Visualize and interpret droplet and pulse-seq topics

Last updated: 2020-09-23

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Rmd	f0bdbda	Peter Carbonetto	2020-09-23	workflowr::wflow_publish(“plots_tracheal_epithelium.Rmd”, verbose = TRUE)
html	361eded	Peter Carbonetto	2020-09-23	Added text to accompany ciliated cells volcano plots.
Rmd	8198c07	Peter Carbonetto	2020-09-23	workflowr::wflow_publish(“plots_tracheal_epithelium.Rmd”, verbose = TRUE)
html	23f87bb	Peter Carbonetto	2020-09-23	Improved volcano plots for ciliated cell type in
Rmd	516a32e	Peter Carbonetto	2020-09-23	workflowr::wflow_publish(“plots_tracheal_epithelium.Rmd”, verbose = TRUE)
html	0ed5061	Peter Carbonetto	2020-09-22	Added some rough, first-draft volcano plots to
Rmd	6ccf998	Peter Carbonetto	2020-09-22	workflowr::wflow_publish(“plots_tracheal_epithelium.Rmd”, verbose = TRUE)
Rmd	de36d31	Peter Carbonetto	2020-09-22	Added volcano plots for ciliated cells to plots_tracheal_epithelium analysis.
Rmd	082352a	Peter Carbonetto	2020-09-22	Added steps to plots_tracheal_epithelium analysis to compute differential expression statistics.
html	06d0b30	Peter Carbonetto	2020-09-22	I’m starting to revamp the plots_tracheal_epithelium analysis.
Rmd	0877a1f	Peter Carbonetto	2020-09-22	workflowr::wflow_publish(“plots_tracheal_epithelium.Rmd”)
Rmd	5af46f1	Peter Carbonetto	2020-09-20	Working on Structure plot for droplet data.
Rmd	c072577	Peter Carbonetto	2020-09-19	Re-created structure plot for pulseseq data.
Rmd	942486b	Peter Carbonetto	2020-09-18	Fixing merge issue.
Rmd	8cf758e	Peter Carbonetto	2020-09-12	Working on improvements to clustering of pulse-seq data.
Rmd	a128de5	Peter Carbonetto	2020-09-12	Revamping the analysis of the pulseseq data in plots_tracheal_epithelium.
Rmd	3ab7da1	Peter Carbonetto	2020-08-25	A few minor edits.
html	f2e0b23	Peter Carbonetto	2020-08-25	Fixed dimensions of PCA plots in plots_tracheal_epithelium analysis.
Rmd	7b59815	Peter Carbonetto	2020-08-25	workflowr::wflow_publish(“plots_tracheal_epithelium.Rmd”)
html	5589611	Peter Carbonetto	2020-08-25	Added PCA plots and structure plots from pulseseq data.
Rmd	b731a4a	Peter Carbonetto	2020-08-25	workflowr::wflow_publish(“plots_tracheal_epithelium.Rmd”)
html	c3c1b12	Peter Carbonetto	2020-08-25	Build site.
Rmd	2defe6d	Peter Carbonetto	2020-08-25	Added crosstab plot to plots_tracheal_epithelium analysis.
html	97c13c2	Peter Carbonetto	2020-08-25	Build site.
Rmd	e11855b	Peter Carbonetto	2020-08-25	Working on revised analysis of droplet and pulse-seq data sets.
html	e11855b	Peter Carbonetto	2020-08-25	Working on revised analysis of droplet and pulse-seq data sets.
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.
Rmd	961570e	Peter Carbonetto	2020-08-20	wflow_publish(“plots_tracheal_epithelium.Rmd”)
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.
Rmd	29a9258	Peter Carbonetto	2020-08-19	wflow_publish(“plots_tracheal_epithelium.Rmd”)
html	0a16b60	Peter Carbonetto	2020-08-19	Fixed structure plot in plots_tracheal_epithelium analysis.
Rmd	3a7bd74	Peter Carbonetto	2020-08-19	wflow_publish(“plots_tracheal_epithelium.Rmd”)
html	f4bdf19	Peter Carbonetto	2020-08-19	Added explanatory text and improved PC-based manual clustering of
Rmd	c7b77ee	Peter Carbonetto	2020-08-19	wflow_publish(“plots_tracheal_epithelium.Rmd”)
Rmd	70a4a60	Peter Carbonetto	2020-08-19	Added note to plots_tracheal_epithelium.Rmd.
html	fb21b3b	Peter Carbonetto	2020-08-19	Added very initial Structure plots to plots_tracheal_epithelium analysis.
Rmd	d35cb03	Peter Carbonetto	2020-08-19	wflow_publish(“plots_tracheal_epithelium.Rmd”)
html	368a74a	Peter Carbonetto	2020-08-19	Added some text to plots_tracheal_epithelium analysis.
Rmd	223406b	Peter Carbonetto	2020-08-19	wflow_publish(“plots_tracheal_epithelium.Rmd”)
html	aca46cc	Peter Carbonetto	2020-08-19	Added manual clustering of droplet samples based on PCs.
Rmd	38f811b	Peter Carbonetto	2020-08-19	wflow_publish(“plots_tracheal_epithelium.Rmd”)
Rmd	343747e	Peter Carbonetto	2020-08-19	Small edit to figure dimensions.
html	5a35bbd	Peter Carbonetto	2020-08-19	Added labeled PCA plot; adjusted plot dimensions in
Rmd	fb91075	Peter Carbonetto	2020-08-19	wflow_publish(“plots_tracheal_epithelium.Rmd”)
html	8b9b528	Peter Carbonetto	2020-08-19	Added more PCA plots to plots_tracheal_epithelium analysis.
Rmd	ee7cbf1	Peter Carbonetto	2020-08-19	wflow_publish(“plots_tracheal_epithelium.Rmd”)
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.
Rmd	f1c7d02	Peter Carbonetto	2020-08-18	wflow_publish(“plots_tracheal_epithelium.Rmd”)
html	0a04fc1	Peter Carbonetto	2020-08-18	Added abundance plots to plots_tracheal_epithelium analysis.
Rmd	f914f7e	Peter Carbonetto	2020-08-18	wflow_publish(“plots_tracheal_epithelium.Rmd”)
Rmd	61917ad	Peter Carbonetto	2020-08-18	Working on new analysis, plots_tracheal_epithelium.Rmd.

