Last updated: 2020-11-05

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Rmd aa4715c kevinlkx 2020-11-05 PCA plots for unbinarized data

Here we explore the structure in the Buenrostro et al (2018) scATAC-seq data inferred from the multinomial topic model with different numbers of \(k\).

Load packages and some functions used in this analysis.

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

We tried both unbinarized counts and binarized scATAC-seq data.

Unbinarized counts

Load the data

data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Buenrostro_2018/processed_data/"
load(file.path(data.dir, "Buenrostro_2018_counts.RData"))
cat(sprintf("%d x %d counts matrix.\n",nrow(counts),ncol(counts)))
rm(counts)
samples$label <- as.factor(samples$label)
# 2034 x 101172 counts matrix.

Plot PCs of the topic proportions

We first use PCA to explore the structure inferred from the multinomial topic model with different numbers of \(k\):

k = 10

Load the \(k = 10\) Poisson NMF fit.

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/unbinarized/"
fit <- readRDS(file.path(out.dir, "/fit-Buenrostro2018-scd-ex-k=10.rds"))$fit

Plot 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")

plot_grid(p.pca1.1,p.pca1.2,p.pca1.3,p.pca1.4)

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")

plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4)

Next, we layer the cell labels onto the PC plots.

p.pca3.1 <- labeled_pca_plot(fit,1:2,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.2 <- labeled_pca_plot(fit,3:4,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.3 <- labeled_pca_plot(fit,5:6,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.4 <- labeled_pca_plot(fit,7:8,samples$label,font_size = 7,
                       legend_label = "Cell labels")

plot_grid(p.pca3.1,p.pca3.2,p.pca3.3,p.pca3.4)

k = 11

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

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/unbinarized/"
fit <- readRDS(file.path(out.dir, "/fit-Buenrostro2018-scd-ex-k=11.rds"))$fit

Plot 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")

plot_grid(p.pca1.1,p.pca1.2,p.pca1.3,p.pca1.4)

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")

plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4)

Next, we layer the cell labels onto the PC plots.

p.pca3.1 <- labeled_pca_plot(fit,1:2,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.2 <- labeled_pca_plot(fit,3:4,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.3 <- labeled_pca_plot(fit,5:6,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.4 <- labeled_pca_plot(fit,7:8,samples$label,font_size = 7,
                       legend_label = "Cell labels")

plot_grid(p.pca3.1,p.pca3.2,p.pca3.3,p.pca3.4)

k = 12

Load the \(k = 12\) Poisson NMF fit.

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/unbinarized/"
fit <- readRDS(file.path(out.dir, "/fit-Buenrostro2018-scd-ex-k=12.rds"))$fit

Plot 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")

plot_grid(p.pca1.1,p.pca1.2,p.pca1.3,p.pca1.4)

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")

plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4)

Next, we layer the cell labels onto the PC plots.

p.pca3.1 <- labeled_pca_plot(fit,1:2,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.2 <- labeled_pca_plot(fit,3:4,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.3 <- labeled_pca_plot(fit,5:6,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.4 <- labeled_pca_plot(fit,7:8,samples$label,font_size = 7,
                       legend_label = "Cell labels")

plot_grid(p.pca3.1,p.pca3.2,p.pca3.3,p.pca3.4)

k = 13

Load the \(k = 13\) Poisson NMF fit.

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/unbinarized/"
fit <- readRDS(file.path(out.dir, "/fit-Buenrostro2018-scd-ex-k=13.rds"))$fit

Plot 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")

plot_grid(p.pca1.1,p.pca1.2,p.pca1.3,p.pca1.4)

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")

plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4)

Next, we layer the cell labels onto the PC plots.

p.pca3.1 <- labeled_pca_plot(fit,1:2,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.2 <- labeled_pca_plot(fit,3:4,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.3 <- labeled_pca_plot(fit,5:6,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.4 <- labeled_pca_plot(fit,7:8,samples$label,font_size = 7,
                       legend_label = "Cell labels")

plot_grid(p.pca3.1,p.pca3.2,p.pca3.3,p.pca3.4)

k = 14

Load the \(k = 14\) Poisson NMF fit.

