Clustering Cusanovich et al (2018) data using topic proportions with K = 13
Kaixuan Luo
Last updated: 2020-10-21
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Knit directory: scATACseq-topics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c00f12b | kevinlkx | 2020-10-21 | update colors and interpret clusters with tissue labels |
| html | 32ed5ae | kevinlkx | 2020-10-21 | Build site. |
| Rmd | 82292d5 | kevinlkx | 2020-10-21 | update colors and interpret clusters with tissue labels |
| html | b8a48b9 | kevinlkx | 2020-10-20 | Build site. |
| Rmd | a4daf6d | kevinlkx | 2020-10-20 | clustering with k = 13 topics |
| html | ac8ca65 | kevinlkx | 2020-10-20 | Build site. |
| Rmd | 98920ed | kevinlkx | 2020-10-20 | clustering with k = 13 topics |
| html | a38788b | kevinlkx | 2020-10-20 | Build site. |
| Rmd | f8bea96 | kevinlkx | 2020-10-20 | clustering with k = 13 topics |
Here we explore the structure in the Cusanovich et al (2018) ATAC-seq data inferred from the multinomial topic model with \(k = 13\).
Load packages and some functions used in this analysis.
library(Matrix)
library(dplyr)
library(ggplot2)
library(cowplot)
library(fastTopics)
source("code/plots.R")Load the data. The counts are no longer needed at this stage of the analysis.
data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Cusanovich_2018/processed_data/"
load(file.path(data.dir, "Cusanovich_2018.RData"))
rm(counts)Plot PCs of the topic proportions
We first use PCA to explore the structure inferred from the multinomial topic model with \(k = 13\):
Load the \(k = 13\) Poisson NMF fit.
out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(out.dir, "/fit-Cusanovich2018-scd-ex-k=13.rds"))$fitPlot PCs of the topic proportions.
p.pca1.1 <- pca_plot(poisson2multinom(fit),pcs = 1:2,fill = "none")
p.pca1.2 <- pca_plot(poisson2multinom(fit),pcs = 3:4,fill = "none")
p.pca1.3 <- pca_plot(poisson2multinom(fit),pcs = 5:6,fill = "none")
p.pca1.4 <- pca_plot(poisson2multinom(fit),pcs = 7:8,fill = "none")
p.pca1.5 <- pca_plot(poisson2multinom(fit),pcs = 9:10,fill = "none")
p.pca1.6 <- pca_plot(poisson2multinom(fit),pcs = 11:12,fill = "none")
plot_grid(p.pca1.1,p.pca1.2,p.pca1.3,p.pca1.4,p.pca1.5,p.pca1.6)
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
Some of the structure is more evident from “hexbin” plots showing the density of the points.
breaks <- c(0,1,5,10,100,Inf)
p.pca2.1 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 1:2, breaks = breaks) + guides(fill = "none")
p.pca2.2 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 3:4, breaks = breaks) + guides(fill = "none")
p.pca2.3 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 5:6, breaks = breaks) + guides(fill = "none")
p.pca2.4 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 7:8, breaks = breaks) + guides(fill = "none")
p.pca2.5 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 9:10, breaks = breaks) + guides(fill = "none")
p.pca2.6 <- pca_hexbin_plot(poisson2multinom(fit), pcs = 11:12, breaks = breaks) + guides(fill = "none")
plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4,p.pca2.5,p.pca2.6)
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
Layer the tissue and cell labels onto the PC plots
Next, we layer the tissue and cell labels onto the PC plots.
PCs 1 and 2:
p.pca3.1 <- labeled_pca_plot(fit,1:2,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.2 <- labeled_pca_plot(fit,1:2,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.1,p.pca3.2,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 3 and 4:
p.pca3.3 <- labeled_pca_plot(fit,3:4,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.4 <- labeled_pca_plot(fit,3:4,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.3,p.pca3.4,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 5 and 6:
p.pca3.5 <- labeled_pca_plot(fit,5:6,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.6 <- labeled_pca_plot(fit,5:6,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.5,p.pca3.6,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 7 and 8:
p.pca3.7 <- labeled_pca_plot(fit,7:8,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.8 <- labeled_pca_plot(fit,7:8,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.7,p.pca3.8,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 9 and 10:
p.pca3.9 <- labeled_pca_plot(fit,9:10,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.10 <- labeled_pca_plot(fit,9:10,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.9,p.pca3.10,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
PCs 11 and 12:
p.pca3.11 <- labeled_pca_plot(fit,11:12,samples$tissue,font_size = 7,
legend_label = "tissue")
p.pca3.12 <- labeled_pca_plot(fit,11:12,samples$cell_label,font_size = 7,
legend_label = "cell_label")
plot_grid(p.pca3.11,p.pca3.12,rel_widths = c(8,11))
| Version | Author | Date |
|---|---|---|
| a38788b | kevinlkx | 2020-10-20 |
Visualize by structure plot grouped by tissues
set.seed(10)
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
samples$tissue <- as.factor(samples$tissue)
p.structure.1 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = samples[rows, "tissue"],n = Inf,gap = 20,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
# Perplexity automatically changed to 35 because original setting of 50 was too large for the number of samples (111)
# Perplexity automatically changed to 41 because original setting of 50 was too large for the number of samples (128)
print(p.structure.1)
| Version | Author | Date |
|---|---|---|
| 32ed5ae | kevinlkx | 2020-10-21 |
k-means clustering on topic proportions
Define clusters using k-means, and then create structure plot based on the clusters from k-means.
