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Knit directory: neural_scRNAseq/
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Rmd | abe855b | khembach | 2021-06-02 | analyse UNC13A gene expression |
html | f73267a | khembach | 2021-05-26 | Build site. |
Rmd | a4e53b8 | khembach | 2021-05-26 | change color of timeline in DR |
html | 18ea633 | khembach | 2021-04-07 | Build site. |
Rmd | 000bc40 | khembach | 2021-04-07 | fix heatmap |
html | 119d6d9 | khembach | 2021-04-07 | Build site. |
Rmd | 3c93c4e | khembach | 2021-04-07 | change plot layout |
html | e7f56c5 | khembach | 2021-04-07 | Build site. |
Rmd | db1e648 | khembach | 2021-04-07 | update heatmap, find NSC marker and UMAP with marker expression |
html | 06d61f5 | khembach | 2021-01-28 | Build site. |
Rmd | c122306 | khembach | 2021-01-28 | add heatmap with virus cell tropism markers |
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Rmd | 68d329d | khembach | 2020-11-10 | analyse clusters of all timepoints |
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)
so <- readRDS(file.path("output", "so_06-clustering_all_timepoints.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))
cluster_cols <- grep("res.[0-9]", colnames(colData(sce)), value = TRUE)
sapply(colData(sce)[cluster_cols], nlevels)
RNA_snn_res.0.2 RNA_snn_res.0.4 RNA_snn_res.0.8 RNA_snn_res.1
13 19 26 29
# set cluster IDs to resolution 0.4 clustering
so <- SetIdent(so, value = "RNA_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 NC223a NC223b
0 4567 4622 2 3 0 1 12 4
1 3397 3407 0 1 0 0 5 2
2 0 0 2932 2497 121 180 11 15
3 0 0 3173 2471 29 49 5 4
4 0 0 1 2 11 8 1727 2577
5 0 0 1 4 115 151 1348 2172
6 0 0 38 25 1196 1865 62 47
7 10 7 1 0 673 369 773 688
8 0 0 1 0 4 5 867 1447
9 0 0 3 33 836 1326 14 3
10 0 0 1044 949 17 20 13 3
11 0 0 332 276 356 368 72 5
12 0 0 646 637 3 8 0 0
13 0 0 299 230 87 188 104 227
14 357 372 0 1 0 0 0 0
15 0 0 112 218 46 36 20 22
16 0 0 0 0 10 2 315 123
17 0 0 49 43 34 19 1 24
18 0 0 53 48 0 0 1 0
so$group_id <- factor(so$group_id, levels = c("P22", "D52", "D96", "D223"))
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 = 8)))
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:
# 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)
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)
p2 <- .plot_dr(so, "umap", id)
if(id == "group_id") {
p1 <- p1 + scale_color_manual(values = c("darkmagenta", "#7BAFDE", "#1965B0", "midnightblue"))
p2 <- p2 + scale_color_manual(values = c("darkmagenta", "#7BAFDE", "#1965B0", "midnightblue"))
}
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")
}
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)
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" "GNG5" "IGFBP5" "GNG11" "PEG10" "PTN" "NEFL"
[8] "STMN2" "VIM" "METRN" "PCSK1N" "RPS4X" "MT-RNR2" "MT-CO2"
[15] "MT-CYB"
$`1`
[1] "SAMD11" "STMN1" "PTN" "CRH" "GFAP" "PCSK1N" "MT-CO2" "MT-CO3"
$`2`
[1] "VGF" "NEFM" "NEFL" "STMN2" "RTN1" "HOXB8" "HOXB9" "MT-CO2"
[9] "MT-CO3"
$`3`
[1] "SAMD11" "STMN2" "PCDH9" "RTN1" "ZFHX3" "PCP4" "MT-CO2"
$`4`
[1] "SPARCL1" "STMN2" "VIM" "RTN1" "GFAP" "MT-ND1" "MT-ATP6"
$`5`
[1] "S100A10" "IGFBP5" "VGF" "STMN2" "VAMP2" "MT-RNR2"
$`6`
[1] "PKIB" "FABP7" "STMN2" "PCP4" "MT-RNR2"
$`7`
[1] "VGF" "CRH" "STMN2" "CRABP1"
$`8`
[1] "SPARCL1" "VGF" "PTPRZ1" "NTRK2" "PTGDS" "MT-ND1" "MT-ATP6"
$`9`
[1] "C1orf61" "IGFBP2" "VIM" "CRYAB" "DLK1" "GFAP" "MT-ND1"
$`10`
[1] "C1orf61" "SQSTM1" "VIM" "CYP26A1" "ATP1B2" "HOXB9" "PPP1R15A"
[8] "FTL"
$`11`
[1] "S100A10" "IGFBP5" "IGFBP7" "CCN2" "CLU" "VIM" "TAGLN"
[8] "METRN" "MT-CYB"
$`12`
[1] "C1orf61" "PTPRZ1" "CLU" "LY6H" "NTRK2" "VIM" "METRN"
[8] "C1QL1" "TTYH1" "S100B" "MT-ND4"
$`13`
[1] "S100A10" "TAC1" "VGF" "STMN2" "DLK1" "MT-CO2"
$`14`
[1] "S100A11"
$`15`
[1] "GADD45A" "VGF" "STMN2" "CRYAB" "DDIT3"
$`16`
