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html 06d0b30 Peter Carbonetto 2020-09-22 I’m starting to revamp the plots_tracheal_epithelium analysis.
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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.
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html c3c1b12 Peter Carbonetto 2020-08-25 Build site.
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Rmd bf23ca0 Peter Carbonetto 2020-08-20 Added manual labeling of purified PBMC data to plots_pbmc analysis.
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html fb21b3b Peter Carbonetto 2020-08-19 Added very initial Structure plots to plots_tracheal_epithelium analysis.
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html aca46cc Peter Carbonetto 2020-08-19 Added manual clustering of droplet samples based on PCs.
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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.

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, the 8 clusters identified in the clustering analysis, and the results of the differential expression analysis.

load("../data/droplet.RData")
load("../output/droplet/diff-count-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
diff_count_droplet <- diff_count_topics
diff_count_clusters_droplet <- diff_count_clusters
diff_count_merge_droplet <- diff_count_merge_club
rm(samples,counts)
rm(diff_count_topics,diff_count_clusters,diff_count_merge_club)

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")
load("../output/pulseseq/diff-count-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
diff_count_pulseseq <- diff_count_topics
diff_count_clusters_pulseseq <- diff_count_clusters
diff_count_merge_pulseseq <- diff_count_merge_bc
rm(samples,counts)
rm(diff_count_topics,diff_count_clusters,diff_count_merge_bc)

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:

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. Ciliated cells are abundant, although not nearly as much as basal and club cells. While gene expression in ciliated cells is largely captured by a single topic, this topic is not attributed only to ciliated cells, so we interpret the cluster only.

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
6a9691b Peter Carbonetto 2020-09-24
30194ad Peter Carbonetto 2020-09-24
c0fa2db Peter Carbonetto 2020-09-24
78d64e9 Peter Carbonetto 2020-09-23
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
6a9691b Peter Carbonetto 2020-09-24
c0fa2db Peter Carbonetto 2020-09-24
78d64e9 Peter Carbonetto 2020-09-23
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—and very rare—ionocyte cell type. Gene expression in these cells is not fully captured by any single topic, yet the mixture of topics forms a distinctive cluster in the embedding formed by the pulse-seq topic proportions.

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
6a9691b Peter Carbonetto 2020-09-24
30194ad Peter Carbonetto 2020-09-24
c0fa2db Peter Carbonetto 2020-09-24
78d64e9 Peter Carbonetto 2020-09-23
23f87bb Peter Carbonetto 2020-09-23
0ed5061 Peter Carbonetto 2020-09-22

We do not identify a cluster for ionocytes in the droplet data. This is not surprising in light of the fact that only a very small number of cells in the droplet data set appear to be ionocytes, at least judging by expression of the Foxi1 ionocyte marker gene:

p4 <- pca_plot_with_counts(fit_droplet,counts_droplet[,"Foxi1"],1:2)
print(p4)

Version Author Date
6a9691b Peter Carbonetto 2020-09-24
30194ad Peter Carbonetto 2020-09-24
c0fa2db Peter Carbonetto 2020-09-24
78d64e9 Peter Carbonetto 2020-09-23
23f87bb Peter Carbonetto 2020-09-23
0ed5061 Peter Carbonetto 2020-09-22

Goblet cells

Consistent with Montoro et al (2018), we identify a cluster of Goblet cells in the droplet data, but not in the pulse-seq data.

goblet_genes <- "Gp2"
p5 <- volcano_plot_with_highlighted_genes(diff_count_clusters_droplet,"G",
                                          goblet_genes,
                                          label_above_quantile = 0.998)
print(p5)

Version Author Date
6a9691b Peter Carbonetto 2020-09-24
30194ad Peter Carbonetto 2020-09-24
23f87bb Peter Carbonetto 2020-09-23
0ed5061 Peter Carbonetto 2020-09-22

Topic \(k = 1\) is unique to this cluster suggesting that the this topic characterizes the goblet cell type.

goblet_genes <- "Gp2"
p6 <- volcano_plot_with_highlighted_genes(diff_count_droplet,"k1",
                                          goblet_genes,
                                          label_above_quantile = 0.998)
print(p6)

Version Author Date
6a9691b Peter Carbonetto 2020-09-24
30194ad Peter Carbonetto 2020-09-24

Indeed, there is a very close correspondence between the topic and cluster, with several characteristic genes (e.g. Gp2) showing somewhat stronger enrichment in the topic.

p7 <- zscores_scatterplot(diff_count_clusters_droplet,
                          diff_count_droplet,"G","k1",goblet_genes,
                          label_above_score = 200,zmax = 800) +
  labs(x = "cluster G",y = "topic 1",title = "z-scores")
p8 <- beta_scatterplot(diff_count_clusters_droplet,diff_count_droplet,
                       "G","k1",goblet_genes,label_above_score = Inf) +
  labs(x = "cluster G",y = "topic 1",title = "log-fold change (beta)")
plot_grid(p7,p8)

Version Author Date
6a9691b Peter Carbonetto 2020-09-24
30194ad Peter Carbonetto 2020-09-24

Tuft and pulmonary neuroendocrine cells

In both data sets, we identify clusters for tuft and pulmonary neuroendocrine cells. The topic models do not clearly distinguish between these two rare cell types; we identify them in a single cluster.

