Last updated: 2022-05-10

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Knit directory: lps/analysis/

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Add text here giving an overview of this analysis.

Load the packages used in the analysis.

library(data.table)
library(fastTopics)
library(ggplot2)
library(cowplot)
source("../code/lps_data.R")

Initialize the sequence of pseudorandom numbers.

set.seed(1)

Load the count data.

dat <- read_lps_data("../data/raw_read_counts.csv.gz")
samples <- dat$samples
counts  <- dat$counts

Remove genes with very low (or no) expression.

j <- which(colSums(counts) > 20)
counts <- counts[,j]

Load the results of the topic modeling analysis.

load("../output/fit-lps-k=16.RData")

Plot the improvement in the solution over time.

p1 <- plot_progress(fit,x = "timing",y = "loglik",colors = "black",
                    add.point.every = 10,e = 1e-4) +
  guides(color = "none",fill = "none",shape = "none",
         linetype = "none",size = "none")
p2 <- plot_progress(fit,x = "timing",y = "res",colors = "black",
                    add.point.every = 10,e = 1e-4) +
  guides(color = "none",fill = "none",shape = "none",
         linetype = "none",size = "none")
plot_grid(p1,p2)

Version Author Date
b5a1eae Peter Carbonetto 2022-05-04

Visualize the structure identified in each of the tissues using a Structure plot, in which the samples in each tissue are ordered by time in which the sample was taken:

set.seed(1)
rows <- order(samples$timepoint)
topic_colors <- c("darkblue","dodgerblue","darkorange","forestgreen",
                  "limegreen","tomato","darkred","olivedrab","magenta",
                  "darkmagenta","sienna","royalblue","lightskyblue",
                  "gold","red","cyan")
p <- structure_plot(fit,grouping = samples$tissue,gap = 5,
                    colors = topic_colors,
                    topics = c(15,3,4,5,6,7,8,9,10,11,12,13,2,14,1,16),
                    loadings_order = rows) +
  theme(legend.key.height = unit(0.15,"cm"),
        legend.text = element_text(size = 7))
print(p)

Version Author Date
6dabc4d Peter Carbonetto 2022-05-05

There is a single topic (topic 9, blue in the plot below) that is capturing changes in expression over time across many tissues. Two other topics (topics 1 and 6) show similar patterns, except these patterns are specific to two tissues (PBMC and LI).

set.seed(1)
topic_colors <- c("gold","darkorange","dodgerblue","white")
fit2 <- poisson2multinom(fit)
fit2 <- merge_topics(fit2,paste0("k",setdiff(1:16,c(1,6,9))))
p <- structure_plot(fit2,grouping = samples$tissue,gap = 5,
                    colors = topic_colors,topics = c(4,1:3),
                    loadings_order = rows)
print(p)

Version Author Date
6dabc4d Peter Carbonetto 2022-05-05

These volcano plots summarize the results of the DE analysis for topics capturing different tissues (and topic 9, which is capturing changes in expression at different time points):

topics <- colnames(de_merged$z)
p <- vector("list",13)
names(p) <- topics
for (k in topics) {
  p[[k]] <- volcano_plot(de_merged,k = k,ymax = 500) +
    guides(fill = "none")
  volcano_plotly(de_merged,k = k,ymax = 500,
                 file = paste("volcano_plot",k,sep = "_"))
}
# This version of bslib is designed to work with shiny version 1.6.0 or higher.
# Warning: `arrange_()` was deprecated in dplyr 0.7.0.
# Please use `arrange()` instead.
# See vignette('programming') for more help
# This warning is displayed once every 8 hours.
# Call `lifecycle::last_warnings()` to see where this warning was generated.
# Warning: Ignoring 290 observations
# Warning: Ignoring 222 observations
# Warning: Ignoring 234 observations
# Warning: Ignoring 207 observations
# Warning: Ignoring 107 observations
# Warning: Ignoring 228 observations
# Warning: Ignoring 267 observations
# Warning: Ignoring 227 observations
# Warning: Ignoring 244 observations
# Warning: Ignoring 240 observations
# Warning: Ignoring 187 observations
# Warning: Ignoring 251 observations
# Warning: Ignoring 246 observations
do.call("plot_grid",c(p,list(ncol = 3,nrow = 5)))

