Annotation of cell identity after Lam et al. NSC integration
Katharina Hembach
7/6/2020
Last updated: 2020-08-07
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
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| Rmd | 7562682 | khembach | 2020-08-07 | adjust fig sizes |
| html | 875e3c5 | khembach | 2020-07-10 | Build site. |
| Rmd | 15a0ad2 | khembach | 2020-07-10 | compare cell cluster membership before and after NES integration; merge |
| html | a1ebb78 | khembach | 2020-07-08 | Build site. |
| Rmd | d8bd339 | khembach | 2020-07-08 | NSC integration with NES from Lam et al. |
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", "Lam-01-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) SCT_snn_res.0.8 RNA_snn_res.0.4 integrated_snn_res.0.1
0 7 5
integrated_snn_res.0.2 integrated_snn_res.0.4 integrated_snn_res.0.8
6 7 12
integrated_snn_res.1 integrated_snn_res.1.2 integrated_snn_res.2
14 17 24 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 NES
0 2896 3024 215
1 1770 1725 206
2 1669 1582 188
3 983 1042 88
4 564 600 19
5 412 401 22
6 37 34 30Relative 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 = 10)))
Distribution of NES subtypes per cluster
In the paper, they identified clusters that were specific for different cell types. For our analysis, we merge identical cell subtypes from the different cell lines.
levels(sce$cell_subtype_nes) [1] "Glia_progenitor" "Neural_prog_Proliferating_SAi2"
[3] "Neural_progenitor" "Neural_progenitor_Ctrl7"
[5] "Neural_progenitor_SAi2" "Neuroblast_Ctrl7"
[7] "Radial_Glia_progenitor" ## merge identical cell subtypes
levels(sce$cell_subtype_nes) <-
c("Glia_progenitor", "Neural_prog_Proliferating", "Neural_progenitor",
"Neural_progenitor", "Neural_progenitor", "Neuroblast",
"Radial_Glia_progenitor")
levels(sce$cell_subtype_nes) [1] "Glia_progenitor" "Neural_prog_Proliferating"
[3] "Neural_progenitor" "Neuroblast"
[5] "Radial_Glia_progenitor" (n_types <- table(sce$cluster_id, sce$cell_subtype_nes))
Glia_progenitor Neural_prog_Proliferating Neural_progenitor Neuroblast
0 44 13 121 2
1 26 62 103 0
2 33 20 113 2
3 43 7 38 0
4 15 1 3 0
5 7 4 11 0
6 0 2 8 18
Radial_Glia_progenitor
0 35
1 15
2 20
3 0
4 0
5 0
6 2fqs <- prop.table(n_types, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster_id",
column_title = "sample_id",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 10)))
DR colored by cluster ID
.plot_dr <- function(so, dr, id)
DimPlot(so, group.by = id, reduction = dr, pt.size = 0.4) +
guides(col = guide_legend(nrow = 11,
override.aes = list(size = 3, alpha = 1))) +
theme_void() + theme(aspect.ratio = 1)
ids <- c("cluster_id", "group_id", "sample_id")
for (id in ids) {
cat("## ", id, "\n")
p1 <- .plot_dr(so, "tsne", id)
lgd <- get_legend(p1)
p1 <- p1 + theme(legend.position = "none")
p2 <- .plot_dr(so, "umap", id) + theme(legend.position = "none")
ps <- plot_grid(plotlist = list(p1, p2), nrow = 1)
p <- plot_grid(ps, lgd, nrow = 1, rel_widths = c(1, 0.2))
print(p)
cat("\n\n")
}
Cluster markers from Lam et al.
Similar to figure 2f in paper.
## source file with list of known marker genes
source(file.path("data", "Lam_figure2_markers.R"))
fs <- lapply(fs, sapply, function(g)
grep(pattern = paste0("\\.", g, "$"), 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")
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))
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_1.24.2
[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_1.2.1
[21] dplyr_1.0.0 ggplot2_3.3.2
[23] cowplot_1.0.0 ComplexHeatmap_2.4.2
[25] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.1.8 circlize_0.4.10
[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_1.16.2
[11] digest_0.6.25 foreach_1.5.0
[13] htmltools_0.5.0 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_3.44.3 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_0.15 jsonlite_1.7.0
[31] crayon_1.3.4 genefilter_1.70.0
[33] lme4_1.1-23 zoo_1.8-8
[35] ape_5.4 survival_3.2-3
[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_1.0.1 BiocSingular_1.4.0
[45] future.apply_1.6.0 shape_1.4.4
[47] scales_1.1.1 DBI_1.1.0
[49] edgeR_3.30.3 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_3.0.4 ellipsis_0.3.1
[63] ica_1.0-2 farver_2.0.3
[65] pkgconfig_2.0.3 XML_3.99-0.4
[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_1.1.0.1 AnnotationDbi_1.50.1
[75] munsell_0.5.0 tools_4.0.0
[77] generics_0.0.2 RSQLite_2.2.0
[79] ggridges_0.5.2 evaluate_0.14
[81] yaml_2.2.1 knitr_1.29
[83] bit64_0.9-7 fs_1.4.2
[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_1.18.2 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_1.2.2.2
[105] vctrs_0.3.1 pillar_1.4.4
[107] lifecycle_0.2.0 lmtest_0.9-37
[109] GlobalOptions_0.1.2 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_1.0.1 httpuv_1.5.4
[117] colorRamps_2.3 R6_2.4.1
[119] promises_1.1.1 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] 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_1.0.2.1 minqa_1.2.4
[137] rmarkdown_2.3 DelayedMatrixStats_1.10.1
[139] Rtsne_0.15 git2r_0.27.1
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