Identify clusters in droplet data using topic model
Peter Carbonetto
Last updated: 2022-01-28
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Knit directory: single-cell-topics/analysis/
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| File | Version | Author | Date | Message |
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| Rmd | f38e9f7 | Peter Carbonetto | 2022-01-28 | workflowr::wflow_publish(“clusters_droplet.Rmd”, verbose = TRUE) |
| Rmd | 015e254 | Peter Carbonetto | 2020-11-22 | Fixed up some of the text and plots in clusters_purified_pbmc analysis. |
| Rmd | 5d7ff64 | Peter Carbonetto | 2020-10-18 | Added analysis of single-cell likelihoods to clusters_pulseseq.Rmd. |
| html | 8e6c384 | Peter Carbonetto | 2020-10-18 | Added text describing likelihood scatterplots in clusters_droplet |
| Rmd | e672ef7 | Peter Carbonetto | 2020-10-18 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | 7363e2a | Peter Carbonetto | 2020-10-18 | Added log-likelihood scatterplots to clusters_droplet analysis. |
| Rmd | a51c33b | Peter Carbonetto | 2020-10-18 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| Rmd | 199ca88 | Peter Carbonetto | 2020-10-17 | Working on analysis of single-cell likelihoods in clusters_droplet.Rmd. |
| Rmd | 627cdb4 | Peter Carbonetto | 2020-10-16 | A couple minor edits to the code and text of the analyses. |
| html | 8a4b9da | Peter Carbonetto | 2020-10-15 | Adjusted code for some of the PCA plots in the clusters_droplet |
| Rmd | bb62871 | Peter Carbonetto | 2020-10-15 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | 4cb48ba | Peter Carbonetto | 2020-10-15 | Adjusted dimensions of a PCA plot in clusters_droplet analysis. |
| Rmd | b64e34f | Peter Carbonetto | 2020-10-15 | workflowr::wflow_publish(“clusters_droplet.Rmd”, verbose = TRUE) |
| html | c679d14 | Peter Carbonetto | 2020-10-15 | Revised PCA plots showing the clustering in the clusters_droplet analysis. |
| Rmd | 3bff164 | Peter Carbonetto | 2020-10-15 | workflowr::wflow_publish(“clusters_droplet.Rmd”, verbose = TRUE) |
| html | c12c1cf | Peter Carbonetto | 2020-10-11 | Replaced dotplot with boxplot in clusters_pulseseq analysis. |
| Rmd | 60b069f | Peter Carbonetto | 2020-10-11 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | 84e5598 | Peter Carbonetto | 2020-10-11 | Fixed small mistake in clusters_droplet analysis. |
| Rmd | bdcb88e | Peter Carbonetto | 2020-10-11 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | d9bf10d | Peter Carbonetto | 2020-10-11 | Added T+N cluster to totalvardist plots in clusters_droplet analysis. |
| Rmd | 87d00d0 | Peter Carbonetto | 2020-10-11 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| html | b6374a1 | Peter Carbonetto | 2020-10-11 | Added plots comparing total variation distances in clusters_droplet |
| Rmd | c69aa03 | Peter Carbonetto | 2020-10-11 | workflowr::wflow_publish(“clusters_droplet.Rmd”) |
| Rmd | 62834cb | Peter Carbonetto | 2020-10-09 | Working on various exploratory analyses of the droplet and pulse-seq data. |
| Rmd | d2377ec | Peter Carbonetto | 2020-10-06 | Simplified implementation of cellcycle_pca_plot by making use of a new pca_plot interface from the fastTopics package. |
| html | 5510fd5 | Peter Carbonetto | 2020-10-06 | clusters_droplet no longer uses plots.R. |
| Rmd | d5ea8c2 | Peter Carbonetto | 2020-10-06 | workflowr::wflow_publish(“clusters_droplet.Rmd”, verbose = TRUE) |
| html | d707238 | Peter Carbonetto | 2020-10-06 | Re-built clusters_droplet analysis using new fastTopics plots. |
| 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 the count 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")$fit
fit <- poisson2multinom(fit)From the PCs of the topic proportions, we define 4 clusters, labeled A, Cil, G and T+N. (The reasoning behind these labels will become clear later.) Points that do not fit in any of these clusters are assigned to a “background cluster”, labeled U.
pca <- prcomp(fit$L)$x
x <- rep("U",nrow(pca))
pc1 <- pca[,1]
pc2 <- pca[,2]
pc6 <- pca[,6]
x[pc2 > -0.15] <- "A"
x[pc1 > 0.3 & pc2 < -0.75] <- "Cil"
x[pc1 <= 0.3 & pc2 >= -0.75 & pc2 < -0.4] <- "T+N"
x[pc6 < -0.05] <- "G"Within the “A” cluster, we label the three more-or-less distinct subclusters as B, C and H, and assign the remaining “in between” data points to cluster “B+C+H”.