These volcano plots summarize the results of the DE analysis for topics capturing expression patterns within tissues:

topics <- c("k1","k2","k5","k6","k13","k14")
p <- vector("list",6)
names(p) <- topics
for (k in topics) {
  p[[k]] <- volcano_plot(de,k = k,ymax = 300) +
    guides(fill = "none")
  volcano_plotly(de,k = k,ymax = 300,
                 file = paste("volcano_plot",k,sep = "_"))
}
# Warning: Ignoring 113 observations
# Warning: Ignoring 67 observations
# Warning: Ignoring 114 observations
# Warning: Ignoring 107 observations
# Warning: Ignoring 115 observations
# Warning: Ignoring 121 observations
do.call("plot_grid",c(p,list(ncol = 3,nrow = 2)))


sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
# 
# 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_3.3.5      fastTopics_0.6-117 data.table_1.12.8 
# 
# loaded via a namespace (and not attached):
#  [1] mcmc_0.9-6         fs_1.5.2           progress_1.2.2     httr_1.4.2        
#  [5] rprojroot_1.3-2    tools_3.6.2        backports_1.1.5    bslib_0.3.1       
#  [9] utf8_1.1.4         R6_2.4.1           irlba_2.3.3        uwot_0.1.10       
# [13] DBI_1.1.0          lazyeval_0.2.2     colorspace_1.4-1   withr_2.5.0       
# [17] tidyselect_1.1.1   prettyunits_1.1.1  compiler_3.6.2     git2r_0.29.0      
# [21] quantreg_5.54      SparseM_1.78       plotly_4.9.2       labeling_0.3      
# [25] sass_0.4.0         scales_1.1.0       SQUAREM_2017.10-1  quadprog_1.5-8    
# [29] pbapply_1.5-1      mixsqp_0.3-46      stringr_1.4.0      digest_0.6.23     
# [33] rmarkdown_2.11     R.utils_2.11.0     MCMCpack_1.4-5     pkgconfig_2.0.3   
# [37] htmltools_0.5.2    fastmap_1.1.0      invgamma_1.1       highr_0.8         
# [41] htmlwidgets_1.5.1  rlang_0.4.11       shiny_1.4.0        jquerylib_0.1.4   
# [45] generics_0.0.2     farver_2.0.1       jsonlite_1.7.2     crosstalk_1.0.0   
# [49] dplyr_1.0.7        R.oo_1.24.0        magrittr_2.0.1     Matrix_1.2-18     
# [53] Rcpp_1.0.7         munsell_0.5.0      fansi_0.4.0        lifecycle_1.0.0   
# [57] R.methodsS3_1.8.1  stringi_1.4.3      whisker_0.4        yaml_2.2.0        
# [61] MASS_7.3-51.4      Rtsne_0.15         grid_3.6.2         parallel_3.6.2    
# [65] promises_1.1.0     ggrepel_0.9.1      crayon_1.4.1       lattice_0.20-38   
# [69] hms_1.1.0          knitr_1.37         pillar_1.6.2       glue_1.4.2        
# [73] evaluate_0.14      RcppParallel_5.1.5 vctrs_0.3.8        httpuv_1.5.2      
# [77] MatrixModels_0.4-1 gtable_0.3.0       purrr_0.3.4        tidyr_1.1.3       
# [81] assertthat_0.2.1   ashr_2.2-54        xfun_0.29          mime_0.8          
# [85] xtable_1.8-4       coda_0.19-3        later_1.0.0        viridisLite_0.3.0 
# [89] truncnorm_1.0-8    tibble_3.1.3       workflowr_1.7.0    ellipsis_0.3.2