Last updated: 2020-08-25

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Knit directory: single-cell-topics/analysis/

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TO DO: Add introductory 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(dplyr)
library(fastTopics)
library(ggplot2)
library(cowplot)
source("../code/plots.R")

Droplet data

We begin with the droplet data. Note that the count data are no longer needed at this stage.

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

Load the \(k = 7\) Poisson NMF model fit.

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

The Montoro et al (2018) article mentions that some epithelial cell types are abundant whereas others are rare. The topics inferred from the droplet data reflect this:

p1 <- create_abundance_plot(fit_droplet)
print(p1)

Version Author Date
0a04fc1 Peter Carbonetto 2020-08-18

The first topic—which is not actually visible in this bar chart—is indeed very rare; only 43 out of 7,193 samples have a greater than 10% contribution from this topic.

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

In this next part of the analysis, we perform PCA on the estimated topic proportions to explore structure in the data as inferred by the topic model. Typically, a nonlinear embedding method such as t-SNE or UMAP is used to visualize the structure. The disadvantage of such methods is that it can often be difficult to get the (many) tuning parameters right, they can be slow when applied to large data sets, and the embeddings are not unique; by contrast, PCA has no tuning parameters, and the principal components (PCs) are unique.

fit <- poisson2multinom(fit_droplet)
pca <- prcomp(fit$L)$x

In the projection onto four of the PCs—PCs 1, 2, 5 and 6—we can delineate 4 clusters. A fifth subset (“E”) is used as a “background” cluster. (Note that PCs 3 and 4 do not reveal any additional substrcture, so are not shown.)

n   <- nrow(pca)
x   <- rep("E",n)
pc1 <- pca[,"PC1"]
pc2 <- pca[,"PC2"]
pc6 <- pca[,"PC6"]
x[pc2 > -0.1]  <- "A"
x[pc6 < -0.04] <- "B"
x[(pc1 - 0)^2 + (pc2 + 0.75)^2 < 0.09]  <- "C"
x[(pc1 - 0.5)^2 + (pc2 + 0.9)^2 < 0.04] <- "D"
samples_droplet$cluster <- x
p1 <- pca_plot_with_labels(fit_droplet,c("PC1","PC2"),x) +
      labs(fill = "cluster")
p2 <- pca_plot_with_labels(fit_droplet,c("PC5","PC6"),x) +
      labs(fill = "cluster")
plot_grid(p1,p2)

Version Author Date
97c13c2 Peter Carbonetto 2020-08-25
e11855b Peter Carbonetto 2020-08-25
adda33f Peter Carbonetto 2020-08-19
0a16b60 Peter Carbonetto 2020-08-19

The vast majority of the cells are in cluster A:

table(x)
# x
#    A    B    C    D    E 
# 6533   50  162  375   73

Cluster A further subdivides into two not-quite-so-distinct subclusters, otherwise there does not appear to be any other interesting substructure to delineate:

rows <- which(samples_droplet$cluster == "A")
fit  <- select(poisson2multinom(fit_droplet),loadings = rows)
pca  <- prcomp(fit$L)$x
n    <- nrow(pca)
x    <- rep("A1",n)
pc1  <- pca[,1]
x[pc1 > 0.2] <- "A2"
samples_droplet[rows,"cluster"] <- x
p3 <- pca_plot_with_labels(fit,c("PC1","PC2"),x) +
      labs(fill = "cluster")
print(p3)

Version Author Date
97c13c2 Peter Carbonetto 2020-08-25

In summary, we have subdivided the data into 6 subsets:

samples_droplet$cluster <- factor(samples_droplet$cluster)
table(samples_droplet$cluster)
# 
#   A1   A2    B    C    D    E 
# 3968 2565   50  162  375   73

The structure plot summarizes the topic proportions in each of these 6 subsets:

set.seed(2)
droplet_topic_colors <- c("gold","royalblue","turquoise","greenyellow",
                          "forestgreen","firebrick","olivedrab")
droplet_topics <- c(1,3,7,4,5,6,2)
rows <- sort(c(sample(which(samples_droplet$cluster == "A1"),800),
               sample(which(samples_droplet$cluster == "A2"),500),
               which(samples_droplet$cluster == "B"),
               which(samples_droplet$cluster == "C"),
               sample(which(samples_droplet$cluster == "D"),200),
               which(samples_droplet$cluster == "E"),200))
p4 <- structure_plot(select(poisson2multinom(fit_droplet),loadings = rows),
                     grouping = samples_droplet[rows,"cluster"],
                     topics = droplet_topics,
                     colors = droplet_topic_colors[droplet_topics],
                     perplexity = c(100,70,12,50,50,20),
                     n = Inf,gap = 40,num_threads = 4,verbose = FALSE)
print(p4)

Version Author Date
97c13c2 Peter Carbonetto 2020-08-25

The bulk of the samples lie on a continuous gradient between topics 2 and 5. There is a smaller cluster at the bottom of this plot, with high contributions from topic 6.