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

Load data and results

Load the smaller “droplet” data set, the $k = 7$ Poisson NMF model fit for these data, and the 8 clusters identified in the clustering analysis.

load("../data/droplet.RData")
counts_droplet <- counts
samples_droplet <- readRDS("../output/droplet/clustering-droplet.rds")
fit_droplet <- readRDS("../output/droplet/rds/fit-droplet-scd-ex-k=7.rds")$fit
rm(samples,counts)

For reference, we show here the Structure plot from the clustering analysis of the droplet data. This Structure plot summarizes the topic proportions in each of the 8 subsets.

Next, load the larger “pulse-seq” data set, the $k = 11$ Poisson NMF model fit for these data, and the 7 clusters identified in the clustering analysis.

load("../data/pulseseq.RData")
counts_pulseseq  <- counts
samples_pulseseq <- readRDS("../output/pulseseq/clustering-pulseseq.rds")
fit_pulseseq <- readRDS("../output/pulseseq/rds/fit-pulseseq-scd-ex-k=11.rds")$fit
rm(samples,counts)

For reference, we show here the Structure plot from the clustering analysis of the pulse-seq data. This Structure plot summarizes the topic proportions in each of the 7 subsets:

Differential expression analysis

Perform differential expression analysis using the topic model fitted to the droplet data.

timing <- system.time(
  diff_count_droplet <- diff_count_analysis(fit_droplet,counts_droplet))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 18388 x 7 = 128716 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 21.57 seconds.

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Perform a differential expression analysis using the topic model fitted to the pulse-seq data.

timing <- system.time(
  diff_count_pulseseq <- diff_count_analysis(fit_pulseseq,counts_pulseseq))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21621 x 11 = 237831 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 1068.47 seconds.

