Cluster annotation
Katharina Hembach
5/29/2020
Last updated: 2020-06-21
Checks: 7 0
Knit directory: neural_scRNAseq/
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| Rmd | f349423 | khembach | 2020-06-21 | regress out number of UMIs and perc mitochondrial features; cyclone |
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| Rmd | a8f31cd | khembach | 2020-06-12 | analyze known marker genes |
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| 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)
library(BiocParallel)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))Number 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
7 12 17
integrated_snn_res.0.8 integrated_snn_res.1 integrated_snn_res.1.2
23 26 30
integrated_snn_res.2
37 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 4224 4453 178 88 262 64
1 0 0 1573 1297 553 610
2 1202 1150 328 279 550 381
3 658 632 609 621 683 401
4 1430 1308 69 58 104 15
5 0 0 1065 870 418 431
6 2 0 917 747 796 271
7 1 4 623 644 559 784
8 12 9 672 463 879 451
9 0 0 884 747 337 374
10 3 2 712 663 269 301
11 247 245 187 264 250 208
12 435 467 117 104 144 112
13 0 0 373 268 170 181
14 34 50 148 146 52 91
15 0 0 183 137 76 97
16 83 88 49 42 87 21Relative 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 scoring with Seurat
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:
DefaultAssay(so) <- "RNA"
# 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] "ENSG00000012963.UBR7" "ENSG00000049541.RFC2"
[3] "ENSG00000051180.RAD51" "ENSG00000073111.MCM2"
[5] "ENSG00000075131.TIPIN" "ENSG00000076003.MCM6"
[7] "ENSG00000076248.UNG" "ENSG00000077514.POLD3"
[9] "ENSG00000092470.WDR76" "ENSG00000092853.CLSPN"
[11] "ENSG00000093009.CDC45" "ENSG00000094804.CDC6"
[13] "ENSG00000095002.MSH2" "ENSG00000100297.MCM5"
[15] "ENSG00000101868.POLA1" "ENSG00000104738.MCM4"
[17] "ENSG00000111247.RAD51AP1" "ENSG00000112312.GMNN"
[19] "ENSG00000117748.RPA2" "ENSG00000118412.CASP8AP2"
[21] "ENSG00000119969.HELLS" "ENSG00000129173.E2F8"
[23] "ENSG00000131153.GINS2" "ENSG00000132646.PCNA"
[25] "ENSG00000132780.NASP" "ENSG00000136492.BRIP1"
[27] "ENSG00000136982.DSCC1" "ENSG00000143476.DTL"
[29] "ENSG00000144354.CDCA7" "ENSG00000151725.CENPU"
[31] "ENSG00000156802.ATAD2" "ENSG00000159259.CHAF1B"
[33] "ENSG00000162607.USP1" "ENSG00000163950.SLBP"
[35] "ENSG00000167325.RRM1" "ENSG00000168496.FEN1"
[37] "ENSG00000171848.RRM2" "ENSG00000174371.EXO1"
[39] "ENSG00000175305.CCNE2" "ENSG00000176890.TYMS"
[41] "ENSG00000197299.BLM" "ENSG00000198056.PRIM1"
[43] "ENSG00000276043.UHRF1" 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"
[3] "ENSG00000013810.TACC3" "ENSG00000072571.HMMR"
[5] "ENSG00000075218.GTSE1" "ENSG00000080986.NDC80"
[7] "ENSG00000087586.AURKA" "ENSG00000088325.TPX2"
[9] "ENSG00000089685.BIRC5" "ENSG00000092140.G2E3"
[11] "ENSG00000094916.CBX5" "ENSG00000100401.RANGAP1"
