Last updated: 2020-09-22

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Knit directory: single-cell-topics/analysis/

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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(Matrix)
library(dplyr)
library(fastTopics)
library(ggplot2)
library(cowplot)
source("../code/plots.R")

Load data and results

Load the pulse-seq data.

load("../data/pulseseq.RData")
x <- as.character(samples$tissue)
x[x == "club (hillock-associated)"] <- "club"
x[x == "goblet.1" | x == "goblet.2" | x == "goblet.progenitor"] <- "goblet"
x[x == "tuft.1" | x == "tuft.2" | x == "tuft.progenitor"] <- "tuft"
samples$tissue <- factor(x)

Load the \(k = 11\) Poisson NMF model 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 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

Some of the structure is more evident from “hexbin” plots showing the density of the points. For example, clear clusters emerge in the hexbin plots for PCs 3 and 4, and for PCs 5 and 6:

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, D, Cil and T+N. (The reasoning behind these cluster 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] <- "D"

We can refine the small 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)

Version Author Date
65f104e Peter Carbonetto 2020-09-18
775fb91 Peter Carbonetto 2020-09-18
13a5956 Peter Carbonetto 2020-09-16

We label this refined cluster as “I”, and the rest are added back 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

Similarly, we mine the much larger cluster D for substructure:

rows <- which(x == "D")
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
65f104e Peter Carbonetto 2020-09-18
775fb91 Peter Carbonetto 2020-09-18
e62fb43 Peter Carbonetto 2020-09-18
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 data points (somewhat arbitrarily) into two subsets, which we label as 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

Within 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
65f104e Peter Carbonetto 2020-09-18
e62fb43 Peter Carbonetto 2020-09-18
13a5956 Peter Carbonetto 2020-09-16

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] <- y

In summary, we have subdivided the pulse-seq data into 7 subsets, which includes a background cluster (U).

samples$cluster <- factor(x,c("B","C","P","Cil","T+N","I","U"))
cluster_colors  <- c("royalblue",   # B
                     "forestgreen", # C
                     "peru",        # P
                     "firebrick",   # Cil
                     "darkorange",  # T+N
                     "darkmagenta", # I
                     "gainsboro")   # U
p16 <- labeled_pca_plot(fit,1:2,samples$cluster,cluster_colors) +
       guides(fill = "none")
p17 <- labeled_pca_plot(fit,3:4,samples$cluster,cluster_colors) +
       guides(fill = "none")
p18 <- labeled_pca_plot(fit,5:6,samples$cluster,cluster_colors) +
       guides(fill = "none")
p19 <- labeled_pca_plot(fit,1:2,samples$cluster,cluster_colors,"tissue") +
       xlim(0,0) + ylim(0,0)
plot_grid(p16,p17,p18,p19)

Version Author Date
7489099 Peter Carbonetto 2020-09-18
942486b Peter Carbonetto 2020-09-18
775fb91 Peter Carbonetto 2020-09-18
e62fb43 Peter Carbonetto 2020-09-18
4b4b233 Peter Carbonetto 2020-09-18
571b311 Peter Carbonetto 2020-09-16

Comparing this to the Montoro et al (2018) clustering, we observe some close correspondence (e.g., B and “basal cells”, P and “proliferating cells”).

with(samples,table(tissue,cluster))
#                 cluster
# tissue               B     C     P   Cil   T+N     I     U
#   basal          40389  1468   199     0     0     0    37
#   ciliated           0     0     6  2896     0     0   114
#   club            1766 15870    36     0     1     6    21
#   goblet             3   396     2     0     0     0     2
#   ionocyte           0    45    15     0     8   193    15
#   neuroendocrine     0     1     0     0   619     7     3
#   proliferating     61   194   914     9     0     4   231
#   tuft               0    18     4     0   691    12     9

Structure plot

The structure plot summarizes the topic proportions in each of the 7 subsets:

set.seed(1)
topic_colors <- c("turquoise","darkorange","dodgerblue","gold","peru",
                  "greenyellow","firebrick","olivedrab","royalblue",
                  "forestgreen","gray")
topics <- c(11,1,3,4,5,6,8,10,9,2,7)
rows <- sort(c(sample(which(samples$cluster == "B"),1000),
               sample(which(samples$cluster == "C"),1000),
               sample(which(samples$cluster == "P"),500),
               sample(which(samples$cluster == "Cil"),500),
               sample(which(samples$cluster == "T+N"),500),
               which(samples$cluster == "I"),
               which(samples$cluster == "U")))
p20 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
                      grouping = samples[rows,"cluster"],
                      topics = topics,
                      colors = topic_colors[topics],
                      perplexity = c(70,70,30,50,50,30,70),
                      n = Inf,gap = 30,num_threads = 4,verbose = FALSE)
print(p20)

Based on this structure plot, and from the other results above, we roughly subdivide the pulse-seq data into two subsets: (1) the Cil, T+N and I clusters that give rise to fairly well-separated clusters, and (2) the B, C and P subsets that contain interesting substructure but much less distinct clusters. Therefore,the cluster labels B, C and P are useful as a guide but should be taken with a grain of salt as the boundaries between these clusters are somewhat arbitrary.