Define clusters using k-means with \(k = 14\):
set.seed(10)
clusters.14 <- factor(kmeans(poisson2multinom(fit)$L,centers = 14)$cluster)
print(sort(table(clusters.14),decreasing = TRUE))
# clusters.14
# 13 7 3 2 4 1 12 10 8 14 5 6 11
# 18096 10936 9331 7693 6077 5817 4184 3994 3627 3306 2964 2821 2073
# 9
# 254Structure plot based on k-means clusters
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
p.structure.2 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = clusters.14[rows],n = Inf,gap = 20,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
# Perplexity automatically changed to 42 because original setting of 50 was too large for the number of samples (131)
# Perplexity automatically changed to 1 because original setting of 50 was too large for the number of samples (7)
# Perplexity automatically changed to 26 because original setting of 50 was too large for the number of samples (82)
# Perplexity automatically changed to 44 because original setting of 50 was too large for the number of samples (137)
print(p.structure.2)
Define clusters using k-means with \(k = 15\):
set.seed(10)
clusters.15 <- factor(kmeans(poisson2multinom(fit)$L,centers = 15)$cluster)
print(sort(table(clusters.15),decreasing = TRUE))
# clusters.15
# 13 7 3 9 8 4 1 14 12 2 15 5 6
# 14936 8426 8043 6796 6757 5951 5784 4314 4168 3657 3649 2960 2738
# 10 11
# 1697 1297Structure plot based on k-means clusters
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
p.structure.3 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = clusters.15[rows],n = Inf,gap = 20,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
# Perplexity automatically changed to 42 because original setting of 50 was too large for the number of samples (131)
# Perplexity automatically changed to 48 because original setting of 50 was too large for the number of samples (149)
# Perplexity automatically changed to 23 because original setting of 50 was too large for the number of samples (75)
# Perplexity automatically changed to 13 because original setting of 50 was too large for the number of samples (44)
print(p.structure.3)
Define clusters using k-means with \(k = 20\):
set.seed(10)
clusters.20 <- factor(kmeans(poisson2multinom(fit)$L,centers = 20)$cluster)
print(sort(table(clusters.20),decreasing = TRUE))
# clusters.20
# 17 13 4 1 3 20 8 12 7 2 15 14 18
# 13635 6703 5963 5809 5557 5057 5045 4012 3783 3492 3490 3147 2989
# 5 6 11 16 10 19 9
# 2958 2738 2158 1839 1756 721 321Structure plot based on k-means clusters
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
p.structure.4 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = clusters.20[rows],n = Inf,gap = 20,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
# Perplexity automatically changed to 43 because original setting of 50 was too large for the number of samples (133)
# Perplexity automatically changed to 1 because original setting of 50 was too large for the number of samples (9)
# Perplexity automatically changed to 26 because original setting of 50 was too large for the number of samples (82)
# Perplexity automatically changed to 25 because original setting of 50 was too large for the number of samples (81)
# Perplexity automatically changed to 43 because original setting of 50 was too large for the number of samples (134)
# Perplexity automatically changed to 27 because original setting of 50 was too large for the number of samples (86)
# Perplexity automatically changed to 48 because original setting of 50 was too large for the number of samples (150)
# Perplexity automatically changed to 10 because original setting of 50 was too large for the number of samples (35)
print(p.structure.4)
Refine clusters
We then further refine the clusters based on k-means result with \(k = 20\): merge “orange” clusters 9, 11, 14; merge “brown” clusters 3 and 10, 16, 19; merge “yellow” clusters 8 and 18. (maybe could also merge the “red” clusters 2 and 4)
clusters.merged <- clusters.20
clusters.merged[clusters.20 %in% c(9,11,14)] <- 9
clusters.merged[clusters.20 %in% c(3,10,16,19)] <- 3
clusters.merged[clusters.20 %in% c(8,18)] <- 8
clusters.merged <- factor(clusters.merged, labels = paste0("c", c(1:length(unique(clusters.merged)))))
print(sort(table(clusters.merged),decreasing = TRUE))
samples$cluster_kmeans <- clusters.merged
# clusters.merged
# c13 c3 c8 c11 c4 c1 c9 c14 c10 c7 c2 c12 c5
# 13635 9873 8034 6703 5963 5809 5626 5057 4012 3783 3492 3490 2958
# c6
# 2738colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
"gray")
rows <- sample(nrow(fit$L),4000)
p.structure.5 <- structure_plot(select(poisson2multinom(fit),loadings = rows),
grouping = clusters.merged[rows],n = Inf,gap = 20,
perplexity = 50,topics = 1:13,colors = colors_topics,
num_threads = 4,verbose = FALSE)
# Perplexity automatically changed to 43 because original setting of 50 was too large for the number of samples (134)
# Perplexity automatically changed to 47 because original setting of 50 was too large for the number of samples (147)
print(p.structure.5)
The clusters defined by k-means on topic proportions reasonably identify the clusters shown in the PCA hexbin plots (below).
