Pseudocounts
Jason Willwerscheid
3/19/2020
Last updated: 2020-03-19
Checks: 6 0
Knit directory: scFLASH/
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Introduction
I redo my previous analysis of pseudocounts using the PBMC 3k dataset. Fits were produced by adding 20 “greedy” factors and backfitting. The code can be viewed here.
source("./code/utils.R")
pbmc <- readRDS("./data/10x/pbmc.rds")
pbmc <- preprocess.pbmc(pbmc)
res <- readRDS("./output/pseudocount/pseudocount_fits_pbmc.rds")Results: ELBO
As expected, the ELBO is monotonically decreasing as a function of the pseudocount.
elbo.df <- data.frame(pseudocount = as.numeric(names(res)),
elbo = sapply(res, function(x) x$fl$elbo + x$elbo.adj))
ggplot(elbo.df, aes(x = pseudocount, y = elbo)) +
geom_point() +
scale_x_continuous(trans = "log2") +
labs(y = "ELBO")
Results: Log likelihood of implied distribution
An in the previous analysis, \(\alpha = 0.5\) does best.
llik.df <- data.frame(pseudocount = as.numeric(names(res)),
llik = sapply(res, function(x) x$p.vals$llik))
ggplot(llik.df, aes(x = pseudocount, y = llik)) +
geom_point() +
scale_x_continuous(trans = "log2") +
labs(y = "log likelihood (implied model)")
Results: p-values
for (pc in names(res)) {
cat("\n### Pseudocount = ", pc, "\n")
plot(plot.p.vals(res[[pc]][["p.vals"]]))
cat("\n")
}Pseudocount = 0.0625
Pseudocount = 0.25
Pseudocount = 0.5
Pseudocount = 1
Pseudocount = 2
Pseudocount = 4
Pseudocount = 16
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] flashier_0.2.4 ggplot2_3.2.0 Matrix_1.2-15
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 compiler_3.5.3 pillar_1.3.1
[4] git2r_0.25.2 workflowr_1.2.0 iterators_1.0.10
[7] tools_3.5.3 digest_0.6.18 evaluate_0.13
[10] tibble_2.1.1 gtable_0.3.0 lattice_0.20-38
[13] pkgconfig_2.0.2 rlang_0.4.2 foreach_1.4.4
[16] parallel_3.5.3 yaml_2.2.0 ebnm_0.1-24
[19] xfun_0.6 withr_2.1.2 stringr_1.4.0
[22] dplyr_0.8.0.1 knitr_1.22 fs_1.2.7
[25] rprojroot_1.3-2 grid_3.5.3 tidyselect_0.2.5
[28] glue_1.3.1 R6_2.4.0 rmarkdown_1.12
[31] mixsqp_0.3-31 irlba_2.3.3 ashr_2.2-38
[34] purrr_0.3.2 magrittr_1.5 whisker_0.3-2
[37] MASS_7.3-51.1 codetools_0.2-16 backports_1.1.3
[40] scales_1.0.0 htmltools_0.3.6 assertthat_0.2.1
[43] colorspace_1.4-1 labeling_0.3 stringi_1.4.3
[46] pscl_1.5.2 doParallel_1.0.14 lazyeval_0.2.2
[49] munsell_0.5.0 truncnorm_1.0-8 SQUAREM_2017.10-1
[52] crayon_1.3.4