Identify clusters in droplet data using topic model
Peter Carbonetto
Last updated: 2020-10-06
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Knit directory: single-cell-topics/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f4e0448 | Peter Carbonetto | 2020-10-06 | workflowr::wflow_publish(“clusters_droplet.Rmd”, verbose = TRUE) |
| html | 3bada76 | Peter Carbonetto | 2020-10-04 | Added PCA plots to clusters_droplet analysis showing substructure in |
| Rmd | 198eaf8 | Peter Carbonetto | 2020-10-04 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| Rmd | 5e57ced | Peter Carbonetto | 2020-10-03 | Working on plots highlighting substructure in T+N cluster. |
| html | ab1ed99 | Peter Carbonetto | 2020-09-27 | Resized plot in clusters_droplet analysis. |
| Rmd | 306f5dc | Peter Carbonetto | 2020-09-27 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | bf299b9 | Peter Carbonetto | 2020-09-27 | Use pca_plot_with_counts in clusters_droplet analysis. |
| Rmd | 54f44d3 | Peter Carbonetto | 2020-09-27 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | 4fe31a6 | Peter Carbonetto | 2020-09-22 | Build site. |
| Rmd | ffdc209 | Peter Carbonetto | 2020-09-22 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | 2ddbe33 | Peter Carbonetto | 2020-09-21 | A couple refinements to clusters_droplet analysis. |
| Rmd | 2a5e8da | Peter Carbonetto | 2020-09-21 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | 0a8b571 | Peter Carbonetto | 2020-09-21 | Added PCA plots showing continuous variation in club cells. |
| Rmd | 605b540 | Peter Carbonetto | 2020-09-21 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | db6135c | Peter Carbonetto | 2020-09-21 | Added B+C cluster to clustering of droplet data, and added plot |
| Rmd | 69d1f0a | Peter Carbonetto | 2020-09-21 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| Rmd | e2a8071 | Peter Carbonetto | 2020-09-20 | Saved new clustering-droplet.rds. |
| html | b5e1a7e | Peter Carbonetto | 2020-09-20 | Fixed up Structure plot in clusters_droplet analysis. |
| 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(Matrix)
library(dplyr)
library(fastTopics)
library(ggplot2)
library(cowplot)
source("../code/plots.R")Load data and results
Load the droplet data.
load("../data/droplet.RData")Load the \(k = 7\) Poisson NMF model fit.
fit <- readRDS("../output/droplet/rds/fit-droplet-scd-ex-k=7.rds")$fitIdentify clusters from principal components
To identify clusters, we begin by plotting PCs computed from the topic proportions. (Note that only 6 PCs are needed for 7 topics.)
p1 <- pca_plot(poisson2multinom(fit),pcs = 1:2,fill = "none")
p2 <- pca_plot(poisson2multinom(fit),pcs = 3:4,fill = "none")
p3 <- pca_plot(poisson2multinom(fit),pcs = 5:6,fill = "none")
plot_grid(p1,p2,p3,nrow = 1,ncol = 3)
| Version | Author | Date |
|---|---|---|
| b1cb82e | Peter Carbonetto | 2020-09-19 |
Some of the structure is more evident from “hexbin” plots showing the density of the points.
breaks <- c(0,1,5,10,100,Inf)
p4 <- pca_hexbin_plot(poisson2multinom(fit),pcs = 1:2,breaks = breaks)
p5 <- pca_hexbin_plot(poisson2multinom(fit),pcs = 3:4,breaks = breaks)
p6 <- pca_hexbin_plot(poisson2multinom(fit),pcs = 5:6,breaks = breaks)
p4 <- p4 + guides(fill = "none")
p5 <- p5 + guides(fill = "none")
p6 <- p6 + guides(fill = "none")
plot_grid(p4,p5,p6,nrow = 1,ncol = 3)
| Version | Author | Date |
|---|---|---|
| b1cb82e | Peter Carbonetto | 2020-09-19 |
From these PCA plots, we define 4 clusters, labeled A, Cil, G and T+N. (The reasoning behind these labels will become clear later.) Points that do not fit in any of these clusters are assigned to a “background cluster”, labeled U.
pca <- prcomp(poisson2multinom(fit)$L)$x
x <- rep("U",nrow(pca))
pc1 <- pca[,1]
pc2 <- pca[,2]
pc6 <- pca[,6]
x[pc2 > -0.15] <- "A"
x[pc1 > 0.3 & pc2 < -0.75] <- "Cil"
x[pc1 <= 0.3 & pc2 >= -0.75 & pc2 < -0.4] <- "T+N"
x[pc6 < -0.05] <- "G"There is additional substructure in cluster A, which is more apparent in the projection onto the top 2 PCs computed from cluster A only.
rows <- which(x == "A")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p7 <- pca_plot(fit2,fill = "none")
p8 <- pca_hexbin_plot(fit2,1:2,breaks = breaks) + guides(fill = "none")
plot_grid(p7,p8)
The variation in PCs 1 and 2 is mostly produced by topics 2, 4 and 5.
