Last updated: 2020-09-18

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File Version Author Date Message
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")$fit

Identify clusters from principal components

To identify clusters, we begin by plotting PCs computed from the rows of the topic proportions matrix. (Note that only 10 PCs are needed for the multinomial topic model with \(k = 11\) 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

Structure is more evident from the following “hexbin” plots showing the density of the points in the PCA projection. For example, the two clusters in PCs 1 and 2 is not evident from the scatterplot, but emerges in the hexbin plots:

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 the PC plots above, we define 4 clusters, labeled A, C, Cil and T+N. (The reason for these labels will become more clear later on.) Points that do not fit in any of these clusters are assigned to a “background cluster” (this subset is 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 the samples in cluster A only:

rows <- which(x == "A")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p11  <- basic_pca_plot(fit2,1:2)
print(p11)

Version Author Date
13a5956 Peter Carbonetto 2020-09-16

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] <- y

Likewise, 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)

Version Author Date
13a5956 Peter Carbonetto 2020-09-16

The hexbin plot suggests two clusters. Although these clusters are not 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] <- y

In 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)

Version Author Date
13a5956 Peter Carbonetto 2020-09-16

We define a new cluster, “P”, recognizing that this cluster is again not very 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] <- y

In summary, we have subdivided the pulse-seq samples into 7 subsets, including 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)

Version Author Date
4b4b233 Peter Carbonetto 2020-09-18

Comparing this to the Montoro et al (2018) clustering, we see that some close correspondence (e.g., B and basal cells, P and proliferating cells). The B and C clusters appear to contain additional structure that cannot be captured by distinct clusters. We will explore this structure in a subsequent analysis.

table(samples$tissue,x)
#                 x
#                      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     9

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Add text here.


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
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