Annotation of cell identity after Lam et al. NSC integration
Katharina Hembach
7/6/2020
Last updated: 2021-08-30
Checks: 7 0
Knit directory: neural_scRNAseq/
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Lam-02-NSC_annotation.Rmd) and HTML (docs/Lam-02-NSC_annotation.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 | f3e4c6b | khembach | 2021-08-30 | remove warnings |
| html | 4c53085 | khembach | 2021-08-27 | Build site. |
| Rmd | 2da9ece | khembach | 2021-08-27 | explore data integration with CellMixS |
| html | 7fcea2b | khembach | 2021-05-26 | Build site. |
| Rmd | 5ebe77e | khembach | 2021-05-26 | change color of NES and iCoMoNSCs in DR |
| html | 489b5df | khembach | 2021-04-06 | Build site. |
| Rmd | d76adbd | khembach | 2021-04-06 | update heatmaps |
| html | d515c70 | khembach | 2020-08-19 | Build site. |
| Rmd | eb3e64d | khembach | 2020-08-19 | split NES into cell lines |
| html | e659e63 | khembach | 2020-08-07 | Build site. |
| 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)
library(CellMixS)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 <- 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)))
We split the cells from Lam et al. into the three different cell lines that they used in the paper.
ind <- which(sce$sample_id == "NES")
cell_label <- sce$sample_id
levels(cell_label) <- c(levels(cell_label), levels(sce$Cell_line))
cell_label[ind] <- sce$Cell_line[ind]
cell_label <- droplevels(cell_label)
levels(cell_label)[levels(cell_label)==".SAi2"] <- "SAi2"
so$cell_label <- cell_label
(n_cells_line <- table(sce$cluster_id, cell_label)) cell_label
1NSC 2NSC SAi2 AF22 Ctrl7
0 2896 3024 51 67 97
1 1770 1725 76 44 86
2 1669 1582 80 46 62
3 983 1042 34 41 13
4 564 600 2 13 4
5 412 401 10 12 0
6 37 34 3 3 24fqs <- prop.table(n_cells_line, 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 = "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)))
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$cell_subtype_nes))
Glia_progenitor Neural_prog_Proliferating Neural_progenitor Neuroblast
0 44 13 121 2
1 26 62 103 0
2 33 20 113 2
3 43 7 38 0
4 15 1 3 0
5 7 4 11 0
6 0 2 8 18
Radial_Glia_progenitor
0 35
1 15
2 20
3 0
4 0
5 0
6 2fqs <- prop.table(n_types, margin = 2)
mat <- round(as.matrix(unclass(fqs))*100, 2)
Heatmap(mat,
col = colfunc(10),
name = "Percentage\nof cells",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster ID",
column_title = "sample ID",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(mat[j, i], x = x, y = y,
gp = gpar(col = "black", fontsize = 10)))
DR colored by cluster ID
.plot_dr <- function(so, dr, id)
DimPlot(so, group.by = id, reduction = dr, pt.size = 0.4) +
guides(col = guide_legend(nrow = 11,
override.aes = list(size = 3, alpha = 1))) +
theme_void() + theme(aspect.ratio = 1)
ids <- c("cluster_id", "group_id", "sample_id", "cell_label")
for (id in ids) {
cat("## ", id, "\n")
p1 <- .plot_dr(so, "tsne", id)
p2 <- .plot_dr(so, "umap", id)
if(id == "group_id") {
p1 <- p1 + scale_color_manual(values = c("springgreen3", "darkmagenta"))
p2 <- p2 + scale_color_manual(values = c("springgreen3", "darkmagenta"))
}
lgd <- get_legend(p1)
p1 <- p1 + theme(legend.position = "none")
p2 <- p2 + theme(legend.position = "none")
ps <- plot_grid(plotlist = list(p1, p2), nrow = 1)
p <- plot_grid(ps, lgd, nrow = 1, rel_widths = c(1, 0.2))
print(p)
cat("\n\n")
}cluster_id
group_id
sample_id
Cluster markers from Lam et al.
Similar to figure 2f in paper.
## source file with list of known marker genes
source(file.path("data", "Lam_figure2_markers.R"))
fs <- lapply(fs, sapply, function(g)
grep(pattern = paste0("\\.", g, "$"), 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))
Explore data integration with CellMixS
We use the CellMixS Bioconductor R package to evaluate the data integration and potential batch effects. We test how well the two dataset are mixing or if there are batch effect with the Cellspecific Mixing Score (CMS), a test for batch effects within k-nearest neighbouring cells. A high cms score refers to good mixing, while a low score indicates batch-specific bias. The test considers differences in the number of cells from each batch.
sce$group_id %>% table.