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/unbinarized/"
fit <- readRDS(file.path(out.dir, "/fit-Buenrostro2018-scd-ex-k=14.rds"))$fit

Plot 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")

plot_grid(p.pca1.1,p.pca1.2,p.pca1.3,p.pca1.4)

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")

plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4)

Next, we layer the cell labels onto the PC plots.

p.pca3.1 <- labeled_pca_plot(fit,1:2,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.2 <- labeled_pca_plot(fit,3:4,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.3 <- labeled_pca_plot(fit,5:6,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.4 <- labeled_pca_plot(fit,7:8,samples$label,font_size = 7,
                       legend_label = "Cell labels")

plot_grid(p.pca3.1,p.pca3.2,p.pca3.3,p.pca3.4)

k = 15

Load the \(k = 15\) Poisson NMF fit.

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Buenrostro_2018/unbinarized/"
fit <- readRDS(file.path(out.dir, "/fit-Buenrostro2018-scd-ex-k=15.rds"))$fit

Plot 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")

plot_grid(p.pca1.1,p.pca1.2,p.pca1.3,p.pca1.4)

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")

plot_grid(p.pca2.1,p.pca2.2,p.pca2.3,p.pca2.4)

Next, we layer the cell labels onto the PC plots.

p.pca3.1 <- labeled_pca_plot(fit,1:2,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.2 <- labeled_pca_plot(fit,3:4,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.3 <- labeled_pca_plot(fit,5:6,samples$label,font_size = 7,
                       legend_label = "Cell labels")
p.pca3.4 <- labeled_pca_plot(fit,7:8,samples$label,font_size = 7,
                       legend_label = "Cell labels")

plot_grid(p.pca3.1,p.pca3.2,p.pca3.3,p.pca3.4)


sessionInfo()
# R version 3.6.1 (2019-07-05)
# 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] fastTopics_0.3-180 cowplot_1.0.0      ggplot2_3.3.2      Matrix_1.2-18     
# [5] workflowr_1.6.2   
# 
# loaded via a namespace (and not attached):
#  [1] ggrepel_0.8.2      Rcpp_1.0.5         lattice_0.20-38    tidyr_1.1.0       
#  [5] prettyunits_1.1.1  assertthat_0.2.1   rprojroot_1.3-2    digest_0.6.27     
#  [9] R6_2.5.0           backports_1.1.10   MatrixModels_0.4-1 evaluate_0.14     
# [13] coda_0.19-3        httr_1.4.1         pillar_1.4.6       rlang_0.4.8       
# [17] progress_1.2.2     lazyeval_0.2.2     data.table_1.12.8  irlba_2.3.3       
# [21] SparseM_1.77       hexbin_1.28.1      whisker_0.4        rmarkdown_2.1     
# [25] labeling_0.4.2     Rtsne_0.15         stringr_1.4.0      htmlwidgets_1.5.1 
# [29] munsell_0.5.0      compiler_3.6.1     httpuv_1.5.3.1     xfun_0.14         
# [33] pkgconfig_2.0.3    mcmc_0.9-7         htmltools_0.4.0    tidyselect_1.1.0  
# [37] tibble_3.0.4       quadprog_1.5-7     viridisLite_0.3.0  crayon_1.3.4      
# [41] dplyr_0.8.5        withr_2.3.0        later_1.0.0        MASS_7.3-51.6     
# [45] grid_3.6.1         jsonlite_1.6       gtable_0.3.0       lifecycle_0.2.0   
# [49] git2r_0.27.1       magrittr_1.5       scales_1.1.1       RcppParallel_4.4.3
# [53] stringi_1.4.6      farver_2.0.3       fs_1.3.1           promises_1.1.0    
# [57] ellipsis_0.3.1     vctrs_0.3.4        tools_3.6.1        glue_1.4.2        
# [61] purrr_0.3.4        hms_0.5.3          yaml_2.2.0         colorspace_1.4-1  
# [65] plotly_4.9.0       knitr_1.28         quantreg_5.41      MCMCpack_1.4-4