[13] "ENSG00000102974.CTCF" "ENSG00000111665.CDCA3"
[15] "ENSG00000112742.TTK" "ENSG00000113810.SMC4"
[17] "ENSG00000114346.ECT2" "ENSG00000115163.CENPA"
[19] "ENSG00000117399.CDC20" "ENSG00000117650.NEK2"
[21] "ENSG00000117724.CENPF" "ENSG00000120802.TMPO"
[23] "ENSG00000123485.HJURP" "ENSG00000123975.CKS2"
[25] "ENSG00000126787.DLGAP5" "ENSG00000129195.PIMREG"
[27] "ENSG00000131747.TOP2A" "ENSG00000134222.PSRC1"
[29] "ENSG00000134690.CDCA8" "ENSG00000136108.CKAP2"
[31] "ENSG00000137804.NUSAP1" "ENSG00000137807.KIF23"
[33] "ENSG00000138160.KIF11" "ENSG00000138182.KIF20B"
[35] "ENSG00000138778.CENPE" "ENSG00000139354.GAS2L3"
[37] "ENSG00000142945.KIF2C" "ENSG00000143228.NUF2"
[39] "ENSG00000143401.ANP32E" "ENSG00000143815.LBR"
[41] "ENSG00000148773.MKI67" "ENSG00000157456.CCNB2"
[43] "ENSG00000158402.CDC25C" "ENSG00000164104.HMGB2"
[45] "ENSG00000169607.CKAP2L" "ENSG00000169679.BUB1"
[47] "ENSG00000170312.CDK1" "ENSG00000173207.CKS1B"
[49] "ENSG00000175063.UBE2C" "ENSG00000175216.CKAP5"
[51] "ENSG00000178999.AURKB" "ENSG00000184661.CDCA2"
[53] "ENSG00000188229.TUBB4B" "ENSG00000189159.JPT1" so <- CellCycleScoring(so, s.features = s_genes, g2m.features = g2m_genes,
set.ident = TRUE)
DefaultAssay(so) <- "integrated"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" "PTN" "NEFL" "VIM" "CKB" "TTYH1"
$`1`
[1] "SAMD11" "STMN2" "CRABP1" "ZFHX3" "HOXB8" "MT-CO3"
$`2`
[1] "SAMD11" "S100A10" "CLU" "VIM" "TAGLN" "CKB" "METRN"
$`3`
[1] "SAMD11" "C1orf61" "PTN" "CLU" "VIM" "TAGLN" "DLK1"
[8] "CKB" "METRN"
$`4`
[1] "HMGB2" "VIM" "CKB" "TOP2A"
$`5`
[1] "STMN2" "RTN1" "MEIS2" "HOXB9" "PCP4" "MT-ND3"
$`6`
[1] "SAMD11" "TAC1" "STMN2" "CRABP1" "ZFHX3" "HOXB8" "MT-CO2"
$`7`
[1] "C1orf61" "SPARCL1" "SPP1" "VIM" "GFAP" "HOXB9" "TTYH1"
$`8`
[1] "STMN2" "SNCG" "RTN1" "HOXB5" "ONECUT2"
$`9`
[1] "FOXP1" "FABP7" "STMN2" "PCP4"
$`10`
[1] "SAMD11" "BASP1" "ENC1" "SOX4" "STMN2" "HOXB8" "HOXB9"
[8] "DCX" "MT-RNR2"
$`11`
[1] "VGF" "NEFL" "STMN2" "ANXA1" "DDIT3" "HSP90AA1"
$`12`
[1] "PTN" "EIF4EBP1" "VIM" "CKB" "FTL"
$`13`
[1] "STMN2" "SNCG" "MPPED2" "PCP4"
$`14`
[1] "C1orf61" "HES6" "SOX2" "VIM" "CKB"
$`15`
[1] "TAC1" "STMN2"
$`16`
[1] "S100A11" "COL3A1" "COL1A1" sub <- sce[unique(unlist(gs)), ]
pbs <- aggregateData(sub, assay = "logcounts", by = "cluster_id", fun = "mean")
mat <- t(muscat:::.scale(assay(pbs)))
## remove the Ensembl ID from the gene names
colnames(mat) <- str_split(colnames(mat), pattern = "\\.", simplify = TRUE)[,2]
Heatmap(mat,
name = "scaled avg.\nexpression",
col = viridis(10),
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster_id",
rect_gp = gpar(col = "white"))
| Version | Author | Date |
|---|---|---|
| f116d0f | khembach | 2020-06-10 |
Known marker genes
## source file with list of known marker genes
source(file.path("data", "known_cell_type_markers.R"))
fs <- lapply(fs, sapply, function(g)