NES P22
768 16739 ## using PCA based on integrated and scaled data
## we set k high but below the size of the smallest group
## because we want to evaluate global structures
## k_min is used to define the minimum size of the local neighbourhoods
sce <- cms(sce, k = 700, k_min = 200, group = "group_id", dim_red = "PCA",
n_dim = 10, unbalanced = TRUE,
BPPARAM = MulticoreParam(workers = 15))
head(colData(sce)[,c("cms_smooth", "cms")])DataFrame with 6 rows and 2 columns
cms_smooth cms
<numeric> <numeric>
AAACCCAAGGTTATAG-1.1NSC 0.415851 0.66510000
AAACCCACATTGACCA-1.1NSC 0.346162 0.00317485
AAACCCAGTAGCGCCT-1.1NSC NA NA
AAACCCAGTATTTCTC-1.1NSC 0.403475 0.99411500
AAACCCAGTTACACTG-1.1NSC 0.469112 0.75650500
AAACGAAAGACAGCGT-1.1NSC 0.425609 0.06196850## cms histogram
visHist(sce)
| Version | Author | Date |
|---|---|---|
| 4c53085 | khembach | 2021-08-27 |
p1 <- visMetric(sce, metric_var = "cms_smooth", dim_red = "UMAP") +
theme_void() + theme(aspect.ratio = 1)
p2 <- visMetric(sce, metric_var = "cms", dim_red = "UMAP") +
theme_void() + theme(aspect.ratio = 1)
plot_grid(p1, p2)
| Version | Author | Date |
|---|---|---|
| 4c53085 | khembach | 2021-08-27 |
## score distribution per cluster
p1 <- visCluster(sce, metric_var = "cms", cluster_var = "cluster_id") +
scale_fill_hue() +
scale_y_discrete(limits = rev(unique(sort(sce$cluster_id))))
p2 <- visCluster(sce, metric_var = "cms_smooth", cluster_var = "cluster_id") +
scale_fill_hue() +
scale_y_discrete(limits = rev(unique(sort(sce$cluster_id))))
plot_grid(p1, p2)
| Version | Author | Date |
|---|---|---|
| 4c53085 | khembach | 2021-08-27 |
We also test how well the two datasets are integrated with the Local Density Differences (ldfDiff) metric. In an optimal case relative densities (according to the same set of cells) should not change by integration and the ldfDiff score should be close to 0. In general the overall distribution of ldfDiff should be centered around 0 without long tails.
sce_int <- as.SingleCellExperiment(so, assay = "integrated")
colData(sce_int) <- as.data.frame(colData(sce_int)) %>%
mutate_if(is.character, as.factor) %>%
DataFrame(row.names = colnames(sce_int))
sce_pre_list <- list("P22" = sce[,sce$group_id == "P22"],
"NES" = sce[,sce$group_id == "NES"])
## remove dimension reduction from integrated data
sce_pre_list <- lapply(sce_pre_list, function(x) {reducedDims(x) <- NULL; x})
sce_int <- ldfDiff(sce_pre_list, sce_combined = sce_int, group = "group_id",
k = 7, dim_red = "PCA", dim_combined = "PCA",
assay_pre = "logcounts", assay_combined = "logcounts",
n_dim = 3, res_name = "Seurat")
visIntegration(sce_int, metric = "diff_ldf", metric_name = "ldfDiff") 
| Version | Author | Date |
|---|---|---|
| 4c53085 | khembach | 2021-08-27 |
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] CellMixS_1.4.2 kSamples_1.2-9
[3] SuppDists_1.1-9.5 BiocParallel_1.22.0
[5] RCurl_1.98-1.3 stringr_1.4.0
[7] SeuratObject_4.0.1 Seurat_4.0.1
[9] scran_1.16.0 SingleCellExperiment_1.10.1