Now, calculate differential expression statistics using the clusters identified in the droplet data.

fit_clusters_droplet <-
  init_poisson_nmf_from_clustering(counts_droplet,samples_droplet$cluster)
timing <- system.time(
  diff_count_clusters_droplet <- diff_count_analysis(fit_clusters_droplet,
                                                     counts_droplet))
# All topic proportions are either zero or one; using simpler single-topic calculations for model parameter estimates
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 18388 x 8 = 147104 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 30.32 seconds.

And do the same in the pulse-seq data.

fit_clusters_pulseseq <-
  init_poisson_nmf_from_clustering(counts_pulseseq,samples_pulseseq$cluster)
timing <- system.time(
  diff_count_clusters_pulseseq <- diff_count_analysis(fit_clusters_pulseseq,
                                                      counts_pulseseq))
# All topic proportions are either zero or one; using simpler single-topic calculations for model parameter estimates
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21621 x 7 = 151347 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 540.97 seconds.

Ciliated cells

We begin with the cluster that captures ciliated cells in the droplet data. This cluster is one of the most distinctive in both the droplet and pulse-seq data sets.

cilitated_genes <- c("Ccdc113","Ccdc153","Cdhr3","Foxj1","Lztfl1","Mlf1")
p1 <- volcano_plot_with_highlighted_genes(diff_count_clusters_droplet,"Cil",
                                          cilitated_genes,
                                          label_above_quantile = 0.998)
print(p1)

Version	Author	Date
361eded	Peter Carbonetto	2020-09-23
23f87bb	Peter Carbonetto	2020-09-23
0ed5061	Peter Carbonetto	2020-09-22

In this volcano plot, marker genes and transcription factors identified in Montoro et al (2018) are highlighted in black, and other top differentially expressed genes are shown with gray labels.

We obtain similar top differentially expressed genes in the pulse-seq data:

p2 <- volcano_plot_with_highlighted_genes(diff_count_clusters_pulseseq,"Cil",
                                          cilitated_genes,
                                          label_above_quantile = 0.998)
print(p2)

Version	Author	Date
361eded	Peter Carbonetto	2020-09-23
23f87bb	Peter Carbonetto	2020-09-23
0ed5061	Peter Carbonetto	2020-09-22

Ionocytes

In the pulse-seq data, we identify a distinctive cluster for the newly discovered ionocyte cell type. Gene expression in these cells is not fully captured by any single topic.

ionocyte_genes <- c("Ascl3","Asgr1","Atp6v0d2","Atp6v1c2","Cftr","Foxi1",
                    "Moxd1","P2ry14","Stap1")
p3 <- volcano_plot_with_highlighted_genes(diff_count_clusters_pulseseq,"I",
                                          ionocyte_genes,
                                          label_above_quantile = 0.998)
print(p3)

Version	Author	Date
23f87bb	Peter Carbonetto	2020-09-23
0ed5061	Peter Carbonetto	2020-09-22

We do not identify a cluster for ionocytes in the droplet data, probably because only a handful of cells in this data set appear to be ionocytes, at least judging by expression of the Foxi1 ionocyte marker gene:

pca  <- prcomp(poisson2multinom(fit_droplet)$L)$x
pdat <- data.frame(PC1  = pca[,1],
                   PC2  = pca[,2],
                   Foxi1 = counts_droplet[,"Foxi1"])
p4 <- ggplot(pdat,aes(x = PC1,y = PC2,fill = Foxi1)) +
  geom_point(shape = 21,color = "white",size = 1.25) +
  scale_fill_gradientn(colors = c("skyblue","gold","darkorange","magenta"),
                        na.value = "lightskyblue") +
  theme_cowplot(font_size = 10) +
  labs(fill = "expression level")
print(p4)

Version	Author	Date
23f87bb	Peter Carbonetto	2020-09-23
0ed5061	Peter Carbonetto	2020-09-22

Goblet cells

Volcano plot for “G” cluster in droplet data:

genes_goblet <- "Gp2"
genes <- colnames(counts_droplet)
p5 <- volcano_plot(diff_count_clusters_droplet,k = "G",labels = genes) +
      labs(title = "")
print(p5)