Last updated: 2021-10-12

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Knit directory: single-cell-topics/analysis/

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File Version Author Date Message
Rmd 2726a86 Peter Carbonetto 2021-10-12 workflowr::wflow_publish(“de_analysis_detailed_look.Rmd”)
html b8eadee Peter Carbonetto 2021-10-11 Added some basic histograms to the de_analysis_detailed_look analysis.
Rmd e2577f1 Peter Carbonetto 2021-10-11 Working on the data simulation step in the de_analysis_detailed_look
html cd17ccf Peter Carbonetto 2021-10-09 Initial build of the de_analysis_detailed_look workflowr page.
Rmd b8e1fe8 Peter Carbonetto 2021-10-09 workflowr::wflow_publish(“de_analysis_detailed_look.Rmd”)
Rmd 888ea7d Peter Carbonetto 2021-10-08 I have an initial implementation of function simulate_twotopic_scrnaseq_data used to evaluate the de_analysis methods in fastTopics.

Add summary of the analysis here.

Load the packages used in the analysis below, and some additional functions for simulating the data.

library(Matrix)
library(fastTopics)
library(MCMCpack)
library(ggplot2)
library(cowplot)
source("../code/de_eval_functions.R")

Simulate UMI counts

We begin by simulating counts intended to “mimic” UMI count data from a single-cell RNA sequencing experiment. In particular, we simulate counts \(x_{ij}\) from a Poisson NMF model \(x_{ij} \sim \mathrm{Poisson}(\lambda_{ij})\), such that \(\lambda_{ij} = \sum_{k=1}^K l_{ik} f_{jk}\). We focus our attention on the \(K = 2\) case to simplify the analysis; comparing gene expression between three or topics brings some additional complications which aren’t necessary for assessing basic properties of the new DE methods. The simulation of the topic proportions \(l_{ik}\) and the gene expression rates \(f_{jk}\) is described in the comments accompanying function simulate_twotopic_umi_data.

set.seed(1)
dat <- simulate_twotopic_umi_data()
X <- dat$X

The sample sizes (total counts for each cell) in the simulated data are roughly normally distributed on the (base-10) log scale with mean 3.2 and standard deviation 0.2:

s <- rowSums(X)
p1 <- ggplot(data.frame(s = log10(s)),aes(x = s)) +
  geom_histogram(color = "white",fill = "black",bins = 32) +
  labs(x = "log10 size",y = "cells") +
  theme_cowplot()
print(p1)
mean(log10(s))
sd(log10(s))
# [1] 3.291997
# [1] 0.2360261

Version Author Date
b8eadee Peter Carbonetto 2021-10-11

We have generated the expression rates so that they are roughly uniformly distributed on the log scale:

p2 <- ggplot(data.frame(f = as.vector(dat$F)),aes(x = log10(f))) +
  geom_histogram(color = "white",fill = "black",bins = 32) +
  labs(x = "log10 expression rate",y = "genes") +
  theme_cowplot()
print(p2)

Version Author Date
b8eadee Peter Carbonetto 2021-10-11

About half of the genes have differences in expression between the two topics, and among the nonzero gene expression differences, the LFCs are roughly t-distibuted with 3 degrees of freedom:

lfc <- log2(dat$F[,1]/dat$F[,2])
p3 <- ggplot(data.frame(lfc = lfc[lfc != 0]),aes(x = lfc)) +
  geom_histogram(color = "white",fill = "black",bins = 32) +
  labs(x = "LFC",y = "genes") +
  theme_cowplot()
print(p3)
mean(lfc == 0)
# [1] 0.5024

Version Author Date
b8eadee Peter Carbonetto 2021-10-11 

sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] cowplot_1.0.0     ggplot2_3.3.5     MCMCpack_1.4-5    MASS_7.3-51.4    
# [5] coda_0.19-3       fastTopics_0.6-70 Matrix_1.2-18    
# 
# loaded via a namespace (and not attached):
#  [1] httr_1.4.2         tidyr_1.1.3        jsonlite_1.7.2     viridisLite_0.3.0 
#  [5] RcppParallel_4.4.2 assertthat_0.2.1   mixsqp_0.3-46      yaml_2.2.0        
#  [9] progress_1.2.2     ggrepel_0.9.1      pillar_1.6.2       backports_1.1.5   
# [13] lattice_0.20-38    quantreg_5.54      glue_1.4.2         quadprog_1.5-8    
# [17] digest_0.6.23      promises_1.1.0     colorspace_1.4-1   htmltools_0.4.0   
# [21] httpuv_1.5.2       pkgconfig_2.0.3    invgamma_1.1       SparseM_1.78      
# [25] purrr_0.3.4        scales_1.1.0       whisker_0.4        later_1.0.0       
# [29] Rtsne_0.15         MatrixModels_0.4-1 git2r_0.26.1       tibble_3.1.3      
# [33] farver_2.0.1       generics_0.0.2     ellipsis_0.3.2     withr_2.4.2       
# [37] ashr_2.2-51        lazyeval_0.2.2     magrittr_2.0.1     crayon_1.4.1      
# [41] mcmc_0.9-6         evaluate_0.14      fs_1.3.1           fansi_0.4.0       
# [45] truncnorm_1.0-8    tools_3.6.2        data.table_1.12.8  prettyunits_1.1.1 
# [49] hms_1.1.0          lifecycle_1.0.0    stringr_1.4.0      plotly_4.9.2      
# [53] munsell_0.5.0      irlba_2.3.3        compiler_3.6.2     rlang_0.4.11      
# [57] grid_3.6.2         htmlwidgets_1.5.1  labeling_0.3       rmarkdown_2.3     
# [61] gtable_0.3.0       DBI_1.1.0          R6_2.4.1           knitr_1.26        
# [65] dplyr_1.0.7        utf8_1.1.4         workflowr_1.6.2    rprojroot_1.3-2   
# [69] stringi_1.4.3      parallel_3.6.2     SQUAREM_2017.10-1  Rcpp_1.0.7        
# [73] vctrs_0.3.8        tidyselect_1.1.1   xfun_0.11