[1] "C1orf61" "VGF" "PTGDS" "VIM" "IFITM3" "CRYAB" "TAGLN"
[8] "GAPDH" "CKB" "METRN" "C1QL1"
$`17`
[1] "COL3A1" "MGP" "COL1A1"
$`18`
[1] "H2AFZ" "HMGB2" "PTTG1" "VIM" "CKB" "METRN"
sub <- sce[unique(unlist(gs)), ]
pbs <- aggregateData(sub, assay = "logcounts", by = "cluster_id", fun = "mean")
mat <- t(muscat:::.scale(assay(pbs)))
## remove the Ensembl ID from the gene names
colnames(mat) <- str_split(colnames(mat), pattern = "\\.", simplify = TRUE)[,2]
Heatmap(mat,
name = "scaled avg.\nexpression",
col = viridis(10),
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster_id",
rect_gp = gpar(col = "white"))
## source file with list of known marker genes
source(file.path("data", "known_cell_type_markers.R"))
fs <- lapply(fs, sapply, function(g)
grep(pattern = paste0("\\.", g, "$"), 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))
# 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", "#FF8E21", "#FDC78E")
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))
# 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")
}
## source file with list of known marker genes
source(file.path("data", "virus_cell_tropism_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))
# 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", "#FF8E21", "#FDC78E")
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))
We want to find more markers specific for the NSCs:
## markers specific for cluster 0, 1 or 14 (NSC) versus all other clusters
scran_markers2 <- findMarkers(sce, groups = sce$cluster_id,
direction = "up", lfc = 2, pval.type="all")
scran_markers2[["0"]][1:10,1:3]
DataFrame with 10 rows and 3 columns
p.value FDR summary.logFC
<numeric> <numeric> <numeric>
ENSG00000187634.SAMD11 1 1 0.000319104
ENSG00000188976.NOC2L 1 1 -0.015896575
ENSG00000187961.KLHL17 1 1 -0.004873865
ENSG00000187583.PLEKHN1 1 1 -0.000151055
ENSG00000187642.PERM1 1 1 -0.000231498
ENSG00000188290.HES4 1 1 -0.003824213
ENSG00000187608.ISG15 1 1 0.028549600
ENSG00000188157.AGRN 1 1 0.007498090
ENSG00000237330.RNF223 1 1 0.000361614
ENSG00000131591.C1orf159 1 1 -0.010512566
scran_markers2[["1"]][1:10,1:3]
DataFrame with 10 rows and 3 columns
p.value FDR summary.logFC
<numeric> <numeric> <numeric>
ENSG00000187634.SAMD11 1 1 -0.000319104
ENSG00000188976.NOC2L 1 1 0.015896575
ENSG00000187961.KLHL17 1 1 0.004873865
ENSG00000187583.PLEKHN1 1 1 0.000151055
ENSG00000187642.PERM1 1 1 0.000231498
ENSG00000188290.HES4 1 1 0.003824213
ENSG00000187608.ISG15 1 1 -0.028549600
ENSG00000188157.AGRN 1 1 -0.007498090
ENSG00000237330.RNF223 1 1 -0.000361614
ENSG00000131591.C1orf159 1 1 0.010512566
scran_markers2[["14"]][1:10,1:3]
DataFrame with 10 rows and 3 columns
p.value FDR summary.logFC
<numeric> <numeric> <numeric>
ENSG00000149591.TAGLN 1 1 1.363236
ENSG00000140416.TPM1 1 1 1.585896
ENSG00000122786.CALD1 1 1 0.478189
ENSG00000181019.NQO1 1 1 1.443555
ENSG00000198467.TPM2 1 1 0.961618
ENSG00000182718.ANXA2 1 1 0.847048
ENSG00000160789.LMNA 1 1 1.007836
ENSG00000163191.S100A11 1 1 -0.328324
ENSG00000122585.NPY 1 1 0.561380
ENSG00000168542.COL3A1 1 1 -1.969313
## we selected following markers
g <- c("NQO1", "TPM1", "PEG10")
genes <- lapply(g, sapply, function(i)
grep(pattern = paste0("\\.", i, "$"), rownames(sce), value = TRUE)
)
names(genes) <- g
# UMAPs colored by marker-expression
for (m in seq_along(genes)) {
p <- FeaturePlot(sub, features = genes[[m]], reduction = "umap", pt.size = 0.4) +
theme(aspect.ratio = 1, legend.position = "none") +
ggtitle(names(genes)[[m]]) + theme_void() + theme(aspect.ratio = 1)
print(p)
}
Version | Author | Date |
---|---|---|
e7f56c5 | khembach | 2021-04-07 |
Version | Author | Date |
---|---|---|
e7f56c5 | khembach | 2021-04-07 |
Version | Author | Date |
---|---|---|
e7f56c5 | khembach | 2021-04-07 |
We plot the gene expression of some selected markers for the paper.
