Deng et al. dataset
Jason Willwerscheid
3/2/2022
Last updated: 2022-03-06
Checks: 7 0
Knit directory: scFLASH/
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Overview
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
#> ✓ tibble 3.1.6 ✓ dplyr 1.0.8
#> ✓ tidyr 1.2.0 ✓ stringr 1.4.0
#> ✓ readr 2.0.0 ✓ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
library(flashier)
#> Loading required package: magrittr
#>
#> Attaching package: 'magrittr'
#> The following object is masked from 'package:purrr':
#>
#> set_names
#> The following object is masked from 'package:tidyr':
#>
#> extract
library(ggrepel)
library(singleCellRNASeqMouseDeng2014)
#> Loading required package: Biobase
#> Loading required package: BiocGenerics
#> Loading required package: parallel
#>
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:parallel':
#>
#> clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#> clusterExport, clusterMap, parApply, parCapply, parLapply,
#> parLapplyLB, parRapply, parSapply, parSapplyLB
#> The following objects are masked from 'package:dplyr':
#>
#> combine, intersect, setdiff, union
#> The following objects are masked from 'package:stats':
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#>
#> anyDuplicated, append, as.data.frame, basename, cbind, colMeans,
#> colnames, colSums, dirname, do.call, duplicated, eval, evalq,
#> Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply,
#> lengths, Map, mapply, match, mget, order, paste, pmax, pmax.int,
#> pmin, pmin.int, Position, rank, rbind, Reduce, rowMeans, rownames,
#> rowSums, sapply, setdiff, sort, table, tapply, union, unique,
#> unsplit, which, which.max, which.min
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
library(Rtsne)
library(fastTopics)
counts <- exprs(Deng2014MouseESC)
meta_data <- pData(Deng2014MouseESC)
gene_names <- rownames(counts)
preprocess <- function(dat, min.nzcts = 10) {
size.factors <- colSums(dat)
size.factors <- size.factors / mean(size.factors)
gene_cts <- rowSums(dat > 0)
dat <- dat[gene_cts >= min.nzcts, ]
lunpc <- max(1 / min(size.factors) - 1 / max(size.factors), 1)
fl.dat <- log1p(t(t(dat) / size.factors) / lunpc)
return(list(
dat = dat,
fl.dat = fl.dat,
size.factors = size.factors,
excluded.genes = gene_cts < min.nzcts)
)
}
Deng <- preprocess(counts)The dataset, from Deng et al., is made available by kkdey’s R package singleCellRNASeqMouseDeng2014, which I installed using command remotes::install_github("kkdey/singleCellRNASeqMouseDeng2014").
After removing genes with nonzero counts in fewer than 10 cells, there remain counts for 17176 genes and 259 cells. Each cell has been labelled as one of 10 cell types (or rather, one of 10 embryonic stages ranging from zygote to late blastocyte).
I fit 6, 10, and 25 semi-nonnegative EBMF factors using flashier. Code is here. In the heatmaps below, rows correspond to individual cells, and factors (columns) are arranged in order of decreasing proportion of variance explained. I also show stacked bar plots for purposes of comparison with STRUCTURE plots.
do.plots <- function(fl, fct_order) {
FF <- ldf(fl, type = "I")$F
FF <- FF[, order(fl$pve, decreasing = TRUE)]
colnames(FF) <- 1:ncol(FF)
cell_type <- meta_data$cell_type
tib <- as_tibble(FF) %>%
mutate(Cell.type = cell_type) %>%
mutate(Cell.type = fct_relevel(Cell.type, c(
"zy",
"early2cell", "mid2cell", "late2cell",
"4cell", "8cell", "16cell",
"earlyblast", "midblast", "lateblast"
)))
tsne_res <- Rtsne(
as.matrix(tib %>% select(-`1`, -Cell.type)),
dims = 1,
perplexity = pmax(1, floor((nrow(tib) - 1) / 3) - 1),
pca = FALSE,
normalize = FALSE,
theta = 0.1,
check_duplicates = FALSE,
verbose = FALSE
)$Y[, 1]
tib <- tib %>%
mutate(tsne_res = unlist(tsne_res)) %>%
arrange(Cell.type, tsne_res) %>%
mutate(Cell.idx = row_number()) %>%
select(-tsne_res)
tib <- tib %>%
pivot_longer(
-c(Cell.idx, Cell.type),
names_to = "Factor",
values_to = "Loading",
values_drop_na = TRUE
) %>%
mutate(Factor = as.numeric(Factor))
cell_type_breaks <- c(1, which(cell_type[2:nrow(tib)] != cell_type[1:(nrow(tib) - 1)]))
heatmap <- ggplot(tib, aes(x = Factor, y = -Cell.idx, fill = Loading)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
labs(y = "") +
scale_y_continuous(breaks = -cell_type_breaks,
minor_breaks = NULL,
labels = levels(tib$Cell.type)) +
theme_minimal() +
geom_hline(yintercept = -cell_type_breaks, size = 0.1)
