Last updated: 2020-10-06

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Knit directory: single-cell-topics/analysis/

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html 4172024 Peter Carbonetto 2020-09-20 Identified H cluster in droplet data.
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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")$fit

Identify 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)

Version Author Date
5361fdf Peter Carbonetto 2020-09-19
311b4e8 Peter Carbonetto 2020-09-19
b1cb82e Peter Carbonetto 2020-09-19

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)

Version Author Date
db6135c Peter Carbonetto 2020-09-21
b5e1a7e Peter Carbonetto 2020-09-20
4172024 Peter Carbonetto 2020-09-20

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)

Version Author Date
ab1ed99 Peter Carbonetto 2020-09-27
bf299b9 Peter Carbonetto 2020-09-27
0a8b571 Peter Carbonetto 2020-09-21
db6135c Peter Carbonetto 2020-09-21
4172024 Peter Carbonetto 2020-09-20
5361fdf Peter Carbonetto 2020-09-19
311b4e8 Peter Carbonetto 2020-09-19

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] <- y

In 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)

Version Author Date
4fe31a6 Peter Carbonetto 2020-09-22
db6135c Peter Carbonetto 2020-09-21

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    7

Structure 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)

Version Author Date
db6135c Peter Carbonetto 2020-09-21
b5e1a7e Peter Carbonetto 2020-09-20
4172024 Peter Carbonetto 2020-09-20

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
# 
# 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_0.3-179 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_1.4.2          
# [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