The structure plot suggests that there is substantial heterogeneity in the C cluster beyond what can be captured by “hard” clusters. In particular, three topics (\(k = 5, 6, 10\)) are largely unique to the cells in this cluster, and two other topics (\(k = 4, 8\)) primarily contribute to the cells in this cluster, although can be found in small proportions elsewhere.

These topics indeed pick up continuous variation in gene expression among the cells in this cluster:

rows <- which(x == "C")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p21 <- pca_plot(fit2,pcs = 1:2,k = c(5,8,10))
print(p21)

And in PCs 3 and 4:

p22 <- pca_plot(fit2,pcs = 3:4,k = c(4,5,6))
print(p22)

The B cell also shows a lot of heterogeneity. Topic 1 is of particular interest because of its close connection to expression of the “hillock” gene Krt13:

rows <- which(x == "B")
fit2 <- select(poisson2multinom(fit),loadings = rows)
pca  <- prcomp(fit2$L)$x
p23  <- pca_plot(fit2,pcs = 2:3,k = 1) + guides(fill = "none")
pdat <- data.frame(PC2   = pca[,2],
                   PC3   = pca[,3],
                   Krt13 = log10(counts[rows,"Krt13"]))
p24 <- ggplot(pdat,aes(x = PC2,y = PC3,fill = Krt13)) +
  geom_point(shape = 21,color = "white",size = 1.25) +
  scale_fill_gradientn(colors = c("skyblue","gold","darkorange","magenta"),
                        na.value = "lightskyblue") +
  labs(fill = "log10 expression level") +
  theme_cowplot(font_size = 10)
plot_grid(p23,p24,rel_widths = c(7,10))

Save results

Save the clustering of the pulse-seq data to an RDS file.

saveRDS(samples,"clustering-pulseseq.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       
# [5] Matrix_1.2-18     
# 
# loaded via a namespace (and not attached):
#  [1] ggrepel_0.9.0        Rcpp_1.0.5           lattice_0.20-38     
#  [4] tidyr_1.0.0          prettyunits_1.1.1    assertthat_0.2.1    
#  [7] zeallot_0.1.0        rprojroot_1.3-2      digest_0.6.23       
# [10] R6_2.4.1             backports_1.1.5      MatrixModels_0.4-1  
# [13] evaluate_0.14        coda_0.19-3          httr_1.4.1          
# [16] pillar_1.4.3         rlang_0.4.5          progress_1.2.2      
# [19] lazyeval_0.2.2       data.table_1.12.8    irlba_2.3.3         
# [22] SparseM_1.78         hexbin_1.28.0        whisker_0.4         
# [25] rmarkdown_2.3        labeling_0.3         Rtsne_0.15          
# [28] stringr_1.4.0        htmlwidgets_1.5.1    munsell_0.5.0       
# [31] compiler_3.6.2       httpuv_1.5.2         xfun_0.11           
# [34] pkgconfig_2.0.3      mcmc_0.9-6           htmltools_0.4.0     
# [37] tidyselect_0.2.5     tibble_2.1.3         workflowr_1.6.2.9000
# [40] quadprog_1.5-8       viridisLite_0.3.0    crayon_1.3.4        
# [43] withr_2.1.2          later_1.0.0          MASS_7.3-51.4       
# [46] grid_3.6.2           jsonlite_1.6         gtable_0.3.0        
# [49] lifecycle_0.1.0      git2r_0.26.1         magrittr_1.5        
# [52] scales_1.1.0         RcppParallel_4.4.2   stringi_1.4.3       
# [55] farver_2.0.1         fs_1.3.1             promises_1.1.0      
# [58] vctrs_0.2.1          tools_3.6.2          glue_1.3.1          
# [61] purrr_0.3.3          hms_0.5.2            yaml_2.2.0          
# [64] colorspace_1.4-1     plotly_4.9.2         knitr_1.26          
# [67] quantreg_5.54        MCMCpack_1.4-5