Last updated: 2020-08-25
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Knit directory: single-cell-topics/analysis/
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | bf23ca0 | Peter Carbonetto | 2020-08-20 | Added manual labeling of purified PBMC data to plots_pbmc analysis. |
Rmd | 077d3d5 | Peter Carbonetto | 2020-08-20 | Added k=9 and k=11 pulseseq fits to plots_tracheal_epithelium analysis. |
html | 0ce9604 | Peter Carbonetto | 2020-08-20 | Re-built plots_tracheal_epithelium with fastTopics 0.3-162. |
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html | b17bfa4 | Peter Carbonetto | 2020-08-19 | Added pulseseq PCA plots to plots_tracheal_epithelium analysis. |
Rmd | 76dc0c6 | Peter Carbonetto | 2020-08-19 | wflow_publish(“plots_tracheal_epithelium.Rmd”) |
Rmd | c70612f | Peter Carbonetto | 2020-08-19 | Revised structure plot settings for abundant droplet samples in plots_tracheal_epithelium. |
html | adda33f | Peter Carbonetto | 2020-08-19 | Fixed another structure plot in plots_tracheal_epithelium analysis. |
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html | f4bdf19 | Peter Carbonetto | 2020-08-19 | Added explanatory text and improved PC-based manual clustering of |
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html | 368a74a | Peter Carbonetto | 2020-08-19 | Added some text to plots_tracheal_epithelium analysis. |
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html | c517ea2 | Peter Carbonetto | 2020-08-18 | Small fix to one of the PCA plots in plots_tracheal_epithelium. |
Rmd | 8f5c210 | Peter Carbonetto | 2020-08-18 | wflow_publish(“plots_tracheal_epithelium.Rmd”) |
html | 01afbd2 | Peter Carbonetto | 2020-08-18 | Added some PC plots to the plots_tracheal_epithelium analysis. |
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html | 0a04fc1 | Peter Carbonetto | 2020-08-18 | Added abundance plots to plots_tracheal_epithelium analysis. |
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Rmd | 61917ad | Peter Carbonetto | 2020-08-18 | Working on new analysis, plots_tracheal_epithelium.Rmd. |
TO DO: Add introductory text here.
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")
We begin with the droplet data. Note that the count data are no longer needed at this stage.
load("../data/droplet.RData")
samples_droplet <- samples
rm(samples,counts)
Load the \(k = 7\) Poisson NMF model fit.
fit_droplet <- readRDS("../output/droplet/rds/fit-droplet-scd-ex-k=7.rds")$fit
The Montoro et al (2018) article mentions that some epithelial cell types are abundant, whereas others are very rare. The topics inferred from the droplet data reflect this:
p1 <- create_abundance_plot(fit_droplet)
print(p1)
Version | Author | Date |
---|---|---|
0a04fc1 | Peter Carbonetto | 2020-08-18 |
The first topic—which is not actually visible in the bar chart above—is indeed very rare; only 43 out of 7,193 samples have a greater than 10% contribution from this topic.
sum(poisson2multinom(fit_droplet)$L[,1] > 0.1)
# [1] 43
In this next part of the analysis, we perform PCA on the estimated topic proportions to explore structure in the data as inferred by the topic model. Typically, a nonlinear embedding method such as t-SNE or UMAP is used to visualize the structure. The disadvantage of such methods is that it can often be difficult to get the (many) tuning parameters right, and they can be slow when applied to large data sets; by contrast, PCA has no tuning parameters.
fit <- poisson2multinom(fit_droplet)
pca <- prcomp(fit$L)$x
In the projection onto PCs 1,2 and 5,6, we can delineate 4 clusters, with a fifth subset (E) used as a “background” cluster. (PCs 3 and 4 do not reveal any additional substrcture, so are not shown.)
n <- nrow(pca)
x <- rep("E",n)
pc1 <- pca[,"PC1"]
pc2 <- pca[,"PC2"]
pc6 <- pca[,"PC6"]
x[pc2 > -0.1] <- "A"
x[pc6 < -0.04] <- "B"
x[(pc1 - 0)^2 + (pc2 + 0.75)^2 < 0.09] <- "C"
x[(pc1 - 0.5)^2 + (pc2 + 0.9)^2 < 0.04] <- "D"
samples_droplet$cluster <- x
p1 <- pca_plot_with_labels(fit_droplet,c("PC1","PC2"),x) +
labs(fill = "cluster")
p2 <- pca_plot_with_labels(fit_droplet,c("PC5","PC6"),x) +
labs(fill = "cluster")
plot_grid(p1,p2)
The vast majority of the cells are in cluster A:
table(x)
# x
# A B C D E
# 6533 50 162 375 73
Let’s look more closely at the topics that show variation in the first two PCs:
p6 <- pca_plot(poisson2multinom(fit_droplet),pcs = 1:2,k = c(2,5,6),
plot_grid_call = function (plots)
do.call(plot_grid,c(lapply(plots,
function (x) x + guides(fill = "none")),list(nrow = 1))))
print(p6)
The bulk of the samples lie on a continuous gradient between topics 2 and 5. There is a smaller cluster at the bottom of this plot, with high contributions from topic 6.
