Last updated: 2020-09-10

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Knit directory: single-cell-topics/analysis/

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File Version Author Date Message
Rmd bfdd876 Peter Carbonetto 2020-09-10 workflowr::wflow_publish(“bcells_and_nkcells.Rmd”, verbose = TRUE)
html 23834e3 Peter Carbonetto 2020-09-10 Fixed results in bcells_and_nkcells analysis after addressing
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Rmd 88ed67f Peter Carbonetto 2020-09-10 Added improved beta and z-score scatterplots to bcells_and_nkcells analysis.
Rmd 58a255f Peter Carbonetto 2020-09-10 Added steps to perform diff count analysis in bcells_and_nkcells.Rmd.
Rmd 5c3a5f4 Peter Carbonetto 2020-09-09 A few small edits to the text in the bcells_and_nkcells analysis.
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html abb8ab4 Peter Carbonetto 2020-09-08 Added histograms to bcells_and_nkcells analysis.
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Rmd 681efe3 Peter Carbonetto 2020-09-08 Added steps to bcells_and_nkcells analysis to run cluster-based differential expression analysis.
Rmd 77499e2 Peter Carbonetto 2020-09-07 Implemented first steps of bcells_and_nkcells analysis.
Rmd d749c17 Peter Carbonetto 2020-09-07 Added notes to plots_pbmc; committed pbmc-clustering.RData output.

In the previous analysis of the purified and 68k PBMC data sets, we identified clusters in both data sets corresponding to B-cells and natural killer cells. Each of these clusters is characterized by high proportions from a single topic. Here we show that the clusters are enriched for top marker genes for these cell types (e.g., CD79A, NKG7), but the topics always show increased enrichment for these genes.

Load the packages used in the analysis below.

library(Matrix)
library(dplyr)
library(fastTopics)
library(ggplot2)
library(ggrepel)
library(cowplot)
source("../code/more_plots.R")

Load data and results

Load the mixture of FACS-purified PBMC data and the \(k = 6\) Poisson NMF model fit.

fit_purified <-
  readRDS("../output/pbmc-purified/rds/fit-pbmc-purified-scd-ex-k=6.rds")$fit
load("../data/pbmc_purified.RData")
counts_purified <- counts
genes_purified  <- genes
rm(samples,genes,counts)

Load the “unsorted” 68k PBMC data and the \(k = 6\) Poisson NMF model fit.

fit_68k <- readRDS("../output/pbmc-68k/rds/fit-pbmc-68k-scd-ex-k=6.rds")$fit
load("../data/pbmc_68k.RData")
counts_68k <- counts
genes_68k  <- genes
rm(samples,genes,counts)

Load the clustering of the purified and 68k data set that was determined in the “plots_pbmc” analysis.

load("../output/pbmc-clustering.RData")

Perform differential expression analysis with the topic model

Compute the differential expression statistics for the purified data.

timing <- system.time(
  diff_count_purified <- diff_count_analysis(fit_purified,counts_purified))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 155.91 seconds.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Compute the differential expression statistics for the 68k data.

timing <- system.time(
  diff_count_68k <- diff_count_analysis(fit_68k,counts_68k))
cat(sprintf("Computation took %0.2f seconds.\n",timing["elapsed"]))
# Fitting 20387 x 6 = 122322 univariate Poisson models.
# Computing log-fold change statistics.
# Computation took 79.61 seconds.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Perform differential expression analysis using the clusters

Perform a differential expression analysis using the clustering of the purified data.

fit_clusters_purified <-
  init_poisson_nmf_from_clustering(counts_purified,samples_purified$cluster)
diff_count_clusters_purified <- diff_count_analysis(fit_clusters_purified,
                                                    counts_purified)
# All topic proportions are either zero or one; using simpler single-topic calculations for model parameter estimates
# Fitting 21952 x 6 = 131712 univariate Poisson models.
# Computing log-fold change statistics.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Perform a differential expression analysis using the clustering of the 68k data.

fit_clusters_68k <- init_poisson_nmf_from_clustering(counts_68k,
                                                     samples_68k$cluster)
diff_count_clusters_68k <- diff_count_analysis(fit_clusters_68k,counts_68k)
# All topic proportions are either zero or one; using simpler single-topic calculations for model parameter estimates
# Fitting 20387 x 9 = 183483 univariate Poisson models.
# Computing log-fold change statistics.

