Deng et al. dataset: nonnegative fits
Jason Willwerscheid
3/28/2022
Last updated: 2022-03-28
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,
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#> '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)do.heatmap <- function(res) {
fl <- res$fl
FF <- ldf(fl, type = "I")$F
FF <- FF[, -1]
FF <- FF[, order(res$fl$pve[-1], decreasing = TRUE)]
colnames(FF) <- 1:ncol(FF)
cell_type <- meta_data$cell_type
tib <- as_tibble(FF) %>%
mutate(Cell.type = cell_type) %>%
mutate(Cell.type = fct_relevel(Cell.type, c(
"zy",
"early2cell", "mid2cell", "late2cell",
"4cell", "8cell", "16cell",
"earlyblast", "midblast", "lateblast"
)))
tsne_res <- Rtsne(
as.matrix(tib %>% select(-Cell.type)),
dims = 1,
perplexity = pmax(1, floor((nrow(tib) - 1) / 3) - 1),
pca = FALSE,
normalize = FALSE,
theta = 0.1,
check_duplicates = FALSE,
verbose = FALSE
)$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$Cell.type)) +
theme_minimal() +
geom_hline(yintercept = -cell_type_breaks, size = 0.1)
}
do.struct.plot <- function(fl, kset, group_by_embryo = FALSE) {
tm <- init_poisson_nmf(X = t(Deng$dat), k = fl$n.factors, init.method = "random")
tm <- poisson2multinom(tm)
L <- ldf(fl)$F %*% diag(ldf(fl)$D)
L <- L[, order(fl$pve, decreasing = TRUE)]
colnames(L) <- paste0("k", 1:ncol(L))
L[, setdiff(1:fl$n.factors, kset)] <- 0
tm$L <- L
topic_colors <- rep("black", fl$n.factors)
topic_colors[kset] <- c("gainsboro", "forestgreen", "tomato", "skyblue", "royalblue",
"darkorange", "peru", "gold", "limegreen", "darkmagenta")[1:length(kset)]
cell_type <- factor(
meta_data$cell_type,
levels = c("zy", "early2cell", "mid2cell", "late2cell", "4cell", "8cell",
"16cell", "earlyblast", "midblast", "lateblast")
)
embryo <- factor(
paste0(meta_data$cell_type, "/", meta_data$embryo_id),
levels = paste0(rep(levels(cell_type), each = length(levels(meta_data$embryo_id))),
"/", levels(meta_data$embryo_id))
)
embryo <- droplevels(embryo)
embed_with_pca <- function (fit, ...) {
drop(pca_from_topics(fit, dims = 1,...))
}
if (group_by_embryo) {
grp <- embryo
} else {
grp <- cell_type
}
set.seed(1)
structure_plot(tm, grouping = grp, colors = topic_colors,
gap = 2, topics = kset, embed_method = embed_with_pca) +
labs(y = "loading", color = "factor", fill = "factor")
}I give a semi-nonnegative and three non-negative flashier fits to the Deng et al. dataset (see here for an introduction). The nonnegative fits were obtained by backfitting from a greedy nonnegative fit and from a NNMF obtained via NNLM.
Semi-nonnegative fit
smnf <- readRDS("./output/deng/smnf.rds")
do.heatmap(smnf)
Nonnegative fit (from greedy)
bf2 <- readRDS("./output/deng/bf2.rds")
do.heatmap(bf2)
Nonnegative fit (from NNLM)
nnlmbf <- readRDS("./output/deng/nnlmbf.rds")
do.heatmap(nnlmbf)
Nonnegative fit (from NNLM, shifted point-exponential priors)
nzpe <- readRDS("./output/deng/nzpe.rds")
shifts <- sapply(nzpe$fl$F.ghat[-1], function(k) k$shift[1])
nzpe$fl$flash.fit$EF[[2]][, -1] <- nzpe$fl$flash.fit$EF[[2]][, -1] - rep(shifts, each = ncol(Deng$fl.dat))
do.heatmap(nzpe)
sessionInfo()
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