PBMCs dataset: nonnegative fits
Jason Willwerscheid
3/30/2022
Last updated: 2022-03-30
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(Matrix)
#>
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#>
#> expand, pack, unpack
library(Rtsne)
library(fastTopics)
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,
pc = lunpc,
excluded.genes = gene_cts < min.nzcts)
)
}
pbmc <- readRDS("../flashier-chapter/data/pbmc.rds")
pbmc <- pbmc[, colSums(pbmc) < 15000]
pbmc <- preprocess(pbmc)
pbmc.celltype <- sapply(strsplit(colnames(pbmc$fl.dat), "_"), `[[`, 2)do.heatmap <- function(res) {
fl <- res$fl
FF <- ldf(fl, type = "I")$F
FF <- FF[, -1]
FF <- FF[, order(res$fl$pve[-1], decreasing = TRUE)]
colnames(FF) <- 1:ncol(FF)
cell_type <- pbmc.celltype
tib <- as_tibble(FF) %>%
mutate(Cell.type = cell_type)
tsne_res <- Rtsne(
as.matrix(tib %>% select(-Cell.type)),
dims = 1,
perplexity = pmax(1, floor((nrow(tib) - 1) / 3) - 1),
pca = FALSE,
normalize = FALSE,
theta = 0.1,
check_duplicates = FALSE,
verbose = FALSE
)$Y[, 1]
tib <- tib %>%
mutate(tsne_res = unlist(tsne_res)) %>%
arrange(Cell.type, tsne_res) %>%
mutate(Cell.idx = row_number()) %>%
select(-tsne_res)
cell_type <- tib$Cell.type
tib <- tib %>%
pivot_longer(
-c(Cell.idx, Cell.type),
names_to = "Factor",
values_to = "Loading",
values_drop_na = TRUE
) %>%
mutate(Factor = as.numeric(Factor))
cell_type_breaks <- c(1, which(cell_type[2:nrow(tib)] != cell_type[1:(nrow(tib) - 1)]))
ggplot(tib, aes(x = Factor, y = -Cell.idx, fill = Loading)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
labs(y = "") +
scale_y_continuous(breaks = -cell_type_breaks,
minor_breaks = NULL,
labels = unique(tib$Cell.type)) +
theme_minimal() +
geom_hline(yintercept = -cell_type_breaks, size = 0.1)
}I give a semi-nonnegative and three non-negative flashier fits to the PBMCs dataset that I also looked at in my thesis. 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/pbmc/smnf.rds")
cat("ELBO:", smnf$fl$elbo, "\n")ELBO: 68092738
units(smnf$t) <- "mins"
cat("Time to fit:", format(smnf$t, digits = 1), "\n")Time to fit: 28 mins
cat("Maximum iterations reached:", !is.null(smnf$fl$flash.fit$maxiter.reached), "\n")Maximum iterations reached: FALSE
do.heatmap(smnf)
| Version | Author | Date |
|---|---|---|
| f2643e0 | Jason Willwerscheid | 2022-03-30 |
Nonnegative fit (from greedy)
greedy <- readRDS("./output/pbmc/greedy.rds")
bf <- readRDS("./output/pbmc/bf.rds")
bf.t <- bf$t + greedy$t
cat("ELBO:", bf$fl$elbo, "\n")ELBO: 67987962
units(bf.t) <- "mins"
cat("Time to fit:", format(bf.t, digits = 1), "\n")Time to fit: 29 mins
cat("Maximum iterations reached:", !is.null(bf$fl$flash.fit$maxiter.reached), "\n")Maximum iterations reached: FALSE
do.heatmap(bf)
| Version | Author | Date |
|---|---|---|
| f2643e0 | Jason Willwerscheid | 2022-03-30 |
Nonnegative fit (from NNLM)
nnlmbf <- readRDS("./output/pbmc/nnlmbf.rds")
cat("ELBO:", nnlmbf$fl$elbo, "\n")ELBO: 68042615
units(nnlmbf$t) <- "mins"
cat("Time to fit:", format(nnlmbf$t, digits = 1), "\n")Time to fit: 110 mins
cat("Maximum iterations reached:", !is.null(nnlmbf$fl$flash.fit$maxiter.reached), "\n")Maximum iterations reached: FALSE
do.heatmap(nnlmbf)
| Version | Author | Date |
|---|---|---|
| f2643e0 | Jason Willwerscheid | 2022-03-30 |
Nonnegative fit (from NNLM, shifted point-exponential priors)
nzpe <- readRDS("./output/pbmc/nzpe.rds")
nzpe2 <- readRDS("./output/pbmc/nzpe2.rds")
# shifts <- sapply(nzpe$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(pbmc$fl.dat))
nzpe.t <- nzpe$t + nzpe2$t
cat("ELBO:", nzpe2$fl$elbo, "\n")ELBO: 68021342
units(nzpe.t) <- "mins"
cat("Time to fit:", format(nzpe.t, digits = 1), "\n")Time to fit: 85 mins
cat("Maximum iterations reached:", !is.null(nzpe2$fl$flash.fit$maxiter.reached), "\n")Maximum iterations reached: FALSE
do.heatmap(nzpe2)
| Version | Author | Date |
|---|---|---|
| f2643e0 | Jason Willwerscheid | 2022-03-30 |
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:
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#>
#> attached base packages:
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#>
#> other attached packages:
#> [1] fastTopics_0.6-101 Rtsne_0.15 Matrix_1.3-4 ggrepel_0.9.1
#> [5] flashier_0.2.29 magrittr_2.0.2 forcats_0.5.1 stringr_1.4.0
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