Identify clusters in pulse-seq data using topic model
Peter Carbonetto
Last updated: 2020-09-18
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Knit directory: single-cell-topics/analysis/
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| Rmd | 60fd1ae | Peter Carbonetto | 2020-09-18 | workflowr::wflow_publish(“clusters_pulseseq.Rmd”) |
| html | 775fb91 | Peter Carbonetto | 2020-09-18 | Added step to save results in clusters_pulseseq analysis. |
| Rmd | 9cea9db | Peter Carbonetto | 2020-09-18 | workflowr::wflow_publish(“clusters_pulseseq.Rmd”) |
| html | e62fb43 | Peter Carbonetto | 2020-09-18 | Refined clustering of pulseseq data. |
| Rmd | ded741c | Peter Carbonetto | 2020-09-18 | workflowr::wflow_publish(“clusters_pulseseq.Rmd”) |
| html | 4b4b233 | Peter Carbonetto | 2020-09-18 | Make some improvements to clusters_pulseseq analysis. |
| Rmd | d2cb602 | Peter Carbonetto | 2020-09-16 | workflowr::wflow_publish(“clusters_pulseseq.Rmd”) |
| html | 13a5956 | Peter Carbonetto | 2020-09-16 | Built clusters_pulseseq page. |
| Rmd | 5e0ee23 | Peter Carbonetto | 2020-09-16 | Completed first rough draft of clustering in clusters_pulseseq.Rmd. |
| Rmd | 337d6fc | Peter Carbonetto | 2020-09-16 | Added clusters identified in PCs 5 and 6 of k=11 pulse-seq fit. |
| Rmd | c0a27bd | Peter Carbonetto | 2020-09-16 | Added clustering of pulseseq data along PCs 3 and 4. |
| html | e7383b2 | Peter Carbonetto | 2020-09-16 | Produced first rendering of clusters_pulseseq analysis. |
| Rmd | 1dd20d4 | Peter Carbonetto | 2020-09-16 | workflowr::wflow_publish(“clusters_pulseseq.Rmd”) |
| Rmd | da9ac09 | Peter Carbonetto | 2020-09-16 | Added hexbin plots to clusters_pulseseq analysis. |
| Rmd | 485639a | Peter Carbonetto | 2020-09-16 | Working on clusters_pulseseq analysis. |
| 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 pulse-seq 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 pulse-seq data. The UMI counts are not needed for this analysis.
load("../data/pulseseq.RData")
x <- as.character(samples$tissue)
x[x == "club (hillock-associated)"] <- "hillock"
x[x == "goblet.1" | x == "goblet.2" | x == "goblet.progenitor"] <- "goblet"
x[x == "tuft.1" | x == "tuft.2" | x == "tuft.progenitor"] <- "tuft"
samples$tissue <- factor(x)
rm(counts)Load the \(k = 11\) Poisson NMF fit.
fit <- readRDS("../output/pulseseq/rds/fit-pulseseq-scd-ex-k=11.rds")$fitIdentify clusters from principal components
To identify clusters, we begin by plotting PCs computed from the topic proportions. (Note that only 10 PCs are needed for 11 topics.)
p1 <- basic_pca_plot(fit,1:2)
p2 <- basic_pca_plot(fit,3:4)
p3 <- basic_pca_plot(fit,5:6)
p4 <- basic_pca_plot(fit,7:8)
p5 <- basic_pca_plot(fit,9:10)
plot_grid(p1,p2,p3,p4,p5,nrow = 2,ncol = 3)
| Version | Author | Date |
|---|---|---|
| e7383b2 | Peter Carbonetto | 2020-09-16 |
The structure is more evident from “hexbin” plots showing the density of the points example, two clusters in PCs 1 and 2 emerge in the hexbin plot:
p6 <- pca_hexbin_plot(fit,1:2) + guides(fill = "none")
p7 <- pca_hexbin_plot(fit,3:4) + guides(fill = "none")
p8 <- pca_hexbin_plot(fit,5:6) + guides(fill = "none")
p9 <- pca_hexbin_plot(fit,7:8) + guides(fill = "none")
p10 <- pca_hexbin_plot(fit,9:10) + guides(fill = "none")
plot_grid(p6,p7,p8,p9,p10,nrow = 2,ncol = 3)
| Version | Author | Date |
|---|---|---|
| e7383b2 | Peter Carbonetto | 2020-09-16 |
From these PCA plots, we define 4 clusters, labeled A, C, Cil and T+N. (The reasoning behind these labels will become clear later.) Points that do not fit in any of these clusters are assigned to a “background cluster”, labeled U for “unknown”.