p9 <- pca_plot(fit2,k = c(2,4,5))
print(p9)
Topic 4 in particular corresponds closely to expression of Krt13 which was identified as being uniquely expressed by transitional “hillock” cells.
p10 <- pca_plot(fit2,fill = log10(counts[rows,"Krt13"])) +
labs(fill = "log10(count)",title = "Krt13")
print(p10)
We label the three more-or-less distinct subclusters as B, C and H, and assign the remaining “in between” data points to a new “background cluster”, B+C.
pca <- prcomp(fit2$L)$x
pc1 <- pca[,1]
pc2 <- pca[,2]
y <- rep("B+C",nrow(pca))
y[pc1 < 0.1] <- "B"
y[pc1 > 0.4 & pc2 < 0.45] <- "C"
y[pc2 > 0.55] <- "H"
x[rows] <- yIn summary, we have subdivided the droplet data into 8 subsets.
samples$cluster <- factor(x,c("B","C","B+C","H","Cil","T+N","G","U"))
cluster_colors <- c("royalblue", # B
"forestgreen", # C
"slategray", # B+C
"turquoise", # H
"firebrick", # Cil
"darkorange", # T+N
"gold", # G
"gainsboro") # U
p11 <- pca_plot(poisson2multinom(fit),pcs = 1:2,fill = samples$cluster) +
scale_fill_manual(values = cluster_colors) +
labs(fill = "cluster")
p12 <- pca_plot(poisson2multinom(fit),pcs = 3:4,fill = samples$cluster) +
scale_fill_manual(values = cluster_colors) +
labs(fill = "cluster")
p13 <- pca_plot(poisson2multinom(fit),pcs = 5:6,fill = samples$cluster) +
scale_fill_manual(values = cluster_colors) +
labs(fill = "cluster")
plot_grid(p11,p12,p13,nrow = 1,ncol = 3)
The clusters identified here correspond well to the Montoro et al (2018) clustering, with some exceptions (e.g., we do not identify an ionocytes cluster, and the neuroendocrine and tuft cells are included in the same cluster).
with(samples,table(tissue,cluster))
# cluster
# tissue B C B+C H Cil T+N G U
# Basal 3682 16 142 5 0 0 0 0
# Ciliated 1 13 4 0 371 5 0 31
# Club 93 1878 411 192 0 0 2 2
# Goblet 2 20 1 0 0 0 42 0
# Ionocyte 9 0 1 0 0 1 1 14
# Neuroendocrine 27 4 6 0 0 51 0 8
# Tuft 27 5 5 0 1 111 2 7Structure plot
The structure plot summarizes the topic proportions in each of these 8 subsets:
set.seed(1)
topic_colors <- c("gold","royalblue","salmon","turquoise","olivedrab",
"firebrick","forestgreen")
topics <- c(3,4,5,1,7,2,6)
rows <- sort(c(sample(which(samples$cluster == "B"),800),
sample(which(samples$cluster == "C"),800),
which(samples$cluster == "B+C"),
which(samples$cluster == "H"),
which(samples$cluster == "Cil"),
which(samples$cluster == "T+N"),
which(samples$cluster == "G"),
which(samples$cluster == "U")))
p14 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = samples[rows,"cluster"],
topics = topics,colors = topic_colors[topics],
perplexity = c(70,70,30,30,50,30,12,18),
n = Inf,gap = 30,num_threads = 4,verbose = FALSE)
print(p14)
Based on this structure plot, and the above results, we roughly subdivide the droplet data into two: (1) the Cil, T+N and G clusters that give rise to well-separated clusters, and (2) the B, C, B+C and H subsets that contain interesting substructure but much less distinct clustering. Therefore,the cluster labels B, C, B+C and H are useful as a guide but should be taken with a grain of salt as the boundaries between these clusters are somewhat arbitrary.
Note the distribution of the topics in cluster C suggests that there is further substantial heterogeneity in these cells beyond what can be captured by identifying “hard” clusters. In particular, there is additional continuous variation in gene expression primarily captured by topics 5 and 7:
rows <- which(is.element(x,c("B","C","H","B+C")))
fit2 <- select(poisson2multinom(fit),loadings = rows)
p15 <- pca_plot(fit2,pcs = 2:3,k = c(4,5,7))
print(p15)
| Version | Author | Date |
|---|---|---|
| 3bada76 | Peter Carbonetto | 2020-10-04 |
Although subtle, there is variation in topic 2 and 6 within the T+N cluster that tracks closely with the tuft (here signaled by gene Gnat3) and pulmonary neuroendocrine (Chga) cell-types:
p16 <- pca_plot(poisson2multinom(fit),k = 2)
p17 <- pca_plot(poisson2multinom(fit),k = 6)
p18 <- pca_plot(poisson2multinom(fit),fill = log10(counts[,"Chga"])) +
labs(fill = "log10(count)",title = "Chga")
p19 <- pca_plot(poisson2multinom(fit),fill = log10(counts[,"Gnat3"])) +
labs(fill = "log10(count)",title = "Gnat3")
plot_grid(p16,p17,p18,p19)
| Version | Author | Date |
|---|---|---|
| 3bada76 | Peter Carbonetto | 2020-10-04 |
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
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