Last updated: 2022-02-13

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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)]
dim(counts)
# [1]   637 15594

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
0ed3329 Peter Carbonetto 2022-02-11
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)

Version Author Date
0ed3329 Peter Carbonetto 2022-02-11 
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)

Version Author Date
0ed3329 Peter Carbonetto 2022-02-11

These volcano plots summarize the results of the GoM DE analysis after merging topics 3 and 4:

p1 <- volcano_plot(de_merged,k = "k1",ymax = 50)
p2 <- volcano_plot(de_merged,k = "k2",ymax = 100)
p3 <- volcano_plot(de_merged,k = "k3+k4",ymax = 100)
p4 <- volcano_plot(de_merged,k = "k5",ymax = 100)
plot_grid(p1,p2,p3,p4)

Version Author Date
42a69cb Peter Carbonetto 2022-02-12
323f198 Peter Carbonetto 2022-02-11

Judging by the most distinctive genes in these volcano plots, the topics capture tuft cells (topic 1), neuroendocrine cells (topic 2) and ciliated cells (topics 3 + 4).

Meanwhile, from the structure plot above, we see that topics 3 and 4 capture continuous variation in the ciliated cells:

p5 <- volcano_plot(de,k = "k3",ymax = 50)
p6 <- volcano_plot(de,k = "k4",ymax = 200)
plot_grid(p5,p6)

Version Author Date
42a69cb Peter Carbonetto 2022-02-12
323f198 Peter Carbonetto 2022-02-11

These same results can also be explored in interactive volcano plots:

volcano_plotly(de_merged,k = "k1",ymax = 50,
               file = "volcano_plot_droplet_rare_tuft.html",
               width = 600,height = 600)
volcano_plotly(de_merged,k = "k2",ymax = 100,
               file = "volcano_plot_droplet_rare_neuroendocrine.html",
               width = 600,height = 600)
volcano_plotly(de_merged,k = "k3+k4",ymax = 100,
               file = "volcano_plot_droplet_rare_ciliated.html",
               width = 600,height = 600)
volcano_plotly(de_merged,k = "k5",ymax = 100,
               file = "volcano_plot_droplet_rare_k5.html",
               width = 600,height = 600)
volcano_plotly(de,k = "k3",ymax = 50,
               file = "volcano_plot_droplet_rare_k3.html",
               width = 600,height = 600)
volcano_plotly(de,k = "k4",ymax = 200,
               file = "volcano_plot_droplet_rare_k4.html",
               width = 600,height = 600)

You can explore these interactive volcano plots by following these links:


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:
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