Next, we look closely at PCs 4 and 5:
p7 <- pca_plot(poisson2multinom(fit_droplet),pcs = 4:5,k = c(3,4,5,7),
plot_grid_call = function (plots)
do.call(plot_grid,c(lapply(plots,
function (x) x + guides(fill = "none")),list(nrow = 1))))
print(p7)
Along these PCs, we see that topics 3, 4, 5 and 7 exist in many combinations, with no apparent discrete populations.
Topic 1 captures a very small discrete population of cells:
p8 <- pca_plot(poisson2multinom(fit_droplet),pcs = 5:6,k = 1)
print(p8)
In summary, topics 1 and 6 pick up discrete “cell types”, whereas the other topics characterize more continuous variation in gene expression, perhaps cell types along a continuous trajectory of development. There are some other discrete clusters that seem to be composed of distinct combinations of topics that we will neeed to examine more closely.
These next few scatterplots show the same PCs as before, with the samples labeled according to their assignment to these clusters:
droplet_cluster_colors <- c("gray","darkblue","gold","yellowgreen","firebrick")
p9 <- pca_plot_with_labels(fit_droplet,c("PC1","PC2"),samples_droplet$cluster,
droplet_cluster_colors) + labs(fill = "cluster")
p10 <- pca_plot_with_labels(fit_droplet,c("PC4","PC5"),samples_droplet$cluster,
droplet_cluster_colors) + labs(fill = "cluster")
p11 <- pca_plot_with_labels(fit_droplet,c("PC5","PC6"),samples_droplet$cluster,
droplet_cluster_colors) + labs(fill = "cluster")
plot_grid(p9,p10,p11)
In the following, we will treat the more abundant clusters, \(c_1\) and \(c_4\), separately from the rest of the samples.
TO DO: Add text here.
p12 <- pca_plot(poisson2multinom(fit_pulseseq),pcs = 3:4,k = 7)
p13 <- pca_plot(poisson2multinom(fit_pulseseq),pcs = 5:6,k = 2)
plot_grid(p12,p13)
TO DO: Add text here.
droplet_topic_colors <- c("gold","royalblue","turquoise","greenyellow",
"forestgreen","firebrick","olivedrab")
names(droplet_topic_colors) <- paste0("k",1:7)
topics <- c("k1","k3","k4","k5","k7","k2","k6")
set.seed(1)
fit_droplet_rare <- select(poisson2multinom(fit_droplet),
loadings = which(samples_droplet$cluster != "c1" &
samples_droplet$cluster != "c4"))
p12 <- structure_plot(fit_droplet_rare,verbose = FALSE,perplexity = 50,
topics = topics,
colors = droplet_topic_colors[topics])
print(p12)
TO DO: Add text here.
topics <- c("k1","k3","k4","k7","k5","k2","k6")
set.seed(1)
fit_droplet_abundant <-
select(poisson2multinom(fit_droplet),
loadings = which(samples_droplet$cluster == "c1" |
samples_droplet$cluster == "c4"))
p13 <- structure_plot(fit_droplet_abundant,n = 2000,perplexity = 50,
verbose = FALSE,topics = topics,
colors = droplet_topic_colors[topics],
scaling = c(1,1,1,1,2,1,1))
print(p13)
load("../data/pulseseq.RData")
samples_pulseseq <- samples
rm(samples,counts)
Load the \(k = 9\) and \(k = 11\) Poisson NMF fits for the pulse-seq data.