tib <- tib %>%
filter(Factor %in% fct_order) %>%
mutate(Factor = factor(Factor, levels = fct_order))
stkbar <- ggplot(tib, aes(x = Cell.idx, y = Loading, fill = Factor, color = Factor)) +
geom_bar(position = "stack", stat = "identity") +
scale_fill_brewer(palette = "Set3") +
scale_color_brewer(palette = "Set3") +
theme_minimal() +
labs(x = "", y = "") +
scale_x_continuous(breaks = cell_type_breaks,
minor_breaks = NULL,
labels = levels(tib$Cell.type)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.text.y = element_blank())
return(list(heatmap = heatmap, stkbar = stkbar))
}6-factor fit
fl6 <- readRDS("./output/deng/deng_fl6.rds")
plt6 <- do.plots(fl6, rev(c(6, 4, 2, 3, 5)))
plt6$heatmap
plt6$stkbar
10-factor fit
fl10 <- readRDS("./output/deng/deng_fl10.rds")
plt10 <- do.plots(fl10, rev(c(6, 4, 5, 3, 2, 7, 9, 8)))
plt10$heatmap
plt10$stkbar
Top genes
Many of the factors in the 25-factor fit seem to primarily capture noise in individual cells. I show volcano plots for the more interesting factors, which I’ve grouped (major factors 2-4; “sub-factors” 5-10; and then interestingly localized minor factors 11 and 16) and arranged in rough ontogenetic order. I label the top 20 genes by (absolute) z-score (defined as posterior mean / posterior SD) as well as the top 20 by (absolute) posterior mean.
do.volcano.plot <- function(fl, k) {
k <- order(fl$pve, decreasing = TRUE)[k]
tib <- tibble(
pm = fl$L.pm[, k],
z = abs(fl$L.pm[, k]) / pmax(sqrt(.Machine$double.eps), fl$L.psd[, k]),
exprmean = log10(rowMeans(Deng$dat)),
SYMBOL = rownames(fl$L.pm)
) %>%
mutate(SYMBOL = ifelse(
z > sort(z, decreasing = TRUE)[21] |
abs(pm) > sort(abs(pm), decreasing = TRUE)[21], SYMBOL, ""))
plt <- ggplot(tib, aes(x = pm, y = z, color = exprmean, label = SYMBOL)) +
geom_point() +
scale_color_gradient2(low = "deepskyblue", mid = "gold", high = "orangered",
na.value = "gainsboro",
midpoint = mean(range(tib$exprmean))) +
scale_y_sqrt() +
geom_text_repel(color = "darkgray",size = 2.25, fontface = "italic",
segment.color = "darkgray", segment.size = 0.25,
min.segment.length = 0, na.rm = TRUE) +
theme_minimal() +
labs(
x = "Factor Loading (posterior mean)",
y = "|z-score|",
color = "Mean Expression (log10)"
) +
theme(legend.position = "bottom")
return(plt)
}Factor 2: zygote to 4-cell
Overlaps with the blue cluster in Dey et al.:
do.volcano.plot(fl25, 2)
#> Warning: ggrepel: 22 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
Factor 3: mid 2-cell to 16-cell
Some overlap with the yellow cluster in Dey et al.:
do.volcano.plot(fl25, 3)
#> Warning: ggrepel: 9 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
Factor 4: early to late blastocyte
Overlaps with the orange cluster in Dey et al.:
do.volcano.plot(fl25, 4)
#> Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
Factor 10: zygote to early 2-cell
do.volcano.plot(fl25, 10)
#> Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
Factor 8: mid to late 2-cell
Overlaps with the magenta cluster in Dey et al.:
do.volcano.plot(fl25, 8)
#> Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
Factor 9: late 2-cell to 4-cell
do.volcano.plot(fl25, 9)
#> Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
Factor 6: 16-cell to late blastocyte
Overlaps with the green cluster in Dey et al., but also the orange to some extent:
do.volcano.plot(fl25, 6)
Factor 5: early to late blastocyte
Overlaps with the purple cluster in Dey et al.:
do.volcano.plot(fl25, 5)
#> Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
Factor 7: late blastocyte
do.volcano.plot(fl25, 7)
#> Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
Factor 16: early blastocyte
do.volcano.plot(fl25, 16)
Factor 11: mid blastocyte
do.volcano.plot(fl25, 11)
#> Warning: ggrepel: 28 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
sessionInfo()
#> R version 3.5.3 (2019-03-11)
#> Platform: x86_64-apple-darwin15.6.0 (64-bit)
#> Running under: macOS Mojave 10.14.6
#>
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#>
#> attached base packages:
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#> [8] base
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#> other attached packages:
#> [1] fastTopics_0.6-100 Rtsne_0.15
#> [3] singleCellRNASeqMouseDeng2014_0.99.0 Biobase_2.42.0
#> [5] BiocGenerics_0.28.0 ggrepel_0.9.1
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