Last updated: 2022-02-11

Checks: 7 0

Knit directory: single-cell-topics/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(1) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 096729f. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    data/droplet.RData
    Ignored:    data/pbmc_68k.RData
    Ignored:    data/pbmc_purified.RData
    Ignored:    data/pulseseq.RData
    Ignored:    output/droplet/diff-count-droplet.RData
    Ignored:    output/droplet/fits-droplet.RData
    Ignored:    output/droplet/rds/
    Ignored:    output/pbmc-purified/fits-pbmc-purified.RData
    Ignored:    output/pbmc-purified/rds/
    Ignored:    output/pulseseq/diff-count-pulseseq.RData
    Ignored:    output/pulseseq/fits-pulseseq.RData
    Ignored:    output/pulseseq/rds/
    Ignored:    output/sims/

Untracked files:
    Untracked:  analysis/de_analysis_detailed_look_cache/
    Untracked:  analysis/de_analysis_detailed_look_more_cache/
    Untracked:  analysis/structure-plot-pulseseq-merged.png
    Untracked:  plots/

Unstaged changes:
    Modified:   analysis/assess_fits_droplet.Rmd
    Modified:   analysis/temp.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/droplet_rare.Rmd) and HTML (docs/droplet_rare.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 096729f Peter Carbonetto 2022-02-11 workflowr::wflow_publish(“droplet_rare.Rmd”)
html a64f3ea Peter Carbonetto 2022-02-11 Added loglik-vs-k plot to droplet_rare analysis.
Rmd 1bd5cef Peter Carbonetto 2022-02-11 workflowr::wflow_publish(“droplet_rare.Rmd”)
Rmd b4b6ab0 Peter Carbonetto 2022-02-11 Working on droplet_rare analysis.
html 0301b6e Peter Carbonetto 2022-02-10 First build of droplet_rare analysis.
Rmd 687fadc Peter Carbonetto 2022-02-10 workflowr::wflow_publish(“droplet_rare.Rmd”)
html 271518c Peter Carbonetto 2022-02-10 Added another link to overview page.
Rmd ad0daee Peter Carbonetto 2022-02-10 workflowr::wflow_publish(“index.Rmd”)

Based on the topic modeling results in the droplet data, with \(K = 7\) topics, one of the topics, topic 6, appears to be capturing rare, specialized epithelial cells, including ionocyte, tuft and neuroendocrine cells. Therefore, here we reanalyze the cells with membership to this topic.

Load the packages used in the analysis.

library(fastTopics)
library(ggplot2)
library(cowplot)

Initialize the sequence of pseudorandom numbers.

set.seed(1)

Load the previously prepared count data.

load("../data/droplet.RData")

Load the results on the rare cell types.

out       <- readRDS("../output/droplet/refit-droplet.rds")
fits      <- out$fits
de        <- out$de
de_merged <- out$de_merged

Below we will look more closely at the topic model with \(K = 5\) topics:

fit <- poisson2multinom(fits$k5)

Find the subset of genes and samples that were used to fit the model:

rownames(samples) <- with(samples,paste(mouse.id,barcode,sep = "_"))
samples <- samples[rownames(fit$L),]
counts  <- counts[rownames(fit$L),rownames(fit$F)]

This plot shows the improvement in the log-likelihood as the rank, \(K\), is increased. The log-likelihoods are shown relative to the log-likelihood at \(K = 2\).

plot_loglik_vs_rank(fits) +
  theme_cowplot(font_size = 12)

Version Author Date
a64f3ea Peter Carbonetto 2022-02-11

This PCA plot showing the top two PCs of the topic proportions shows that the topics correspond well with the clusters identified by Montoro et al (2018):

clusters <- as.character(samples$tissue)
clusters[clusters == "Basal" |
         clusters == "Club" |
         clusters == "Goblet"] <- "Other"
clusters <- factor(clusters)
tissue_colors <- c("firebrick",   # ciliated
                   "darkmagenta", # ionocyte
                   "darkorange",  # neuroendocrine
                   "gainsboro",   # other
                   "skyblue")     # tuft
p <- pca_plot(fit,pcs = 1:2,fill = clusters) +
  scale_fill_manual(values = tissue_colors)
# Scale for 'fill' is already present. Adding another scale for 'fill', which
# will replace the existing scale.
print(p)

ggsave("../plots/pca_cluster_droplet_rare.png",p,
       height = 3,width = 4,dpi = 450,bg = "white")

The structure plot summarizes the estimated topic proportions:

set.seed(1)
topic_colors <- c("skyblue","darkorange","red","violet","gainsboro")
topics <- c(5,2,1,3,4)
p <- structure_plot(fit,topics = topics,colors = topic_colors,
                    perplexity = 70,num_threads = 2,verbose = FALSE)
print(p)

Results to add:


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_0.6-98
# 
# loaded via a namespace (and not attached):
#  [1] httr_1.4.2         tidyr_1.1.3        jsonlite_1.7.2     viridisLite_0.3.0 
#  [5] RcppParallel_4.4.2 assertthat_0.2.1   highr_0.8          mixsqp_0.3-46     
#  [9] yaml_2.2.0         progress_1.2.2     ggrepel_0.9.1      pillar_1.6.2      
# [13] backports_1.1.5    lattice_0.20-38    quantreg_5.54      glue_1.4.2        
# [17] quadprog_1.5-8     digest_0.6.23      promises_1.1.0     colorspace_1.4-1  
# [21] htmltools_0.4.0    httpuv_1.5.2       Matrix_1.2-18      pkgconfig_2.0.3   
# [25] invgamma_1.1       SparseM_1.78       purrr_0.3.4        scales_1.1.0      
# [29] whisker_0.4        later_1.0.0        Rtsne_0.15         MatrixModels_0.4-1
# [33] git2r_0.26.1       tibble_3.1.3       farver_2.0.1       generics_0.0.2    
# [37] ellipsis_0.3.2     withr_2.4.2        ashr_2.2-51        pbapply_1.5-1     
# [41] lazyeval_0.2.2     magrittr_2.0.1     crayon_1.4.1       mcmc_0.9-6        
# [45] evaluate_0.14      fs_1.3.1           fansi_0.4.0        MASS_7.3-51.4     
# [49] truncnorm_1.0-8    tools_3.6.2        data.table_1.12.8  prettyunits_1.1.1 
# [53] hms_1.1.0          lifecycle_1.0.0    stringr_1.4.0      MCMCpack_1.4-5    
# [57] plotly_4.9.2       munsell_0.5.0      irlba_2.3.3        compiler_3.6.2    
# [61] jquerylib_0.1.4    systemfonts_1.0.2  rlang_0.4.11       grid_3.6.2        
# [65] htmlwidgets_1.5.1  labeling_0.3       rmarkdown_2.11     gtable_0.3.0      
# [69] DBI_1.1.0          R6_2.4.1           knitr_1.37         dplyr_1.0.7       
# [73] uwot_0.1.10        utf8_1.1.4         workflowr_1.7.0    rprojroot_1.3-2   
# [77] ragg_0.3.1         stringi_1.4.3      parallel_3.6.2     SQUAREM_2017.10-1 
# [81] Rcpp_1.0.7         vctrs_0.3.8        tidyselect_1.1.1   xfun_0.29         
# [85] coda_0.19-3