p.pca.4.1 <- labeled_pca_plot(fit,1:2,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.2 <- labeled_pca_plot(fit,3:4,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.3 <- labeled_pca_plot(fit,5:6,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.4 <- labeled_pca_plot(fit,7:8,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.5 <- labeled_pca_plot(fit,9:10,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
p.pca.4.6 <- labeled_pca_plot(fit,11:12,samples$cluster_kmeans,font_size = 7,
legend_label = "cluster_kmeans")
plot_grid(p.pca.4.1,p.pca.4.2,p.pca.4.3,p.pca.4.4,p.pca.4.5,p.pca.4.6)
plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4,p.pca2.5,p.pca2.6)
We then label the cells in each cluster with the known tissue labels.
Tissues:
samples$tissue <- as.factor(samples$tissue)
cat(length(levels(samples$tissue)), "tissues. \n")
table(samples$tissue)
# 13 tissues.
#
# BoneMarrow Cerebellum Heart Kidney
# 8403 2278 7650 6431
# LargeIntestine Liver Lung PreFrontalCortex
# 7086 6167 9996 5959
# SmallIntestine Spleen Testes Thymus
# 4077 4020 2723 7617
# WholeBrain
# 8766Plot the distribution of tissues by cluster.
Stacked barplot for the counts of tissues by clusters:
library(plyr);library(dplyr)
# ------------------------------------------------------------------------------
# You have loaded plyr after dplyr - this is likely to cause problems.
# If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
# library(plyr); library(dplyr)
# ------------------------------------------------------------------------------
#
# Attaching package: 'plyr'
# The following objects are masked from 'package:dplyr':
#
# arrange, count, desc, failwith, id, mutate, rename, summarise,
# summarize
library(RColorBrewer)
freq_cluster_tissue <- count(samples, vars=c("cluster_kmeans","tissue"))
colors_tissues <- colorRampPalette(brewer.pal(9, "Set1"))(13)
# stacked barplot for the counts of tissues by clusters
p.structure.6 <- ggplot(freq_cluster_tissue, aes(fill=tissue, y=freq, x=cluster_kmeans)) +
geom_bar(position="stack", stat="identity") +
theme_classic() + xlab("Cluster") + ylab("Number of cells") +
scale_fill_manual(values = colors_tissues) +
guides(fill=guide_legend(ncol=2)) +
theme(
legend.title = element_text(size = 10),
legend.text = element_text(size = 8)
)
print(p.structure.6)
Percent stacked barplot for the counts of tissues by clusters:
freq_cluster_tissue <- count(samples, vars=c("cluster_kmeans","tissue"))
colors_tissues <- colorRampPalette(brewer.pal(9, "Set1"))(13)
p.structure.7 <- ggplot(freq_cluster_tissue, aes(fill=tissue, y=freq, x=cluster_kmeans)) +
geom_bar(position="fill", stat="identity") +
theme_classic() + xlab("Cluster") + ylab("Proportion of cells") +
scale_fill_manual(values = colors_tissues) +
guides(fill=guide_legend(ncol=2)) +
theme(
legend.title = element_text(size = 10),
legend.text = element_text(size = 8)
)
print(p.structure.7)
We can see a few clusters are tissue specific: cluster c5 is kidney specific; cluster c7 is heart specific; cluster c9 is liver specific; cluster c3 is primarily thymus; cluster c6 is primarily bone marrow.
