Last updated: 2020-08-18

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Knit directory: single-cell-topics/analysis/

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Rmd f914f7e Peter Carbonetto 2020-08-18 wflow_publish(“plots_tracheal_epithelium.Rmd”)
Rmd 61917ad Peter Carbonetto 2020-08-18 Working on new analysis, plots_tracheal_epithelium.Rmd.

TO DO: Add introductory text here.

Load the packages used in the analysis below.

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

Load the sample annotations. (The count data are no longer needed at this stage of the analysis.)

load("../data/droplet.RData")
samples_droplet <- samples
load("../data/pulseseq.RData")
samples_pulseseq <- samples
rm(samples,counts)

Load the \(k = 7\) Poisson NMF model fit for the droplet data, and the \(k = 11\) Poisson NMF fit for the pulse-seq data.

fit_droplet <- readRDS("../output/droplet/rds/fit-droplet-scd-ex-k=7.rds")$fit
fit_pulseseq <- readRDS("../output/pulseseq/rds/fit-pulseseq-scd-ex-k=11.rds")$fit

Rare and abundant cell types in the droplet data:

p1 <- create_abundance_plot(fit_droplet)
print(p1)

The first topic is indeed very rare; only 43 samples have a greater than 10% contribution from this topic.

sum(poisson2multinom(fit_droplet)$L[,1] > 0.1)
# [1] 43

Rare and abundant cell types in the pulse-seq data:

p2 <- create_abundance_plot(fit_pulseseq)
print(p2)

fit <- poisson2multinom(droplet_fit)
pca <- prcomp(fit$L)
ggplot(as.data.frame(pca$x),aes(x = PC1,y = PC2)) +
  geom_point(shape = 21,color = "white",fill = "black",size = 2) +
  theme_cowplot(font_size = 12)
ggplot(as.data.frame(pca$x),aes(x = PC5,y = PC6)) +
  geom_point(shape = 21,color = "white",fill = "black",size = 2) +
  theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k2 = fit$L[,2])),
       aes(x = PC1,y = PC2,fill = k2)) +
  geom_point(shape = 21,color = "white",size = 2) +
  scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
                       midpoint = 0.5) +
  theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k5 = fit$L[,5])),
       aes(x = PC1,y = PC2,fill = k5)) +
  geom_point(shape = 21,color = "white",size = 2) +
  scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
                       midpoint = 0.5) +
  theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k6 = fit$L[,6])),
       aes(x = PC1,y = PC2,fill = k6)) +
  geom_point(shape = 21,color = "white",size = 2) +
  scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
                       midpoint = 0.5) +
  theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k1 = fit$L[,1])),
       aes(x = PC5,y = PC6,fill = k1)) +
  geom_point(shape = 21,color = "white",size = 2) +
  scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
                       midpoint = 0.5) +
  theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k3 = fit$L[,3])),
       aes(x = PC1,y = PC2,fill = k3)) +
  geom_point(shape = 21,color = "white",size = 2) +
  scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
                       midpoint = 0.5) +
  theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k4 = fit$L[,4])),
       aes(x = PC1,y = PC2,fill = k4)) +
  geom_point(shape = 21,color = "white",size = 2) +
  scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
                       midpoint = 0.5) +
  theme_cowplot(font_size = 12)
clrs <- c("royalblue",      # basal
          "firebrick",      # ciliated
          "forestgreen",    # club
          "gold",           # goblet
          "darkmagenta",    # ionocyte
          "darkorange",     # neuroendocrine
          "darkgray")       # tuft
ggplot(cbind(samples_droplet,pca$x),aes(x = PC1,y = PC2,fill = tissue)) +
  geom_point(shape = 21,color = "white",size = 2) +
  scale_fill_manual(values = clrs) +
  theme_cowplot(font_size = 12)
ggplot(cbind(samples_droplet,pca$x),aes(x = PC5,y = PC6,fill = tissue)) +
  geom_point(shape = 21,color = "white",,size = 2) +
  scale_fill_manual(values = clrs) +
  theme_cowplot(font_size = 12)

sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.5
# 
# 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_0.3-159
# 
# loaded via a namespace (and not attached):
#  [1] ggrepel_0.9.0        Rcpp_1.0.3           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_1.4.1          
# [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          Matrix_1.2-18       
# [25] rmarkdown_2.3        labeling_0.3         Rtsne_0.15          
# [28] stringr_1.4.0        htmlwidgets_1.5.1    munsell_0.5.0       
# [31] compiler_3.6.2       httpuv_1.5.2         xfun_0.11           
# [34] pkgconfig_2.0.3      mcmc_0.9-6           htmltools_0.4.0     
# [37] tidyselect_0.2.5     tibble_2.1.3         workflowr_1.6.2.9000
# [40] quadprog_1.5-8       viridisLite_0.3.0    crayon_1.3.4        
# [43] dplyr_0.8.3          withr_2.1.2          later_1.0.0         
# [46] MASS_7.3-51.4        grid_3.6.2           jsonlite_1.6        
# [49] gtable_0.3.0         lifecycle_0.1.0      git2r_0.26.1        
# [52] magrittr_1.5         scales_1.1.0         RcppParallel_4.4.4  
# [55] stringi_1.4.3        farver_2.0.1         fs_1.3.1            
# [58] promises_1.1.0       vctrs_0.2.1          tools_3.6.2         
# [61] glue_1.3.1           purrr_0.3.3          hms_0.5.2           
# [64] yaml_2.2.0           colorspace_1.4-1     plotly_4.9.2        
# [67] knitr_1.26           quantreg_5.54        MCMCpack_1.4-5