Along these PCs, we see that topics 3, 4, 5 and 7 exist in many combinations, with no apparent discrete populations.

Topic 1 captures a very small discrete population of cells:

In summary, topics 1 and 6 pick up discrete “cell types”, whereas the other topics characterize more continuous variation in gene expression, perhaps cell types along a continuous trajectory of development. There are some other discrete clusters that seem to be composed of distinct combinations of topics that we will need to examine more closely.

Compare these clusters with the clusters identified by Montoro et al (2018):

pdat <- as.data.frame(with(samples_droplet,table(tissue,cluster)))
p5 <- ggplot(pdat,aes(x = cluster,y = tissue,size = Freq)) +
  geom_point(color = "dodgerblue",na.rm = TRUE,show.legend = FALSE) +
  scale_size_continuous(range = c(0.1,8)) +
  ylim(rev(levels(samples_droplet$tissue))) +
  theme_cowplot(font_size = 10)
print(p5)

Pulse-seq data

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

Load the \(k = 9\) and \(k = 11\) Poisson NMF fits for the pulse-seq data.

fit_pulseseq9 <- readRDS("../output/pulseseq/rds/fit-pulseseq-scd-ex-k=9.rds")$fit
fit_pulseseq11 <- readRDS("../output/pulseseq/rds/fit-pulseseq-scd-ex-k=11.rds")$fit

Likewise, we also pick up rare and abundant topics in the pulse-seq data:

p2 <- create_abundance_plot(fit_pulseseq)
print(p2)
temp <- select(poisson2multinom(fit_droplet),
               loadings = which(samples_droplet$tissue == "Ionocyte"))
structure_plot(temp,rows = order(temp$L[,3]),
                      topics = topic_ordering,
                      colors = droplet_topic_colors[topic_ordering])
temp2 <- select(poisson2multinom(fit_droplet),
                loadings = which(samples_droplet$tissue == "Tuft"))
structure_plot(temp2,perplexity = 30,
               topics = topic_ordering,
               colors = droplet_topic_colors[topic_ordering])
temp3 <- select(poisson2multinom(fit_droplet),
                loadings = which(samples_droplet$tissue == "Neuroendocrine"))
structure_plot(temp3,perplexity = 30,
               topics = topic_ordering,
               colors = droplet_topic_colors[topic_ordering])

It is helpful to compare these results with clustering reported in the Montoro et al (2018) paper. To make this comparison, we layer the 7 clusters on top of these PCs:

droplet_celltype_colors <-
  c("royalblue",      # Basal
    "firebrick",      # Ciliated
    "forestgreen",    # Club
    "gold",           # Goblet
    "darkmagenta",    # Ionocyte
    "darkorange",     # Neuroendocrine
    "lightsteelblue") # Tuft
p9 <- pca_plot_with_labels(fit_droplet,c("PC1","PC2"),samples_droplet$tissue,
                           droplet_celltype_colors) + labs(fill = "celltype")
p10 <- pca_plot_with_labels(fit_droplet,c("PC4","PC5"),samples_droplet$tissue,
                            droplet_celltype_colors) + labs(fill = "celltype")
p11 <- pca_plot_with_labels(fit_droplet,c("PC5","PC6"),samples_droplet$tissue,
                            droplet_celltype_colors) + labs(fill = "celltype")
plot_grid(p9,p10,p11)

TO DO: Add Structure plot9s) to compare Montoro et al (2018) clustering.

And likewise in the pulse-seq data:

p5 <- basic_pca_plot(fit_pulseseq,c("PC3","PC4"))
p6 <- basic_pca_plot(fit_pulseseq,c("PC5","PC6"))
plot_grid(p5,p6)
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)
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)
clrs <- c("royalblue",      # basal
          "firebrick",      # ciliated
          "forestgreen",    # club
          "gold",           # goblet
          "darkmagenta",    # ionocyte
          "darkorange",     # neuroendocrine
          "tomato",         # proliferating
          "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_0.3-165 dplyr_0.8.3       
# 
# 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_1.4.1          
# [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] 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_5.0.2   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