pca <- prcomp(poisson2multinom(fit)$L)$x
x <- rep("U",nrow(pca))
pc3 <- pca[,3]
pc4 <- pca[,4]
pc5 <- pca[,5]
pc6 <- pca[,6]
x[pc4 < 5.5*pc3 + 0.5] <- "A"
x[(pc3 + 0.7)^2 + (pc4 - 0.1)^2 < 0.18^2] <- "Cil"
x[x == "A" & pc6 > 1.3*pc5 + 0.87] <- "T+N"
x[x == "A" & pc5 > -0.4 & pc6 < 1.3*pc5 + 0.49] <- "C"We can refine cluster A somewhat by plotting PCs computed from cluster A only:
rows <- which(x == "A")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p11 <- basic_pca_plot(fit2,1:2)
print(p11)
We label this refined cluster as “I”, and the rest are added to the background cluster (U).
pca <- prcomp(fit2$L)$x
y <- rep("U",nrow(pca))
pc1 <- pca[,1]
y[pc1 > -0.1] <- "I"
x[rows] <- yLikewise, we inspect cluster C for further structure:
rows <- which(x == "C")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p12 <- basic_pca_plot(fit2,1:2)
p13 <- pca_hexbin_plot(fit2,1:2) + guides(fill = "none")
plot_grid(p12,p13)
The hexbin plot suggests two clusters. Although these clusters are not at all distinct, it may nonetheless be useful to subdivide these points (somewhat arbitrarily) into two subsets, labeled B and C.
pca <- prcomp(fit2$L)$x
y <- rep("C",nrow(pca))
pc1 <- pca[,1]
pc2 <- pca[,2]
y[pc2 > -pc1 - 0.15] <- "B"
x[rows] <- yWithin cluster B, there is some interesting structure along PCs 5 and 6:
rows <- which(x == "B")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p14 <- basic_pca_plot(fit2,5:6)
p15 <- pca_hexbin_plot(fit2,5:6) + guides(fill = "none")
plot_grid(p14,p15)
From PCs 5 and 6 define a new cluster, “P”, recognizing that this cluster is not particularly distinct.
pca <- prcomp(fit2$L)$x
y <- rep("B",nrow(pca))
pc5 <- pca[,5]
pc6 <- pca[,6]
y[pc5 > 0.1 & pc6 < 0] <- "P"
x[rows] <- yIn summary, we have subdivided the pulse-seq data into 7 subsets, which includes a background cluster (U).
colors <- c("royalblue","forestgreen","firebrick","darkmagenta",
"darkorange","peru","gainsboro")
p16 <- labeled_pca_plot(fit,1:2,x,colors) + guides(fill = "none")
p17 <- labeled_pca_plot(fit,3:4,x,colors) + guides(fill = "none")
p18 <- labeled_pca_plot(fit,5:6,x,colors) + guides(fill = "none")
p19 <- labeled_pca_plot(fit,1:2,x,colors,"tissue") +
xlim(0,0) + ylim(0,0)
plot_grid(p16,p17,p18,p19)
Comparing this to the Montoro et al (2018) clustering, we observe some close correspondence (e.g., B and “basal cells”, P and “proliferating cells”). The B and C clusters appear to contain additional structure that is not well-captured by clusters. We will explore this additional structure in subsequent analyses.
samples$cluster <- factor(x)
with(samples,table(tissue,cluster))
# cluster
# tissue B C Cil I P T+N U
# basal 40389 1468 0 0 199 0 37
# ciliated 0 0 2896 0 6 0 114
# club 1677 11834 0 6 32 1 18
# goblet 3 396 0 0 2 0 2
# hillock 89 4036 0 0 4 0 3
# ionocyte 0 45 0 193 15 8 15
# neuroendocrine 0 1 0 7 0 619 3
# proliferating 61 194 9 4 914 0 231
# tuft 0 18 0 12 4 691 9Save results
Save the clustering of the pulse-seq data to an RDS file.
saveRDS(samples,"clustering-pulseseq.rds")
sessionInfo()
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