by cluster
# 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", "#CC6677", "#11588A", "#88CCEE", "#117733", "#44AA99")
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))
DR colored by marker expression
# downsample to 5000 cells
cs <- sample(colnames(sce), 5e3)
sub <- subset(so, cells = cs)
# 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(sub, 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")
}
Cluster annotation
Based on the plots we annotated the clusters: …
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.0/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.0/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] RCurl_1.98-1.2 stringr_1.4.0
[3] Seurat_3.1.5 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_1.24.0 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_1.2.0 dplyr_0.8.5
[21] ggplot2_3.3.0 cowplot_1.0.0
[23] ComplexHeatmap_2.4.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.1.7 circlize_0.4.9
[3] blme_1.0-4 igraph_1.2.5
[5] plyr_1.8.6 lazyeval_0.2.2
[7] TMB_1.7.16 splines_4.0.0
[9] BiocParallel_1.22.0 listenv_0.8.0
[11] scater_1.16.0 digest_0.6.25
[13] foreach_1.5.0 htmltools_0.4.0
[15] gdata_2.18.0 lmerTest_3.1-2
[17] magrittr_1.5 memoise_1.1.0
[19] cluster_2.1.0 doParallel_1.0.15
[21] ROCR_1.0-11 limma_3.44.1
[23] globals_0.12.5 annotate_1.66.0
[25] prettyunits_1.1.1 colorspace_1.4-1
[27] rappdirs_0.3.1 ggrepel_0.8.2
[29] blob_1.2.1 xfun_0.14
[31] jsonlite_1.6.1 crayon_1.3.4
[33] genefilter_1.70.0 lme4_1.1-23
[35] zoo_1.8-8 ape_5.3
[37] survival_3.1-12 iterators_1.0.12
[39] glue_1.4.1 gtable_0.3.0
[41] zlibbioc_1.34.0 XVector_0.28.0
[43] leiden_0.3.3 GetoptLong_0.1.8
[45] BiocSingular_1.4.0 future.apply_1.5.0
[47] shape_1.4.4 scales_1.1.1
[49] DBI_1.1.0 edgeR_3.30.0
[51] Rcpp_1.0.4.6 xtable_1.8-4
[53] progress_1.2.2 clue_0.3-57
[55] reticulate_1.16 dqrng_0.2.1
[57] bit_1.1-15.2 rsvd_1.0.3
[59] tsne_0.1-3 htmlwidgets_1.5.1
[61] httr_1.4.1 gplots_3.0.3
[63] ellipsis_0.3.1 ica_1.0-2
[65] farver_2.0.3 pkgconfig_2.0.3
[67] XML_3.99-0.3 uwot_0.1.8
[69] locfit_1.5-9.4 labeling_0.3
[71] tidyselect_1.1.0 rlang_0.4.6
[73] reshape2_1.4.4 later_1.0.0
[75] AnnotationDbi_1.50.0 munsell_0.5.0
[77] tools_4.0.0 RSQLite_2.2.0
[79] ggridges_0.5.2 evaluate_0.14
[81] yaml_2.2.1 knitr_1.28
[83] bit64_0.9-7 fs_1.4.1
[85] fitdistrplus_1.1-1 caTools_1.18.0
[87] RANN_2.6.1 pbapply_1.4-2
[89] future_1.17.0 nlme_3.1-148
[91] whisker_0.4 pbkrtest_0.4-8.6
[93] compiler_4.0.0 plotly_4.9.2.1
[95] beeswarm_0.2.3 png_0.1-7
[97] variancePartition_1.18.0 tibble_3.0.1
[99] statmod_1.4.34 geneplotter_1.66.0
[101] stringi_1.4.6 lattice_0.20-41
[103] Matrix_1.2-18 nloptr_1.2.2.1
[105] vctrs_0.3.0 pillar_1.4.4
[107] lifecycle_0.2.0 lmtest_0.9-37
[109] GlobalOptions_0.1.1 RcppAnnoy_0.0.16
[111] BiocNeighbors_1.6.0 data.table_1.12.8
[113] bitops_1.0-6 irlba_2.3.3
[115] patchwork_1.0.0 httpuv_1.5.2
[117] colorRamps_2.3 R6_2.4.1
[119] promises_1.1.0 KernSmooth_2.23-17
[121] gridExtra_2.3 vipor_0.4.5
[123] codetools_0.2-16 boot_1.3-25
[125] MASS_7.3-51.6 gtools_3.8.2
[127] assertthat_0.2.1 DESeq2_1.28.1
[129] rprojroot_1.3-2 rjson_0.2.20
[131] withr_2.2.0 sctransform_0.2.1
[133] GenomeInfoDbData_1.2.3 hms_0.5.3
[135] tidyr_1.1.0 glmmTMB_1.0.1
[137] minqa_1.2.4 rmarkdown_2.1
[139] DelayedMatrixStats_1.10.0 Rtsne_0.15
[141] git2r_0.27.1 numDeriv_2016.8-1.1
[143] ggbeeswarm_0.6.0