Last updated: 2021-01-05

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Knit directory: single-cell-topics/analysis/

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Rmd bf07930 Peter Carbonetto 2021-01-05 workflowr::wflow_publish(“plots_purified_pbmc.Rmd”, verbose = TRUE)
Rmd 40c2d84 Peter Carbonetto 2021-01-04 Working on log-fold change scatterplots in plots_purified_pbmc analysis.
Rmd e437ddf Peter Carbonetto 2021-01-04 Working on volcano plots in plots_purified_pbmc analysis.

Add text here.

Load the packages used in the analysis below, as well as additional functions that we will use to generate some of the plots.

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

Load the count data, the \(K = 6\) topic model fit, and the 7 clusters identified in the clustering analysis.

load("../data/pbmc_purified.RData")
fit <- readRDS(file.path("../output/pbmc-purified/rds",
                         "fit-pbmc-purified-scd-ex-k=6.rds"))$fit
fit <- poisson2multinom(fit)
samples <- readRDS("../output/pbmc-purified/clustering-pbmc-purified.rds")

Code used temporarily for testing:

rows <- sample(1:94655,9000)
counts <- counts[rows,]
samples <- samples[rows,]
fit <- select_loadings(fit,loadings = rows)

Perform differential expression analysis using the FACS labeling.

celltype <- as.character(samples$celltype)
celltype[celltype == "CD4+/CD45RA+/CD25- Naive T" |
         celltype == "CD8+ Cytotoxic T" |
         celltype == "CD4+/CD45RO+ Memory" |
         celltype == "CD8+/CD45RA+ Naive Cytotoxic" |
         celltype == "CD4+ T Helper2" |
         celltype == "CD4+/CD25 T Reg"] <- "T cell"
celltype <- factor(celltype)
timing <- system.time(
  diff_count_facs <- diff_count_clusters(celltype,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21952 x 5 = 109760 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 7.92 seconds.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Perform differential expression analysis using the clusters.

timing <- system.time(
  diff_count_clusters <- diff_count_clusters(samples$cluster,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21952 x 7 = 153664 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 14.43 seconds.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Perform differential expression analysis using the multinomial topic model, after removing the dendritic cells cluster, and after merging the two T-cell topics.

rows <- which(samples$cluster != "dendritic")
fit_merged <- select_loadings(fit,loadings = rows)
fit_merged <- merge_topics(fit,c(1,6))
timing <- system.time(
  diff_count_topics <- diff_count_analysis(fit_merged,counts))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21952 x 5 = 109760 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 10.25 seconds.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Perform differential expression analysis on the T cells only.

rows <- which(samples$cluster == "T")
fit_t <- select_loadings(fit,loadings = rows)
timing <- system.time(
  diff_count_t <- diff_count_analysis(fit_t,counts[rows,]))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 7.81 seconds.

Add text here.

p1 <- volcano_plot_better(diff_count_topics,"k3",genes$symbol,0.998,
                          "topic 3 (B cells)",show_legend = TRUE)
print(p1)

Add text here.

pdat2 <- diff_count_t
i <- which(pdat2$beta >= 10)
n <- length(i)
pdat2$beta[i] <- pdat2$beta[i] + runif(n)
p2 <- volcano_plot_better(diff_count_topics,"k3",genes$symbol,0.998,
                          "topic 3 (B cells)")
p3 <- volcano_plot_better(diff_count_topics,"k4",genes$symbol,0.998,
                          "topic 4 (NK cells)")
p4 <- volcano_plot_better(diff_count_topics,"k2",genes$symbol,0.998,
                          "topic 2 (CD14+)")
p5 <- volcano_plot_better(diff_count_topics,"k5",genes$symbol,0.998,
                          "topic 5 (CD34+)")
p6 <- volcano_plot_better(diff_count_topics,"k1+k6",genes$symbol,0.998,
                          "topics 1 + 6 (T cells)")
p7 <- volcano_plot_better(diff_count_t,"k1",genes$symbol,0.995,
                          "topic 1 (CD4+/CD8+)")
p8 <- volcano_plot_better(diff_count_clusters,"CD8+",genes$symbol,0.998,
                          "CD8+ cluster") + xlim(-11,11)
p9 <- volcano_plot_better(diff_count_clusters,"dendritic",genes$symbol,0.997,
                          "dendritic cells cluster")
plot_grid(p2,p3,p4,p5,p6,p7,p8,p9,nrow = 3,ncol = 3)

Add text here.

rows <- which(abs(diff_count_facs$Z[,"CD19+ B"]) > 6 |
              abs(diff_count_topics$Z[,"k3"]) > 6)
pdat <- data.frame(x = diff_count_facs$beta[rows,"CD19+ B"],
                   y = diff_count_topics$beta[rows,"k3"])
pdat$x <- pmax(pdat$x,-10)
pdat$y <- pmax(pdat$y,-10)
p9 <- ggplot(pdat,aes(x = x,y = y)) +
  geom_point(shape = 21,color = "white",fill = "dodgerblue") +
  geom_abline(slope = 1,intercept = 0,color = "black",linetype = "dotted") +
  theme_cowplot(font_size = 9)

TO DO:


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_3.3.0     fastTopics_0.4-13 Matrix_1.2-18    
# 
# loaded via a namespace (and not attached):
#  [1] ggrepel_0.9.0        Rcpp_1.0.5           lattice_0.20-38     
#  [4] tidyr_1.0.0          prettyunits_1.1.1    assertthat_0.2.1    
#  [7] zeallot_0.1.0        rprojroot_1.3-2      digest_0.6.23       
# [10] R6_2.4.1             backports_1.1.5      MatrixModels_0.4-1  
# [13] evaluate_0.14        coda_0.19-3          httr_1.4.2          
# [16] pillar_1.4.3         rlang_0.4.5          progress_1.2.2      
# [19] lazyeval_0.2.2       data.table_1.12.8    irlba_2.3.3         
# [22] SparseM_1.78         whisker_0.4          rmarkdown_2.3       
# [25] labeling_0.3         Rtsne_0.15           stringr_1.4.0       
# [28] htmlwidgets_1.5.1    munsell_0.5.0        compiler_3.6.2      
# [31] httpuv_1.5.2         xfun_0.11            pkgconfig_2.0.3     
# [34] mcmc_0.9-6           htmltools_0.4.0      tidyselect_0.2.5    
# [37] tibble_2.1.3         workflowr_1.6.2.9000 quadprog_1.5-8      
# [40] viridisLite_0.3.0    crayon_1.3.4         dplyr_0.8.3         
# [43] withr_2.1.2          later_1.0.0          MASS_7.3-51.4       
# [46] grid_3.6.2           jsonlite_1.6         gtable_0.3.0        
# [49] lifecycle_0.1.0      git2r_0.26.1         magrittr_1.5        
# [52] scales_1.1.0         RcppParallel_4.4.2   stringi_1.4.3       
# [55] farver_2.0.1         fs_1.3.1             promises_1.1.0      
# [58] vctrs_0.2.1          tools_3.6.2          glue_1.3.1          
# [61] purrr_0.3.3          hms_0.5.2            yaml_2.2.0          
# [64] colorspace_1.4-1     plotly_4.9.2         knitr_1.26          
# [67] quantreg_5.54        MCMCpack_1.4-5