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

B-cells

In the purified data set, compare the gene-wise z-scores from topic 3 to the z-scores from cluster B.

p1 <- logfoldchange_scatterplot(diff_count_clusters_purified$beta[,"B"],
                                diff_count_purified$beta[,3],
                                diff_count_purified$colmeans) +
  labs(x = "cluster B",y = "topic 3",title = "log-fold change (beta)")
p2 <- zscores_scatterplot(diff_count_clusters_purified$Z[,"B"],
                          diff_count_purified$Z[,3],
                          diff_count_purified$colmeans,
                          genes_purified$symbol,
                          label_above_score = 250,
                          zmax = 650) +
  labs(x = "cluster B",y = "topic 3",title = "z-scores")
plot_grid(p1,p2)

Version Author Date
23834e3 Peter Carbonetto 2020-09-10

B-cell marker CD79A, for example, emerges in the cluster and the topic, but its enrichment is much stronger in the topic. We observe the same result in the 68k data when we compare the gene-wise z-scores from topic 5 to the z-scores from cluster A2:

p3 <- logfoldchange_scatterplot(diff_count_clusters_68k$beta[,"A2"],
                                diff_count_68k$beta[,5],
                                diff_count_68k$colmeans) +
  labs(x = "cluster A2",y = "topic 5",title = "log-fold change (beta)")
p4 <- zscores_scatterplot(diff_count_clusters_68k$Z[,"A2"],
                          diff_count_68k$Z[,5],
                          diff_count_68k$colmeans,
                          genes_68k$symbol,
                          label_above_score = 175,
                          zmax = 500) +
  labs(x = "cluster A2",y = "topic 5",title = "z-scores")
plot_grid(p3,p4)

Version Author Date
23834e3 Peter Carbonetto 2020-09-10
8cdad6c Peter Carbonetto 2020-09-08
35d7ca9 Peter Carbonetto 2020-09-08

NK cells

We observe a similar result in natural killer cells. In the purified data set, compare the gene-wise z-scores from topic 4 to the z-scores from cluster A1:

p5 <- logfoldchange_scatterplot(diff_count_clusters_purified$beta[,"A1"],
                                diff_count_purified$beta[,4],
                                diff_count_purified$colmeans) +
  labs(x = "cluster A1",y = "topic 4",title = "log-fold change (beta)")
p6 <- zscores_scatterplot(diff_count_clusters_purified$Z[,"A1"],
                          diff_count_purified$Z[,4],
                          diff_count_purified$colmeans,
                          genes_purified$symbol,
                          label_above_score = 250,
                          zmax = 750) +
  labs(x = "cluster A1",y = "topic 4",title = "z-scores")
plot_grid(p5,p6)

Version Author Date
23834e3 Peter Carbonetto 2020-09-10
8cdad6c Peter Carbonetto 2020-09-08
35d7ca9 Peter Carbonetto 2020-09-08

Genes NKG7 and GNLY (granulysin), both examples of characteristic NK genes, are strongly enriched in the cluster, and moreso in the topic. The 68k data yields a similar result:

p7 <- logfoldchange_scatterplot(diff_count_clusters_68k$beta[,"A1b"],
                                diff_count_68k$beta[,3],
                                diff_count_68k$colmeans) +
  labs(x = "cluster A1b",y = "topic 3",title = "log-fold change (beta)")
p8 <- zscores_scatterplot(diff_count_clusters_68k$Z[,"A1b"],
                          diff_count_68k$Z[,3],
                          diff_count_68k$colmeans,
                          genes_68k$symbol,
                          label_above_score = 200,
                          zmax = 750) +
  labs(x = "cluster A1b",y = "topic 3",title = "z-scores")
plot_grid(p7,p8)

Version Author Date
23834e3 Peter Carbonetto 2020-09-10
8cdad6c Peter Carbonetto 2020-09-08
35d7ca9 Peter Carbonetto 2020-09-08

These results suggest the clusters and topics appear to be highlighting different biological patterns.


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_0.3-175
# [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