Last updated: 2022-03-04

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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   1.1.4     ✓ 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,
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#> 
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#> The following objects are masked from 'package:stats':
#> 
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#> 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)

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.

do.heatmap <- function(fl) {
  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)]))
  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)
}

6-factor fit

fl6 <- readRDS("./output/deng/deng_fl6.rds")
do.heatmap(fl6)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03

10-factor fit

fl10 <- readRDS("./output/deng/deng_fl10.rds")
do.heatmap(fl10)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03

25-factor fit

fl25 <- readRDS("./output/deng/deng_fl25.rds")
do.heatmap(fl25)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03

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)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03
f7e7249 Jason Willwerscheid 2022-03-02

Factor 3: mid 2-cell to 16-cell

Some overlap with the yellow cluster in Dey et al.:

do.volcano.plot(fl25, 3)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03
f7e7249 Jason Willwerscheid 2022-03-02

Factor 4: early to late blastocyte

Overlaps with the orange cluster in Dey et al.:

do.volcano.plot(fl25, 4)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03
f7e7249 Jason Willwerscheid 2022-03-02

Factor 10: zygote to early 2-cell

do.volcano.plot(fl25, 10)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03
f7e7249 Jason Willwerscheid 2022-03-02

Factor 8: mid to late 2-cell

Overlaps with the magenta cluster in Dey et al.:

do.volcano.plot(fl25, 8)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03

Factor 9: late 2-cell to 4-cell

do.volcano.plot(fl25, 9)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03
f7e7249 Jason Willwerscheid 2022-03-02

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)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03
f7e7249 Jason Willwerscheid 2022-03-02

Factor 5: early to late blastocyte

Overlaps with the purple cluster in Dey et al.:

do.volcano.plot(fl25, 5)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03
f7e7249 Jason Willwerscheid 2022-03-02

Factor 7: late blastocyte

do.volcano.plot(fl25, 7)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03
f7e7249 Jason Willwerscheid 2022-03-02

Factor 16: early blastocyte

do.volcano.plot(fl25, 16)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03

Factor 11: mid blastocyte

do.volcano.plot(fl25, 11)

Version Author Date
a9459a1 Jason Willwerscheid 2022-03-03
f7e7249 Jason Willwerscheid 2022-03-02

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
#> 
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] parallel  stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] Rtsne_0.15                           singleCellRNASeqMouseDeng2014_0.99.0
#>  [3] Biobase_2.42.0                       BiocGenerics_0.28.0                 
#>  [5] ggrepel_0.8.2                        flashier_0.2.27                     
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#>  [9] stringr_1.4.0                        dplyr_1.0.8                         
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#> [13] tidyr_1.1.4                          tibble_3.1.6                        
#> [15] ggplot2_3.3.5                        tidyverse_1.3.1                     
#> [17] workflowr_1.6.2                     
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