Last updated: 2020-09-16

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Knit directory: single-cell-topics/analysis/

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File Version Author Date Message
Rmd 5e0ee23 Peter Carbonetto 2020-09-16 Completed first rough draft of clustering in clusters_pulseseq.Rmd.
Rmd 337d6fc Peter Carbonetto 2020-09-16 Added clusters identified in PCs 5 and 6 of k=11 pulse-seq fit.
Rmd c0a27bd Peter Carbonetto 2020-09-16 Added clustering of pulseseq data along PCs 3 and 4.
html e7383b2 Peter Carbonetto 2020-09-16 Produced first rendering of clusters_pulseseq analysis.
Rmd 1dd20d4 Peter Carbonetto 2020-09-16 workflowr::wflow_publish(“clusters_pulseseq.Rmd”)
Rmd da9ac09 Peter Carbonetto 2020-09-16 Added hexbin plots to clusters_pulseseq analysis.
Rmd 485639a Peter Carbonetto 2020-09-16 Working on clusters_pulseseq analysis.
Rmd c8dd3af Peter Carbonetto 2020-09-16 Implemented basic_pca_plot; improved labeled_pca_plot function.

Briefly explain the aims of this analysis, and what we find.

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")

Load data and results

Load the pulse-seq data. The UMI counts are not needed for this analysis.

load("../data/pulseseq.RData")
x <- as.character(samples$tissue)
x[x == "club (hillock-associated)"] <- "hillock"
x[x == "goblet.1" | x == "goblet.2" | x == "goblet.progenitor"] <- "goblet"
x[x == "tuft.1" | x == "tuft.2" | x == "tuft.progenitor"] <- "tuft"
samples$tissue <- factor(x)
rm(counts)

Load the \(k = 11\) Poisson NMF fit.

fit <- readRDS("../output/pulseseq/rds/fit-pulseseq-scd-ex-k=11.rds")$fit

Identify clusters from principal components

Explain the process used to identify clusters.

Show the PCA projection of the cells. Note that only 10 PCs are needed for 11 topics.

p1 <- basic_pca_plot(poisson2multinom(fit),1:2)
p2 <- basic_pca_plot(poisson2multinom(fit),3:4)
p3 <- basic_pca_plot(poisson2multinom(fit),5:6)
p4 <- basic_pca_plot(poisson2multinom(fit),7:8)
p5 <- basic_pca_plot(poisson2multinom(fit),9:10)
plot_grid(p1,p2,p3,p4,p5,nrow = 2,ncol = 3)

Version Author Date
e7383b2 Peter Carbonetto 2020-09-16

These next plots show the density of the points in the PCA projection.

p6 <- pca_hexbin_plot(poisson2multinom(fit),1:2) + guides(fill = "none")
p7 <- pca_hexbin_plot(poisson2multinom(fit),3:4) + guides(fill = "none")
p8 <- pca_hexbin_plot(poisson2multinom(fit),5:6) + guides(fill = "none")
p9 <- pca_hexbin_plot(poisson2multinom(fit),7:8) + guides(fill = "none")
p10 <- pca_hexbin_plot(poisson2multinom(fit),9:10) + guides(fill = "none")
plot_grid(p6,p7,p8,p9,p10,nrow = 2,ncol = 3)

Version Author Date
e7383b2 Peter Carbonetto 2020-09-16

Identify clusters from PC plots above.

pca <- prcomp(poisson2multinom(fit)$L)$x
x   <- rep("U",nrow(pca))
pc3 <- pca[,3]
pc4 <- pca[,4]
pc5 <- pca[,5]
pc6 <- pca[,6]
x[pc4 < 5.5*pc3 + 0.5] <- "A"
x[(pc3 + 0.7)^2 + (pc4 - 0.1)^2 < 0.18^2] <- "Cil"
x[x == "A" & pc6 > 1.3*pc5 + 0.87] <- "T+N"
x[x == "A" & pc5 > -0.4 & pc6 < 1.3*pc5 + 0.49] <- "C"

PCA plot for cluster A:

rows <- which(x == "A")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p11  <- basic_pca_plot(fit2,1:2)
print(p11)

Subclustering of cluster A:

pca  <- prcomp(fit2$L)$x
y    <- rep("U",nrow(pca))
pc1  <- pca[,1]
y[pc1 > -0.1] <- "I"
x[rows] <- y

PCA plot for cluster C:

rows <- which(x == "C")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p12  <- basic_pca_plot(fit2,1:2)
p13  <- pca_hexbin_plot(fit2,1:2)
plot_grid(p12,p13)

Clustering of cluster C:

pca  <- prcomp(fit2$L)$x
y    <- rep("C",nrow(pca))
pc1  <- pca[,1]
pc2  <- pca[,2]
y[pc2 > -pc1 - 0.15] <- "B"
x[rows] <- y

PCA plot for cluster B:

rows <- which(x == "B")
fit2 <- select(poisson2multinom(fit),loadings = rows)
p14  <- basic_pca_plot(fit2,5:6)
p15  <- pca_hexbin_plot(fit2,5:6)
plot_grid(p14,p15)

Clustering of cluster B:

pca <- prcomp(fit2$L)$x
y   <- rep("B",nrow(pca))
pc5 <- pca[,5]
pc6 <- pca[,6]
y[pc5 > 0.1 & pc6 < 0] <- "P"
x[rows] <- y

Compare to Montoro et al (2018) clustering:

table(samples$tissue,x)
#                 x
#                      B     C   Cil     I     P   T+N     U
#   basal          40389  1468     0     0   199     0    37
#   ciliated           0     0  2896     0     6     0   114
#   club            1677 11834     0     6    32     1    18
#   goblet             3   396     0     0     2     0     2
#   hillock           89  4036     0     0     4     0     3
#   ionocyte           0    45     0   193    15     8    15
#   neuroendocrine     0     1     0     7     0   619     3
#   proliferating     61   194     9     4   914     0   231
#   tuft               0    18     0    12     4   691     9

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      ggplot2_3.3.0      fastTopics_0.3-175 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         hexbin_1.28.0        whisker_0.4         
# [25] Matrix_1.2-18        rmarkdown_2.3        labeling_0.3        
# [28] Rtsne_0.15           stringr_1.4.0        htmlwidgets_1.5.1   
# [31] munsell_0.5.0        compiler_3.6.2       httpuv_1.5.2        
# [34] xfun_0.11            pkgconfig_2.0.3      mcmc_0.9-6          
# [37] htmltools_0.4.0      tidyselect_0.2.5     tibble_2.1.3        
# [40] workflowr_1.6.2.9000 quadprog_1.5-8       viridisLite_0.3.0   
# [43] crayon_1.3.4         withr_2.1.2          later_1.0.0         
# [46] MASS_7.3-51.4        grid_3.6.2           jsonlite_1.6        
# [49] gtable_0.3.0         lifecycle_0.1.0      git2r_0.26.1        
# [52] magrittr_1.5         scales_1.1.0         RcppParallel_4.4.2  
# [55] stringi_1.4.3        farver_2.0.1         fs_1.3.1            
# [58] promises_1.1.0       vctrs_0.2.1          tools_3.6.2         
# [61] glue_1.3.1           purrr_0.3.3          hms_0.5.2           
# [64] yaml_2.2.0           colorspace_1.4-1     plotly_4.9.2        
# [67] knitr_1.26           quantreg_5.54        MCMCpack_1.4-5