grep(pattern = paste0("\\.", g, "$"), 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] BiocParallel_1.22.0 RCurl_1.98-1.2
[3] stringr_1.4.0 Seurat_3.1.5
[5] scran_1.16.0 SingleCellExperiment_1.10.1
[7] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[9] matrixStats_0.56.0 Biobase_2.48.0
[11] GenomicRanges_1.40.0 GenomeInfoDb_1.24.0
[13] IRanges_2.22.2 S4Vectors_0.26.1
[15] BiocGenerics_0.34.0 viridis_0.5.1
[17] viridisLite_0.3.0 RColorBrewer_1.1-2
[19] purrr_0.3.4 muscat_1.2.0
[21] dplyr_0.8.5 ggplot2_3.3.0
[23] cowplot_1.0.0 ComplexHeatmap_2.4.2
[25] 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] listenv_0.8.0 scater_1.16.0
[11] digest_0.6.25 foreach_1.5.0
[13] htmltools_0.4.0 gdata_2.18.0
[15] lmerTest_3.1-2 magrittr_1.5
[17] memoise_1.1.0 cluster_2.1.0
[19] doParallel_1.0.15 ROCR_1.0-11
[21] limma_3.44.1 globals_0.12.5
[23] annotate_1.66.0 prettyunits_1.1.1
[25] colorspace_1.4-1 rappdirs_0.3.1
[27] ggrepel_0.8.2 blob_1.2.1
[29] xfun_0.14 jsonlite_1.6.1
[31] crayon_1.3.4 genefilter_1.70.0
[33] lme4_1.1-23 zoo_1.8-8
[35] ape_5.3 survival_3.1-12
[37] iterators_1.0.12 glue_1.4.1
[39] gtable_0.3.0 zlibbioc_1.34.0
[41] XVector_0.28.0 leiden_0.3.3
[43] GetoptLong_0.1.8 BiocSingular_1.4.0
[45] future.apply_1.5.0 shape_1.4.4
[47] scales_1.1.1 DBI_1.1.0
[49] edgeR_3.30.0 Rcpp_1.0.4.6
[51] xtable_1.8-4 progress_1.2.2
[53] clue_0.3-57 reticulate_1.16
[55] dqrng_0.2.1 bit_1.1-15.2
[57] rsvd_1.0.3 tsne_0.1-3
[59] htmlwidgets_1.5.1 httr_1.4.1
[61] gplots_3.0.3 ellipsis_0.3.1
[63] ica_1.0-2 farver_2.0.3
[65] pkgconfig_2.0.3 XML_3.99-0.3
[67] uwot_0.1.8 locfit_1.5-9.4
[69] labeling_0.3 tidyselect_1.1.0
[71] rlang_0.4.6 reshape2_1.4.4
[73] later_1.0.0 AnnotationDbi_1.50.0
[75] munsell_0.5.0 tools_4.0.0
[77] RSQLite_2.2.0 ggridges_0.5.2
[79] evaluate_0.14 yaml_2.2.1
[81] knitr_1.28 bit64_0.9-7
[83] fs_1.4.1 fitdistrplus_1.1-1
[85] caTools_1.18.0 RANN_2.6.1
[87] pbapply_1.4-2 future_1.17.0
[89] nlme_3.1-148 whisker_0.4
[91] pbkrtest_0.4-8.6 compiler_4.0.0
[93] plotly_4.9.2.1 beeswarm_0.2.3
[95] png_0.1-7 variancePartition_1.18.0
[97] tibble_3.0.1 statmod_1.4.34
[99] geneplotter_1.66.0 stringi_1.4.6
[101] lattice_0.20-41 Matrix_1.2-18
[103] nloptr_1.2.2.1 vctrs_0.3.0
[105] pillar_1.4.4 lifecycle_0.2.0
[107] lmtest_0.9-37 GlobalOptions_0.1.1
[109] RcppAnnoy_0.0.16 BiocNeighbors_1.6.0
[111] data.table_1.12.8 bitops_1.0-6
[113] irlba_2.3.3 patchwork_1.0.0
[115] httpuv_1.5.2 colorRamps_2.3
[117] R6_2.4.1 promises_1.1.0
[119] KernSmooth_2.23-17 gridExtra_2.3
[121] vipor_0.4.5 codetools_0.2-16
[123] boot_1.3-25 MASS_7.3-51.6
[125] gtools_3.8.2 assertthat_0.2.1
[127] DESeq2_1.28.1 rprojroot_1.3-2
[129] rjson_0.2.20 withr_2.2.0
[131] sctransform_0.2.1 GenomeInfoDbData_1.2.3
[133] hms_0.5.3 tidyr_1.1.0
[135] glmmTMB_1.0.1 minqa_1.2.4
[137] rmarkdown_2.1 DelayedMatrixStats_1.10.0
[139] Rtsne_0.15 git2r_0.27.1
[141] numDeriv_2016.8-1.1 ggbeeswarm_0.6.0