# SOX2 and NQO1 (NSCs), GFAP (astrocytic), PTPRZ1 (OPCs), DCN (pericytes),
# SYP (neuronal), SLC32A1 (GABAergic) or SLC17A6 (Glutamatergic)
g <- c("SOX2","NQO1", "GFAP", "PTPRZ1", "DCN", "SYP", "SLC32A1", "SLC17A6")
genes <- lapply(g, sapply, function(i)
grep(pattern = paste0("\\.", i, "$"), rownames(sce), value = TRUE)
)
names(genes) <- g
ps <- lapply(names(genes)[1:2], function(i) {
FeaturePlot(sub, features = genes[[i]], reduction = "umap", pt.size = 0.4,
max.cutoff = 3) +
ggtitle(i) + theme_void() + theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
})
lgd1 <- get_legend(ps[[1]])
ps <- lapply(ps, function(x) x + theme(legend.position = "none"))
ps1 <- plot_grid(plotlist = ps, nrow = 1)
p1 <- plot_grid(ps1, lgd1, nrow = 1, label_size = 10, rel_widths = c(1, 0.1))
ps <- lapply(names(genes)[3:5], function(i) {
FeaturePlot(sub, features = genes[[i]], reduction = "umap", pt.size = 0.4,
min.cutoff = 0, max.cutoff = 3.5) +
ggtitle(i) + theme_void() + theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
})
lgd2 <- get_legend(ps[[1]])
ps <- lapply(ps, function(x) x + theme(legend.position = "none"))
ps2 <- plot_grid(plotlist = ps, nrow = 1)
p2 <- plot_grid(ps2, lgd2, nrow = 1, label_size = 10, rel_widths = c(1, 0.1))
p <- plot_grid(p1, p2, nrow = 2)
ps <- lapply(names(genes)[6:8], function(i) {
FeaturePlot(sub, features = genes[[i]], reduction = "umap", pt.size = 0.4,
min.cutoff = 0, max.cutoff = 2.5) +
ggtitle(i) + theme_void() + theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
})
lgd3 <- get_legend(ps[[1]])
ps <- lapply(ps, function(x) x + theme(legend.position = "none"))
ps3 <- plot_grid(plotlist = ps, nrow = 1)
p3 <- plot_grid(ps3, lgd3, nrow = 1, label_size = 10, rel_widths = c(1, 0.1))
p <- plot_grid(p1, p2, p3, nrow = 3)
print(p)
How are the UNC13A expression levels in the different clusters and time points?
## we selected following markers
g <- "UNC13A"
gene <- grep(pattern = paste0("\\.", g, "$"), rownames(sce), value = TRUE)
names(gene) <- g
# UMAP colored by marker-expression
p <- FeaturePlot(sub, features = gene, reduction = "umap", pt.size = 0.4) +
theme(aspect.ratio = 1, legend.position = "none") +
ggtitle(g) + theme_void() + theme(aspect.ratio = 1)
print(p)
# compute cluster-marker means
mat <- vapply(cs_by_k, function(i)
Matrix::rowMeans(logcounts(sce)[gene, i, drop = FALSE]),
numeric(length(gene)))
# prep. for plotting & scale b/w 0 and 1
scale_vector <- function (x) {
qs <- rowQuantiles(t(as.matrix(x)), probs = c(0.01, 0.99), na.rm = TRUE)
x <- (x - qs[1])/(qs[2] - qs[1])
x[x < 0] <- 0
x[x > 1] <- 1
return(t(as.matrix(x)))
}
mat <- scale_vector(mat)
# 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", "#FF8E21", "#FDC78E")
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 4.0.5 (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] BiocParallel_1.22.0 RCurl_1.98-1.3
[3] stringr_1.4.0 SeuratObject_4.0.1
[5] Seurat_4.0.1 scran_1.16.0
[7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[9] DelayedArray_0.14.0 matrixStats_0.56.0
[11] Biobase_2.48.0 GenomicRanges_1.40.0
[13] GenomeInfoDb_1.24.2 IRanges_2.22.2
[15] S4Vectors_0.26.1 BiocGenerics_0.34.0
[17] viridis_0.5.1 viridisLite_0.3.0
[19] RColorBrewer_1.1-2 purrr_0.3.4
[21] muscat_1.2.1 dplyr_1.0.2
[23] ggplot2_3.3.2 cowplot_1.0.0
[25] ComplexHeatmap_2.4.2 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
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[89] mime_0.9 evaluate_0.14
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[99] KernSmooth_2.23-17 crayon_1.3.4
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[105] tidyr_1.1.0 geneplotter_1.66.0
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[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
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[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
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[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