[11] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[13] matrixStats_0.56.0 Biobase_2.48.0
[15] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[17] IRanges_2.22.2 S4Vectors_0.26.1
[19] BiocGenerics_0.34.0 viridis_0.5.1
[21] viridisLite_0.3.0 RColorBrewer_1.1-2
[23] purrr_0.3.4 muscat_1.2.1
[25] dplyr_1.0.2 ggplot2_3.3.2
[27] cowplot_1.0.0 ComplexHeatmap_2.4.2
[29] 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] Rtsne_0.15 munsell_0.5.0
[9] codetools_0.2-16 ica_1.0-2
[11] statmod_1.4.34 future_1.17.0
[13] miniUI_0.1.1.1 withr_2.4.1
[15] colorspace_1.4-1 knitr_1.29
[17] ROCR_1.0-11 tensor_1.5
[19] listenv_0.8.0 labeling_0.3
[21] git2r_0.27.1 GenomeInfoDbData_1.2.3
[23] polyclip_1.10-0 farver_2.0.3
[25] bit64_0.9-7 glmmTMB_1.0.2.1
[27] rprojroot_1.3-2 vctrs_0.3.4
[29] generics_0.0.2 xfun_0.15
[31] R6_2.4.1 doParallel_1.0.15
[33] ggbeeswarm_0.6.0 clue_0.3-57
[35] rsvd_1.0.3 locfit_1.5-9.4
[37] spatstat.utils_2.1-0 bitops_1.0-6
[39] cachem_1.0.4 promises_1.1.1
[41] scales_1.1.1 beeswarm_0.2.3
[43] gtable_0.3.0 globals_0.12.5
[45] goftest_1.2-2 rlang_0.4.10
[47] genefilter_1.70.0 GlobalOptions_0.1.2
[49] splines_4.0.5 TMB_1.7.16
[51] lazyeval_0.2.2 spatstat.geom_2.1-0
[53] abind_1.4-5 yaml_2.2.1
[55] reshape2_1.4.4 backports_1.1.9
[57] httpuv_1.5.4 tools_4.0.5
[59] spatstat.core_2.1-2 ellipsis_0.3.1
[61] gplots_3.0.4 ggridges_0.5.2
[63] Rcpp_1.0.5 plyr_1.8.6
[65] progress_1.2.2 zlibbioc_1.34.0
[67] prettyunits_1.1.1 rpart_4.1-15
[69] deldir_0.2-10 pbapply_1.4-2
[71] GetoptLong_1.0.1 zoo_1.8-8
[73] ggrepel_0.8.2 cluster_2.1.0
[75] colorRamps_2.3 fs_1.5.0
[77] variancePartition_1.18.2 magrittr_1.5
[79] data.table_1.12.8 scattermore_0.7
[81] lmerTest_3.1-2 circlize_0.4.10
[83] lmtest_0.9-37 RANN_2.6.1
[85] whisker_0.4 fitdistrplus_1.1-1
[87] hms_0.5.3 patchwork_1.0.1
[89] mime_0.9 evaluate_0.14
[91] xtable_1.8-4 pbkrtest_0.4-8.6
[93] XML_3.99-0.4 gridExtra_2.3
[95] shape_1.4.4 compiler_4.0.5
[97] scater_1.16.2 tibble_3.0.3
[99] KernSmooth_2.23-17 crayon_1.3.4
[101] minqa_1.2.4 htmltools_0.5.0
[103] mgcv_1.8-31 later_1.1.0.1
[105] tidyr_1.1.0 geneplotter_1.66.0
[107] DBI_1.1.0 MASS_7.3-51.6
[109] rappdirs_0.3.1 boot_1.3-25
[111] Matrix_1.3-3 gdata_2.18.0
[113] igraph_1.2.5 pkgconfig_2.0.3
[115] numDeriv_2016.8-1.1 spatstat.sparse_2.0-0
[117] plotly_4.9.2.1 foreach_1.5.0
[119] annotate_1.66.0 vipor_0.4.5
[121] dqrng_0.2.1 blme_1.0-4
[123] XVector_0.28.0 digest_0.6.25
[125] sctransform_0.3.2 RcppAnnoy_0.0.18
[127] spatstat.data_2.1-0 rmarkdown_2.3
[129] leiden_0.3.3 uwot_0.1.10
[131] edgeR_3.30.3 DelayedMatrixStats_1.10.1
[133] shiny_1.5.0 gtools_3.8.2
[135] rjson_0.2.20 nloptr_1.2.2.2
[137] lifecycle_1.0.0 nlme_3.1-148
[139] jsonlite_1.7.2 BiocNeighbors_1.6.0
[141] limma_3.44.3 pillar_1.4.6
[143] lattice_0.20-41 fastmap_1.0.1
[145] httr_1.4.2 survival_3.2-3
[147] glue_1.4.2 png_0.1-7
[149] iterators_1.0.12 bit_1.1-15.2
[151] stringi_1.4.6 blob_1.2.1
[153] DESeq2_1.28.1 BiocSingular_1.4.0
[155] caTools_1.18.0 memoise_2.0.0
[157] irlba_2.3.3 future.apply_1.6.0