tuft_neuroendocrine_genes <- c("Ascl1","Ascl2","Ascl3","Chga","Dclk1","Rgs13")
p8 <- volcano_plot_with_highlighted_genes(diff_count_clusters_droplet,"T+N",
                                          tuft_neuroendocrine_genes,
                                          label_above_quantile = 0.998)
print(p8)

Version Author Date
6a9691b Peter Carbonetto 2020-09-24

Here is the volcano plot from the pulse-seq data:

p9 <- volcano_plot_with_highlighted_genes(diff_count_clusters_pulseseq,"T+N",
                                          tuft_neuroendocrine_genes,
                                          label_above_quantile = 0.998)
print(p9)

Version Author Date
6a9691b Peter Carbonetto 2020-09-24

Club, basal and hillock cells

We end our exploration with the large subset of cells that does not decompose to form distinct clusters.

Abundant basal cells:

basal_genes <- c("Aqp3","Krt5","Dapl1","Hspa1a","Trp63")
p10 <- volcano_plot_with_highlighted_genes(diff_count_clusters_droplet,"B",
                                           basal_genes,
                                           label_above_quantile = 0.995)
print(p10)
p11 <- volcano_plot_with_highlighted_genes(diff_count_droplet,"k2",
                                           basal_genes,
                                           label_above_quantile = 0.995)
print(p11)
p12 <- logfoldchange_scatterplot(diff_count_clusters_droplet$beta[rows,"B"],
                                  diff_count_droplet$beta[rows,2], 
                                  diff_count_droplet$colmeans[rows])
print(p12)

Hillock cells:

hillock_genes <- c("Krt4","Krt13","Ecm1","S100a11","Cldn3","Lgals3","Anxa1")
p10 <- volcano_plot_with_highlighted_genes(diff_count_clusters_droplet,"H",
                                           hillock_genes,
                                           label_above_quantile = 0.998)
print(p10)
p11 <- volcano_plot_with_highlighted_genes(diff_count_droplet,"k4",
                                           hillock_genes,
                                           label_above_quantile = 0.998)
print(p11)
p12 <- zscores_scatterplot(diff_count_clusters_droplet$Z[,"H"],
                           diff_count_droplet$Z[,4],
                           diff_count_droplet$colmeans,
                           colnames(counts_droplet),
                           label_above_score = 100,
                           zmax = 400) +
  labs(x = "cluster H",y = "topic 4",title = "z-scores")
print(p12)
rows <- which(abs(diff_count_clusters_droplet$Z[,"H"] > 2) |
              abs(diff_count_droplet$Z[,4]) > 2)
rows <- is.element(rownames(diff_count_droplet$Z),hillock_genes)
p12b <- logfoldchange_scatterplot(diff_count_clusters_droplet$beta[rows,"H"],
                                  diff_count_droplet$beta[rows,4], 
                                  diff_count_droplet$colmeans[rows])
print(p12b)
p13 <- volcano_plot_with_highlighted_genes(diff_count_pulseseq,"k1",
                                           hillock_genes,
                                           label_above_quantile = 0.995)
print(p13)

TO DO:


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      ggrepel_0.9.0      ggplot2_3.3.0      fastTopics_0.3-177
# [5] dplyr_0.8.3        Matrix_1.2-18     
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.5           lattice_0.20-38      tidyr_1.0.0         
#  [4] prettyunits_1.1.1    assertthat_0.2.1     zeallot_0.1.0       
#  [7] rprojroot_1.3-2      digest_0.6.23        R6_2.4.1            
# [10] backports_1.1.5      MatrixModels_0.4-1   evaluate_0.14       
# [13] coda_0.19-3          httr_1.4.1           pillar_1.4.3        
# [16] rlang_0.4.5          progress_1.2.2       lazyeval_0.2.2      
# [19] data.table_1.12.8    irlba_2.3.3          SparseM_1.78        
# [22] whisker_0.4          rmarkdown_2.3        labeling_0.3        
# [25] Rtsne_0.15           stringr_1.4.0        htmlwidgets_1.5.1   
# [28] munsell_0.5.0        compiler_3.6.2       httpuv_1.5.2        
# [31] xfun_0.11            pkgconfig_2.0.3      mcmc_0.9-6          
# [34] htmltools_0.4.0      tidyselect_0.2.5     tibble_2.1.3        
# [37] workflowr_1.6.2.9000 quadprog_1.5-8       viridisLite_0.3.0   
# [40] crayon_1.3.4         withr_2.1.2          later_1.0.0         
# [43] MASS_7.3-51.4        grid_3.6.2           jsonlite_1.6        
# [46] gtable_0.3.0         lifecycle_0.1.0      git2r_0.26.1        
# [49] magrittr_1.5         scales_1.1.0         RcppParallel_4.4.2  
# [52] stringi_1.4.3        farver_2.0.1         fs_1.3.1            
# [55] promises_1.1.0       vctrs_0.2.1          tools_3.6.2         
# [58] glue_1.3.1           purrr_0.3.3          hms_0.5.2           
# [61] yaml_2.2.0           colorspace_1.4-1     plotly_4.9.2        
# [64] knitr_1.26           quantreg_5.54        MCMCpack_1.4-5