Last updated: 2022-02-11

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Knit directory: single-cell-topics/analysis/

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Based on the topic modeling results in the droplet data, with \(K = 7\) topics, one of the topics, topic 6, appears to be capturing rare, specialized epithelial cells, including ionocyte, tuft and neuroendocrine cells. Therefore, here we reanalyze the cells with membership to this topic.

Load the packages used in the analysis.

library(fastTopics)
library(ggplot2)
library(cowplot)

Initialize the sequence of pseudorandom numbers.

set.seed(1)

Load the previously prepared count data.

load("../data/droplet.RData")

Load the results on the rare cell types.

out       <- readRDS("../output/droplet/refit-droplet.rds")
fits      <- out$fits
de        <- out$de
de_merged <- out$de_merged

Below we will look more closely at the topic model with \(K = 5\) topics:

fit <- poisson2multinom(fits$k5)

Find the subset of genes and samples that were used to fit the model:

rownames(samples) <- with(samples,paste(mouse.id,barcode,sep = "_"))
samples <- samples[rownames(fit$L),]
counts  <- counts[rownames(fit$L),rownames(fit$F)]

This plot shows the improvement in the log-likelihood as the rank, \(K\), is increased. The log-likelihoods are shown relative to the log-likelihood at \(K = 2\).

plot_loglik_vs_rank(fits) +
  theme_cowplot(font_size = 12)

Version Author Date
a64f3ea Peter Carbonetto 2022-02-11

This PCA plot showing the top two PCs of the topic proportions shows that the topics correspond well with the clusters identified by Montoro et al (2018):

clusters <- as.character(samples$tissue)
clusters[clusters == "Basal" |
         clusters == "Club" |
         clusters == "Goblet"] <- "Other"
clusters <- factor(clusters)
tissue_colors <- c("firebrick",   # ciliated
                   "darkmagenta", # ionocyte
                   "darkorange",  # neuroendocrine
                   "gainsboro",   # other
                   "skyblue")     # tuft
p <- pca_plot(fit,pcs = 1:2,fill = clusters) +
  scale_fill_manual(values = tissue_colors)
# Scale for 'fill' is already present. Adding another scale for 'fill', which
# will replace the existing scale.
print(p)

ggsave("../plots/pca_cluster_droplet_rare.png",p,
       height = 3,width = 4,dpi = 450,bg = "white")

The structure plot summarizes the estimated topic proportions:

set.seed(1)
topic_colors <- c("skyblue","darkorange","red","violet","gainsboro")
topics <- c(5,2,1,3,4)
p <- structure_plot(fit,topics = topics,colors = topic_colors,
                    perplexity = 70,num_threads = 2,verbose = FALSE)
print(p)

Results to add:


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     fastTopics_0.6-98
# 
# 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   highr_0.8          mixsqp_0.3-46     
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# [13] backports_1.1.5    lattice_0.20-38    quantreg_5.54      glue_1.4.2        
# [17] quadprog_1.5-8     digest_0.6.23      promises_1.1.0     colorspace_1.4-1  
# [21] htmltools_0.4.0    httpuv_1.5.2       Matrix_1.2-18      pkgconfig_2.0.3   
# [25] invgamma_1.1       SparseM_1.78       purrr_0.3.4        scales_1.1.0      
# [29] whisker_0.4        later_1.0.0        Rtsne_0.15         MatrixModels_0.4-1
# [33] git2r_0.26.1       tibble_3.1.3       farver_2.0.1       generics_0.0.2    
# [37] ellipsis_0.3.2     withr_2.4.2        ashr_2.2-51        pbapply_1.5-1     
# [41] lazyeval_0.2.2     magrittr_2.0.1     crayon_1.4.1       mcmc_0.9-6        
# [45] evaluate_0.14      fs_1.3.1           fansi_0.4.0        MASS_7.3-51.4     
# [49] truncnorm_1.0-8    tools_3.6.2        data.table_1.12.8  prettyunits_1.1.1 
# [53] hms_1.1.0          lifecycle_1.0.0    stringr_1.4.0      MCMCpack_1.4-5    
# [57] plotly_4.9.2       munsell_0.5.0      irlba_2.3.3        compiler_3.6.2    
# [61] jquerylib_0.1.4    systemfonts_1.0.2  rlang_0.4.11       grid_3.6.2        
# [65] htmlwidgets_1.5.1  labeling_0.3       rmarkdown_2.11     gtable_0.3.0      
# [69] DBI_1.1.0          R6_2.4.1           knitr_1.37         dplyr_1.0.7       
# [73] uwot_0.1.10        utf8_1.1.4         workflowr_1.7.0    rprojroot_1.3-2   
# [77] ragg_0.3.1         stringi_1.4.3      parallel_3.6.2     SQUAREM_2017.10-1 
# [81] Rcpp_1.0.7         vctrs_0.3.8        tidyselect_1.1.1   xfun_0.29         
# [85] coda_0.19-3