Cluster annotation
Katharina Hembach
5/29/2020
Last updated: 2020-06-10
Checks: 6 1
Knit directory: neural_scRNAseq/
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- dim-red
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|---|---|---|---|---|
| Rmd | b6767a6 | khembach | 2020-06-10 | wflow_publish(“analysis/05-annotation.Rmd”, verbose = TRUE) |
| html | 419ac73 | khembach | 2020-06-09 | Build site. |
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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)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)))
| Version | Author | Date |
|---|---|---|
| a4d0e04 | khembach | 2020-05-29 |
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 are supposed to be in G1 phase.
# A list of cell cycle markers, from Tirosh et al, 2015, is loaded with Seurat. We can
# segregate this list into markers of G2/M phase and markers of S phase
(s_genes <- cc.genes$s.genes) [1] "MCM5" "PCNA" "TYMS" "FEN1" "MCM2" "MCM4"
[7] "RRM1" "UNG" "GINS2" "MCM6" "CDCA7" "DTL"
[13] "PRIM1" "UHRF1" "MLF1IP" "HELLS" "RFC2" "RPA2"
[19] "NASP" "RAD51AP1" "GMNN" "WDR76" "SLBP" "CCNE2"
[25] "UBR7" "POLD3" "MSH2" "ATAD2" "RAD51" "RRM2"
[31] "CDC45" "CDC6" "EXO1" "TIPIN" "DSCC1" "BLM"
[37] "CASP8AP2" "USP1" "CLSPN" "POLA1" "CHAF1B" "BRIP1"
[43] "E2F8" (g2m_genes <- cc.genes$g2m.genes) [1] "HMGB2" "CDK1" "NUSAP1" "UBE2C" "BIRC5" "TPX2" "TOP2A"
[8] "NDC80" "CKS2" "NUF2" "CKS1B" "MKI67" "TMPO" "CENPF"
[15] "TACC3" "FAM64A" "SMC4" "CCNB2" "CKAP2L" "CKAP2" "AURKB"
[22] "BUB1" "KIF11" "ANP32E" "TUBB4B" "GTSE1" "KIF20B" "HJURP"
[29] "CDCA3" "HN1" "CDC20" "TTK" "CDC25C" "KIF2C" "RANGAP1"
[36] "NCAPD2" "DLGAP5" "CDCA2" "CDCA8" "ECT2" "KIF23" "HMMR"
[43] "AURKA" "PSRC1" "ANLN" "LBR" "CKAP5" "CENPE" "CTCF"
[50] "NEK2" "G2E3" "GAS2L3" "CBX5" "CENPA" # match the marker genes to the features
m <- match(s_genes, str_split(rownames(GetAssayData(so, assay = "RNA")),
pattern = "\\.", simplify = TRUE)[,2])
s_genes <- rownames(GetAssayData(so, assay = "RNA"))[m]
s_genes <- s_genes[!is.na(s_genes)]
m <- match(g2m_genes, str_split(rownames(GetAssayData(so, assay = "RNA")),
pattern = "\\.", simplify = TRUE)[,2])
g2m_genes <- rownames(GetAssayData(so, assay = "RNA"))[m]
g2m_genes <- g2m_genes[!is.na(g2m_genes)]
# change the default assay to the raw counts
DefaultAssay(so) <- "RNA"
so <- CellCycleScoring(so, s.features = s_genes, g2m.features = g2m_genes,
set.ident = TRUE)
# change back to integrated
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")
}
Phase
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"))
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] Seurat_3.1.5 scran_1.16.0
[3] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[5] DelayedArray_0.14.0 matrixStats_0.56.0
[7] Biobase_2.48.0 GenomicRanges_1.40.0
[9] GenomeInfoDb_1.24.0 IRanges_2.22.2
[11] S4Vectors_0.26.1 BiocGenerics_0.34.0
[13] viridis_0.5.1 viridisLite_0.3.0
[15] RColorBrewer_1.1-2 purrr_0.3.4
[17] muscat_1.2.0 dplyr_0.8.5
[19] ggplot2_3.3.0 cowplot_1.0.0
[21] 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] RCurl_1.98-1.2 genefilter_1.70.0
[35] lme4_1.1-23 zoo_1.8-8
[37] ape_5.3 survival_3.1-12
[39] iterators_1.0.12 glue_1.4.1
[41] gtable_0.3.0 zlibbioc_1.34.0
[43] XVector_0.28.0 leiden_0.3.3
[45] GetoptLong_0.1.8 BiocSingular_1.4.0
[47] future.apply_1.5.0 shape_1.4.4
[49] scales_1.1.1 DBI_1.1.0
[51] edgeR_3.30.0 Rcpp_1.0.4.6
[53] xtable_1.8-4 progress_1.2.2
[55] clue_0.3-57 reticulate_1.16
[57] dqrng_0.2.1 bit_1.1-15.2
[59] rsvd_1.0.3 tsne_0.1-3
[61] htmlwidgets_1.5.1 httr_1.4.1
[63] gplots_3.0.3 ellipsis_0.3.1
[65] ica_1.0-2 farver_2.0.3
[67] pkgconfig_2.0.3 XML_3.99-0.3
[69] uwot_0.1.8 locfit_1.5-9.4
[71] labeling_0.3 tidyselect_1.1.0
[73] rlang_0.4.6 reshape2_1.4.4
[75] later_1.0.0 AnnotationDbi_1.50.0
[77] munsell_0.5.0 tools_4.0.0
[79] RSQLite_2.2.0 ggridges_0.5.2
[81] evaluate_0.14 stringr_1.4.0
[83] yaml_2.2.1 knitr_1.28
[85] bit64_0.9-7 fs_1.4.1
[87] fitdistrplus_1.1-1 caTools_1.18.0
[89] RANN_2.6.1 pbapply_1.4-2
[91] future_1.17.0 nlme_3.1-148
[93] whisker_0.4 pbkrtest_0.4-8.6
[95] compiler_4.0.0 plotly_4.9.2.1
[97] beeswarm_0.2.3 png_0.1-7
[99] variancePartition_1.18.0 tibble_3.0.1
[101] statmod_1.4.34 geneplotter_1.66.0
[103] stringi_1.4.6 lattice_0.20-41
[105] Matrix_1.2-18 nloptr_1.2.2.1
[107] vctrs_0.3.0 pillar_1.4.4
[109] lifecycle_0.2.0 lmtest_0.9-37
[111] GlobalOptions_0.1.1 RcppAnnoy_0.0.16
[113] BiocNeighbors_1.6.0 data.table_1.12.8
[115] bitops_1.0-6 irlba_2.3.3
[117] patchwork_1.0.0 httpuv_1.5.2
[119] colorRamps_2.3 R6_2.4.1
[121] promises_1.1.0 KernSmooth_2.23-17
[123] gridExtra_2.3 vipor_0.4.5
[125] codetools_0.2-16 boot_1.3-25
[127] MASS_7.3-51.6 gtools_3.8.2
[129] assertthat_0.2.1 DESeq2_1.28.1
[131] rprojroot_1.3-2 rjson_0.2.20
[133] withr_2.2.0 sctransform_0.2.1
[135] GenomeInfoDbData_1.2.3 hms_0.5.3
[137] tidyr_1.1.0 glmmTMB_1.0.1
[139] minqa_1.2.4 rmarkdown_2.1
[141] DelayedMatrixStats_1.10.0 Rtsne_0.15
[143] git2r_0.27.1 numDeriv_2016.8-1.1
[145] ggbeeswarm_0.6.0