Identify clusters in droplet data using topic model
Peter Carbonetto
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")$fitIdentify clusters from principal components
To identify clusters, we begin by plotting PCs computed from the topic proportions. (Note that only 6 PCs are needed for 7 topics.)
p1 <- basic_pca_plot(fit,1:2)
p2 <- basic_pca_plot(fit,3:4)
p3 <- basic_pca_plot(fit,5:6)
plot_grid(p1,p2,p3,nrow = 1,ncol = 3)
| Version | Author | Date |
|---|---|---|
| b1cb82e | Peter Carbonetto | 2020-09-19 |
Some of the structure is more evident from “hexbin” plots showing the density of the points.
bins <- c(0,1,5,10,100,Inf)
p4 <- pca_hexbin_plot(fit,1:2,bins = bins) + guides(fill = "none")
p5 <- pca_hexbin_plot(fit,3:4,bins = bins) + guides(fill = "none")
p6 <- pca_hexbin_plot(fit,5:6,bins = bins) + guides(fill = "none")
plot_grid(p4,p5,p6,nrow = 1,ncol = 3)
| Version | Author | Date |
|---|---|---|
| b1cb82e | Peter Carbonetto | 2020-09-19 |
From these PCA plots, we define 4 clusters, labeled A, Cil, G and T+N. (The reasoning behind these labels will become clear later.) Points that do not fit in any of these clusters are assigned to a “background cluster”, labeled U.
pca <- prcomp(poisson2multinom(fit)$L)$x
x <- rep("U",nrow(pca))
pc1 <- pca[,1]
pc2 <- pca[,2]
pc6 <- pca[,6]
x[pc2 > -0.15] <- "A"
x[pc1 > 0.3 & pc2 < -0.75] <- "Cil"
x[pc1 <= 0.3 & pc2 >= -0.75 & pc2 < -0.4] <- "T+N"
x[pc6 < -0.05] <- "G"There is additional substructure in cluster A, which is more apparent in the projection onto the top 2 PCs computed from cluster A only.
rows <- which(x == "A")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p7 <- basic_pca_plot(fit2,1:2)
p8 <- pca_hexbin_plot(fit2,1:2,bins = bins) + guides(fill = "none")
plot_grid(p7,p8)
Add text here.
p9 <- pca_plot(fit2,pcs = 1:2,k = c(2,4,5))
print(p9)
| Version | Author | Date |
|---|---|---|
| 4172024 | Peter Carbonetto | 2020-09-20 |
We label the three more-or-less distinct subclusters as B, C and H, and assign the remaining samples to the background cluster.
pca <- prcomp(fit2$L)$x
y <- rep("U",nrow(pca))
pc1 <- pca[,1]
pc2 <- pca[,2]
y[pc1 < 0.1] <- "B"
y[pc1 > 0.4 & pc2 < 0.45] <- "C"
y[pc2 > 0.55] <- "H"
x[rows] <- yIn summary, we have subdivided the droplet data into 7 subsets, including a background cluster (U).
colors <- c("royalblue","forestgreen","firebrick","gold","turquoise",
"darkorange","gainsboro")
p10 <- labeled_pca_plot(fit,1:2,x,colors,font_size = 8)
p11 <- labeled_pca_plot(fit,3:4,x,colors,font_size = 8)
p12 <- labeled_pca_plot(fit,5:6,x,colors,font_size = 8)
plot_grid(p10,p11,p12,nrow = 1,ncol = 3)
| Version | Author | Date |
|---|---|---|
| 4172024 | Peter Carbonetto | 2020-09-20 |
The clusters identified here correspond well to the Montoro et al (2018) clustering, with some exceptions (e.g., we do not identify an ionocytes cluster, and the neuroendocrine and tuft cells are included in the same cluster).
samples$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 12Structure 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)
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# attached base packages:
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