fit_pulseseq9 <- readRDS("../output/pulseseq/rds/fit-pulseseq-scd-ex-k=9.rds")$fit
fit_pulseseq11 <- readRDS("../output/pulseseq/rds/fit-pulseseq-scd-ex-k=11.rds")$fit
Likewise, we also pick up rare and abundant topics in the pulse-seq data:
p2 <- create_abundance_plot(fit_pulseseq)
print(p2)
temp <- select(poisson2multinom(fit_droplet),
loadings = which(samples_droplet$tissue == "Ionocyte"))
structure_plot(temp,rows = order(temp$L[,3]),
topics = topic_ordering,
colors = droplet_topic_colors[topic_ordering])
temp2 <- select(poisson2multinom(fit_droplet),
loadings = which(samples_droplet$tissue == "Tuft"))
structure_plot(temp2,perplexity = 30,
topics = topic_ordering,
colors = droplet_topic_colors[topic_ordering])
temp3 <- select(poisson2multinom(fit_droplet),
loadings = which(samples_droplet$tissue == "Neuroendocrine"))
structure_plot(temp3,perplexity = 30,
topics = topic_ordering,
colors = droplet_topic_colors[topic_ordering])
It is helpful to compare these results with clustering reported in the Montoro et al (2018) paper. To make this comparison, we layer the 7 clusters on top of these PCs:
droplet_celltype_colors <-
c("royalblue", # Basal
"firebrick", # Ciliated
"forestgreen", # Club
"gold", # Goblet
"darkmagenta", # Ionocyte
"darkorange", # Neuroendocrine
"lightsteelblue") # Tuft
p9 <- pca_plot_with_labels(fit_droplet,c("PC1","PC2"),samples_droplet$tissue,
droplet_celltype_colors) + labs(fill = "celltype")
p10 <- pca_plot_with_labels(fit_droplet,c("PC4","PC5"),samples_droplet$tissue,
droplet_celltype_colors) + labs(fill = "celltype")
p11 <- pca_plot_with_labels(fit_droplet,c("PC5","PC6"),samples_droplet$tissue,
droplet_celltype_colors) + labs(fill = "celltype")
plot_grid(p9,p10,p11)
TO DO: Add Structure plot9s) to compare Montoro et al (2018) clustering.
And likewise in the pulse-seq data:
p5 <- basic_pca_plot(fit_pulseseq,c("PC3","PC4"))
p6 <- basic_pca_plot(fit_pulseseq,c("PC5","PC6"))
plot_grid(p5,p6)
ggplot(cbind(pca$x,data.frame(k2 = fit$L[,2])),
aes(x = PC1,y = PC2,fill = k2)) +
geom_point(shape = 21,color = "white",size = 2) +
scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
midpoint = 0.5) +
theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k5 = fit$L[,5])),
aes(x = PC1,y = PC2,fill = k5)) +
geom_point(shape = 21,color = "white",size = 2) +
scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
midpoint = 0.5) +
theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k6 = fit$L[,6])),
aes(x = PC1,y = PC2,fill = k6)) +
geom_point(shape = 21,color = "white",size = 2) +
scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
midpoint = 0.5) +
theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k1 = fit$L[,1])),
aes(x = PC5,y = PC6,fill = k1)) +
geom_point(shape = 21,color = "white",size = 2) +
scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
midpoint = 0.5) +
theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k3 = fit$L[,3])),
aes(x = PC1,y = PC2,fill = k3)) +
geom_point(shape = 21,color = "white",size = 2) +
scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
midpoint = 0.5) +
theme_cowplot(font_size = 12)
ggplot(cbind(pca$x,data.frame(k4 = fit$L[,4])),
aes(x = PC1,y = PC2,fill = k4)) +
geom_point(shape = 21,color = "white",size = 2) +
scale_fill_gradient2(low = "deepskyblue",mid = "gold",high = "orangered",
midpoint = 0.5) +
theme_cowplot(font_size = 12)
ggplot(cbind(samples_droplet,pca$x),aes(x = PC1,y = PC2,fill = tissue)) +
geom_point(shape = 21,color = "white",size = 2) +
scale_fill_manual(values = clrs) +
theme_cowplot(font_size = 12)
ggplot(cbind(samples_droplet,pca$x),aes(x = PC5,y = PC6,fill = tissue)) +
geom_point(shape = 21,color = "white",,size = 2) +
scale_fill_manual(values = clrs) +
theme_cowplot(font_size = 12)
clrs <- c("royalblue", # basal
"firebrick", # ciliated
"forestgreen", # club
"gold", # goblet
"darkmagenta", # ionocyte
"darkorange", # neuroendocrine
"tomato", # proliferating
"darkgray") # tuft
ggplot(cbind(samples_droplet,pca$x),aes(x = PC1,y = PC2,fill = tissue)) +
geom_point(shape = 21,color = "white",size = 2) +
scale_fill_manual(values = clrs) +
theme_cowplot(font_size = 12)
ggplot(cbind(samples_droplet,pca$x),aes(x = PC5,y = PC6,fill = tissue)) +
geom_point(shape = 21,color = "white",,size = 2) +
scale_fill_manual(values = clrs) +
theme_cowplot(font_size = 12)
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.5
#
# 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_0.3-165 dplyr_0.8.3
#
# 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 whisker_0.4 Matrix_1.2-18
# [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_5.0.2 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