rows <- which(x == "A")
fit2 <- select(fit,loadings = rows)
pca <- prcomp(fit2$L)$x
pc1 <- pca[,1]
pc2 <- pca[,2]
y <- rep("B+C+H",nrow(pca))
y[pc1 < 0.1] <- "B"
y[pc1 > 0.4 & pc2 < 0.45] <- "C"
y[pc2 > 0.55] <- "H"
x[rows] <- yIn summary, we have subdivided the droplet data into 8 subsets:
samples$cluster <- factor(x,c("B","C","H","B+C+H","Cil","T+N","G","U"))
table(samples$cluster)
#
# B C H B+C+H Cil T+N G U
# 3841 1936 197 570 372 168 47 62There is a close correspondence, with some exceptions, between these clusters based on the topic proportions and the Montoro et al (2018) clustering:
with(samples,table(tissue,cluster))
# cluster
# tissue B C H B+C+H Cil T+N G U
# Basal 3682 16 5 142 0 0 0 0
# Ciliated 1 13 0 4 371 5 0 31
# Club 93 1878 192 411 0 0 2 2
# Goblet 2 20 0 1 0 0 42 0
# Ionocyte 9 0 0 1 0 1 1 14
# Neuroendocrine 27 4 0 6 0 51 0 8
# Tuft 27 5 0 5 1 111 2 7This correspondence can also be seen from these PCA plots:
abundant <- c("B","C","H","B+C+H")
rare <- c("Cil","T+N","G","U")
tissue_colors <- c("royalblue", # basal
"firebrick", # ciliated
"forestgreen", # club
"gold", # goblet
"darkmagenta", # ionocyte
"darkorange", # neuroendocrine
"skyblue") # tuft
rows1 <- which(is.element(samples$cluster,abundant))
rows2 <- which(is.element(samples$cluster,rare))
fit1 <- select(fit,loadings = rows1)
fit2 <- select(fit,loadings = rows2)
p1 <- pca_plot(fit1,fill = samples[rows1,"tissue"]) +
scale_fill_manual(values = tissue_colors,drop = FALSE) +
labs(fill = "cluster")
p2 <- pca_plot(fit2,fill = samples[rows2,"tissue"]) +
scale_fill_manual(values = tissue_colors,drop = FALSE) +
labs(fill = "cluster")
plot_grid(p1,p2)
The structure plot summarizes the topic proportions in each of these 8 subsets:
set.seed(1)
topic_colors <- c("gold","royalblue","salmon","turquoise","olivedrab",
"firebrick","forestgreen")
topics <- c(3,4,5,1,7,2,6)
rows <- sort(c(sample(which(samples$cluster == "B"),400),
sample(which(samples$cluster == "C"),400),
which(samples$cluster == "H"),
sample(which(samples$cluster == "B+C+H"),200),
sample(which(samples$cluster == "Cil"),200),
which(samples$cluster == "T+N"),
which(samples$cluster == "G"),
which(samples$cluster == "U")))
p <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = samples[rows,"cluster"],
topics = topics,colors = topic_colors,
perplexity = 70,
n = Inf,gap = 15,num_threads = 4,verbose = FALSE)
print(p)
Save the clustering of the droplet data to an RDS file:
saveRDS(samples,"clustering-droplet.rds")Analysis of single-cell likelihoods
Here we calculate single-cell likelihoods to assess how well the topic model captures expression in different cell types.
fit_merge <- merge_topics(poisson2multinom(fit),c("k5","k7"))
fit_montoro <- init_poisson_nmf_from_clustering(counts,samples$tissue)
fit_montoro <- poisson2multinom(fit_montoro)
loglik_topics <- loglik_multinom_topic_model(counts,fit_merge)
loglik_montoro <- loglik_multinom_topic_model(counts,fit_montoro)Next, we compare the topic-model likelihoods to the clustering-based likelihoods. In most cases, the topic model provides a fit that is better or at least as good as the clustering-based fit. The exceptions are the less abundant tuft, neuroendocrine and ionocyte cell types.
minloglik <- -20000
p1 <- loglik_scatterplot(loglik_montoro,loglik_topics,samples$tissue,
"Basal",minloglik,"cluster","topics")
p2 <- loglik_scatterplot(loglik_montoro,loglik_topics,samples$tissue,
"Ciliated",minloglik,"cluster","topics")
p3 <- loglik_scatterplot(loglik_montoro,loglik_topics,samples$tissue,
"Club",minloglik,"cluster","topics")
p4 <- loglik_scatterplot(loglik_montoro,loglik_topics,samples$tissue,
"Goblet",minloglik,"cluster","topics")
p5 <- loglik_scatterplot(loglik_montoro,loglik_topics,samples$tissue,
"Ionocyte",minloglik,"cluster","topics")
p6 <- loglik_scatterplot(loglik_montoro,loglik_topics,samples$tissue,
"Neuroendocrine",minloglik,"cluster","topics")
p7 <- loglik_scatterplot(loglik_montoro,loglik_topics,samples$tissue,
"Tuft",minloglik,"cluster","topics")
plot_grid(p1,p2,p3,p4,p5,p6,p7,nrow = 3,ncol = 3)
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
#
# 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_3.3.5 fastTopics_0.6-98 dplyr_1.0.7
# [5] Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] httr_1.4.2 tidyr_1.1.3 jsonlite_1.7.2 viridisLite_0.3.0
# [5] RcppParallel_4.4.2 assertthat_0.2.1 highr_0.8 mixsqp_0.3-46
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# [57] munsell_0.5.0 irlba_2.3.3 compiler_3.6.2 jquerylib_0.1.4
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# [65] rmarkdown_2.11 gtable_0.3.0 DBI_1.1.0 R6_2.4.1
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