Last updated: 2020-03-19

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Rmd 8973b42 Jason Willwerscheid 2020-03-19 wflow_publish(“analysis/size_factors_pbmc.Rmd”)

Introduction

I redo my previous analysis of size factors 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/size_factors/sizefactor_fits_pbmc.rds")

Results: scran size factors

sf.df <- data.frame(libsize = res$libsize$size.factors, scran = res$scran$size.factors)
ggplot(sf.df, aes(x = libsize, y = scran)) +
  geom_point(size = 0.2) +
  geom_abline(slope = 1) + 
  labs(x = "library size normalization", y = "scran")

Results: ELBO

method.names <- c("No size factors", 
                  "Library size normalization",
                  "scran size factors")
elbo.df <- data.frame(method = method.names,
                      elbo = sapply(res, function(x) x$fl$elbo + x$elbo.adj))
ggplot(elbo.df, aes(x = method, y = elbo)) + 
  geom_point() +
  scale_x_discrete(limits = method.names) +
  labs(x = NULL, y = "ELBO")

Results: Distribution of p-values

KL.df <- data.frame(method = method.names, 
                    KL = sapply(res, function(x) x$p.vals$KL.divergence))
ggplot(KL.df, aes(x = method, y = KL)) +
  geom_bar(stat = "identity") +
  scale_x_discrete(limits = method.names) +
  labs(x = NULL, y = "KL divergence relative to uniform")

Results: Log likelihood of implied distribution

llik.df <- data.frame(method = method.names, 
                      llik = sapply(res, function(x) x$p.vals$llik))
ggplot(llik.df, aes(x = method, y = llik)) + 
  geom_point() +
  scale_x_discrete(limits = method.names) +
  labs(x = NULL, y = "Log likelihood of implied distribution")

Results: Factors and cell types

plot.factors(res$scran, pbmc$cell.type, 1:20, title = "scran")

scran.v.libsize <- compare.factors(res$scran$fl, res$libsize$fl)
plot.factors(res$libsize, pbmc$cell.type, scran.v.libsize$fl2.k,
             title = "Library size normalization")

scran.v.noscale <- compare.factors(res$scran$fl, res$noscale$fl)
plot.factors(res$noscale, pbmc$cell.type, scran.v.noscale$fl2.k,
             title = "No size factors")


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        plyr_1.8.4        compiler_3.5.3   
 [4] pillar_1.3.1      git2r_0.25.2      workflowr_1.2.0  
 [7] iterators_1.0.10  tools_3.5.3       digest_0.6.18    
[10] evaluate_0.13     tibble_2.1.1      gtable_0.3.0     
[13] lattice_0.20-38   pkgconfig_2.0.2   rlang_0.4.2      
[16] foreach_1.4.4     parallel_3.5.3    yaml_2.2.0       
[19] ebnm_0.1-24       xfun_0.6          withr_2.1.2      
[22] stringr_1.4.0     dplyr_0.8.0.1     knitr_1.22       
[25] fs_1.2.7          rprojroot_1.3-2   grid_3.5.3       
[28] tidyselect_0.2.5  glue_1.3.1        R6_2.4.0         
[31] rmarkdown_1.12    mixsqp_0.3-31     irlba_2.3.3      
[34] reshape2_1.4.3    ashr_2.2-38       purrr_0.3.2      
[37] magrittr_1.5      whisker_0.3-2     MASS_7.3-51.1    
[40] codetools_0.2-16  backports_1.1.3   scales_1.0.0     
[43] htmltools_0.3.6   assertthat_0.2.1  colorspace_1.4-1 
[46] labeling_0.3      stringi_1.4.3     pscl_1.5.2       
[49] doParallel_1.0.14 lazyeval_0.2.2    munsell_0.5.0    
[52] truncnorm_1.0-8   SQUAREM_2017.10-1 crayon_1.3.4