Last updated: 2020-09-20

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Knit directory: single-cell-topics/analysis/

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File Version Author Date Message
Rmd b7d1acc Peter Carbonetto 2020-09-20 workflowr::wflow_publish(“clusters_droplet.Rmd”)
html 4172024 Peter Carbonetto 2020-09-20 Identified H cluster in droplet data.
Rmd decefd4 Peter Carbonetto 2020-09-20 workflowr::wflow_publish(“clusters_droplet.Rmd”)
html 5361fdf Peter Carbonetto 2020-09-19 Adjusted the plots in clusters_droplet analysis.
Rmd 7830b35 Peter Carbonetto 2020-09-19 workflowr::wflow_publish(“clusters_droplet.Rmd”)
html 311b4e8 Peter Carbonetto 2020-09-19 Made a few minor improvements to the clusters_droplet analysis.
Rmd ba90d80 Peter Carbonetto 2020-09-19 workflowr::wflow_publish(“clusters_droplet.Rmd”)
html b1cb82e Peter Carbonetto 2020-09-19 Added clustering from PCA plots to clusters_droplet analysis.
Rmd 81e7faf Peter Carbonetto 2020-09-19 workflowr::wflow_publish(“clusters_droplet.Rmd”)
Rmd c8dd3af Peter Carbonetto 2020-09-16 Implemented basic_pca_plot; improved labeled_pca_plot function.

Here we perform PCA on the topic proportions to identify clusters in the droplet data.

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")

Load data and results

Load the droplet data. The UMI counts are not needed for this analysis.

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

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

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

Identify clusters from principal components

To identify clusters, we begin by plotting PCs computed from the topic proportions. (Note that only 6 PCs are needed for 7 topics.)

p1 <- basic_pca_plot(fit,1:2)
p2 <- basic_pca_plot(fit,3:4)
p3 <- basic_pca_plot(fit,5:6)
plot_grid(p1,p2,p3,nrow = 1,ncol = 3)

Version Author Date
b1cb82e Peter Carbonetto 2020-09-19

Some of the structure is more evident from “hexbin” plots showing the density of the points.

bins <- c(0,1,5,10,100,Inf)
p4 <- pca_hexbin_plot(fit,1:2,bins = bins) + guides(fill = "none")
p5 <- pca_hexbin_plot(fit,3:4,bins = bins) + guides(fill = "none")
p6 <- pca_hexbin_plot(fit,5:6,bins = bins) + guides(fill = "none")
plot_grid(p4,p5,p6,nrow = 1,ncol = 3)

Version Author Date
b1cb82e Peter Carbonetto 2020-09-19

From these PCA plots, we define 4 clusters, labeled A, Cil, G and T+N. (The reasoning behind these labels will become clear later.) Points that do not fit in any of these clusters are assigned to a “background cluster”, labeled U.

pca <- prcomp(poisson2multinom(fit)$L)$x
x   <- rep("U",nrow(pca))
pc1 <- pca[,1]
pc2 <- pca[,2]
pc6 <- pca[,6]
x[pc2 > -0.15] <- "A"
x[pc1 > 0.3 & pc2 < -0.75] <- "Cil"
x[pc1 <= 0.3 & pc2 >= -0.75 & pc2 < -0.4] <- "T+N"
x[pc6 < -0.05] <- "G"

There is additional substructure in cluster A, which is more apparent in the projection onto the top 2 PCs computed from cluster A only.

rows <- which(x == "A")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p7   <- basic_pca_plot(fit2,1:2)
p8   <- pca_hexbin_plot(fit2,1:2,bins = bins) + guides(fill = "none")
plot_grid(p7,p8)

Version Author Date
5361fdf Peter Carbonetto 2020-09-19
311b4e8 Peter Carbonetto 2020-09-19
b1cb82e Peter Carbonetto 2020-09-19

Add text here.

p9 <- pca_plot(fit2,pcs = 1:2,k = c(2,4,5))
print(p9)

Version Author Date
4172024 Peter Carbonetto 2020-09-20

We label the three more-or-less distinct subclusters as B, C and H, and assign the remaining samples to the background cluster.

pca <- prcomp(fit2$L)$x
y   <- rep("U",nrow(pca))
pc1 <- pca[,1]
pc2 <- pca[,2]
y[pc1 < 0.1] <- "B"
y[pc1 > 0.4 & pc2 < 0.45] <- "C"
y[pc2 > 0.55] <- "H"
x[rows] <- y

In summary, we have subdivided the droplet data into 7 subsets, including a background cluster (U).

colors <- c("royalblue","forestgreen","firebrick","gold","turquoise",
            "darkorange","gainsboro")
p10 <- labeled_pca_plot(fit,1:2,x,colors,font_size = 8)
p11 <- labeled_pca_plot(fit,3:4,x,colors,font_size = 8)
p12 <- labeled_pca_plot(fit,5:6,x,colors,font_size = 8)
plot_grid(p10,p11,p12,nrow = 1,ncol = 3)

Version Author Date
4172024 Peter Carbonetto 2020-09-20

The clusters identified here correspond well to the Montoro et al (2018) clustering, with some exceptions (e.g., we do not identify an ionocytes cluster, and the neuroendocrine and tuft cells are included in the same cluster).

samples$cluster <- factor(x,c("B","C","H","Cil","T+N","G","U"))
with(samples,table(tissue,cluster))
#                 cluster
# tissue              B    C    H  Cil  T+N    G    U
#   Basal          3682   16    5    0    0    0  142
#   Ciliated          1   13    0  371    5    0   35
#   Club             93 1878  192    0    0    2  413
#   Goblet            2   20    0    0    0   42    1
#   Ionocyte          9    0    0    0    1    1   15
#   Neuroendocrine   27    4    0    0   51    0   14
#   Tuft             27    5    0    1  111    2   12

Structure plot

Add text here.

set.seed(1)
topic_colors <- c("gold","royalblue","salmon","turquoise","olivedrab",
                  "firebrick","forestgreen")
topics <- c(3,4,5,1,7,2,6)
rows <- sort(c(sample(which(samples$cluster == "B"),800),
               sample(which(samples$cluster == "C"),800),
               which(samples$cluster == "H"),
               which(samples$cluster == "Cil"),
               which(samples$cluster == "T+N"),
               which(samples$cluster == "G"),
               which(samples$cluster == "U")))
p13 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
                      grouping = samples[rows,"cluster"],
                      topics = topics,colors = topic_colors[topics],
                      perplexity = c(70,70,30,50,30,12,50),
                      n = Inf,gap = 30,num_threads = 4,verbose = FALSE)
print(p13)

Version Author Date
4172024 Peter Carbonetto 2020-09-20

TO DO: Show PCA plots suggesting additional hetergeneity in C cluster.

Save results

Save the clustering of the droplet data to an RDS file.

saveRDS(samples,"clustering-droplet.rds")

sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.6
# 
# 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_0.3-175 dplyr_0.8.3       
# 
# loaded via a namespace (and not attached):
#  [1] ggrepel_0.9.0        Rcpp_1.0.5           lattice_0.20-38     
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