Some clusters are combinations of related tissues: cluster c4 is half lung and half spleen; cluster c8 and c12 are mainly from pre-frontal cortex, whole brain (and cerebellum) – all neuron related. cluster c10 is also from whole brain and cerebellum. cluster c14 is mainly from Kidney, LargeIntestine, and Lung.
Some clusters are more heterogeneous mixtures of different tissues: e.g. c1, c2, c11, c13.
freq_cluster_tissue <- with(samples,table(tissue,cluster_kmeans))
print(freq_cluster_tissue)
# cluster_kmeans
# tissue c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12
# BoneMarrow 33 1342 174 259 0 2453 0 1 0 0 3085 0
# Cerebellum 80 8 0 29 0 0 0 387 0 1140 47 510
# Heart 2084 128 15 64 5 0 3776 14 0 0 442 4
# Kidney 1047 45 27 48 2953 0 0 4 3 0 347 1
# LargeIntestine 328 107 109 70 0 0 1 32 0 0 310 5
# Liver 334 96 41 36 0 0 0 0 5577 1 47 0
# Lung 1468 1143 1017 2647 0 2 5 28 28 2 552 2
# PreFrontalCortex 97 84 1 127 0 0 0 4453 0 2 39 1115
# SmallIntestine 112 71 49 9 0 0 0 3 15 0 1601 0
# Spleen 8 302 1033 2498 0 43 0 0 0 0 114 0
# Testes 34 1 0 2 0 240 0 30 3 0 43 0
# Thymus 6 56 7406 88 0 0 1 0 0 0 25 0
# WholeBrain 178 109 1 86 0 0 0 3082 0 2867 51 1853
# cluster_kmeans
# tissue c13 c14
# BoneMarrow 1056 0
# Cerebellum 73 4
# Heart 1113 5
# Kidney 158 1798
# LargeIntestine 4617 1507
# Liver 31 4
# Lung 1378 1724
# PreFrontalCortex 38 3
# SmallIntestine 2212 5
# Spleen 22 0
# Testes 2367 3
# Thymus 31 4
# WholeBrain 539 0
sessionInfo()
# R version 3.5.1 (2018-07-02)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Scientific Linux 7.4 (Nitrogen)
#
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
#
# locale:
# [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
# [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
# [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
# [9] LC_ADDRESS=C LC_TELEPHONE=C
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] RColorBrewer_1.1-2 plyr_1.8.6 fastTopics_0.3-180 cowplot_1.0.0
# [5] ggplot2_3.3.0 dplyr_0.8.5 Matrix_1.2-15 workflowr_1.6.2
#
# loaded via a namespace (and not attached):
# [1] ggrepel_0.8.2 Rcpp_1.0.4.6 lattice_0.20-38 tidyr_0.8.3
# [5] prettyunits_1.1.1 assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25
# [9] R6_2.4.1 backports_1.1.7 MatrixModels_0.4-1 evaluate_0.14
# [13] coda_0.19-2 httr_1.4.1 pillar_1.4.4 rlang_0.4.6
# [17] progress_1.2.2 lazyeval_0.2.2 data.table_1.12.8 irlba_2.3.3
# [21] SparseM_1.77 whisker_0.4 hexbin_1.28.1 rmarkdown_2.1
# [25] labeling_0.3 Rtsne_0.15 stringr_1.4.0 htmlwidgets_1.5.1
# [29] munsell_0.5.0 compiler_3.5.1 httpuv_1.5.3.1 xfun_0.14
# [33] pkgconfig_2.0.3 mcmc_0.9-7 htmltools_0.4.0 tidyselect_0.2.5
# [37] tibble_3.0.1 quadprog_1.5-5 viridisLite_0.3.0 crayon_1.3.4
# [41] withr_2.1.2 later_1.0.0 MASS_7.3-51.6 grid_3.5.1
# [45] jsonlite_1.6 gtable_0.3.0 lifecycle_0.2.0 git2r_0.27.1
# [49] magrittr_1.5 scales_1.1.1 RcppParallel_4.4.3 stringi_1.4.6
# [53] farver_2.0.3 fs_1.3.1 promises_1.1.0 ellipsis_0.3.1
# [57] vctrs_0.3.0 tools_3.5.1 glue_1.4.1 purrr_0.3.4
# [61] hms_0.4.2 yaml_2.2.0 colorspace_1.4-1 plotly_4.8.0
# [65] knitr_1.28 quantreg_5.36 MCMCpack_1.4-4