Last updated: 2021-01-08

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Knit directory: single-cell-topics/analysis/

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Here we calculate single-cell likelihoods to assess how well the multinomial topic model captures expression in different cells and cell types.

Load the packages used in the analysis below.

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

Load the count data, the \(K = 12\) topic model fit, and the X clusters identified in the clustering analysis.

load("../data/pbmc_68k.RData")
fit <- readRDS(file.path("../output/pbmc-68k/rds",
                         "fit-pbmc-68k-scd-ex-k=12.rds"))$fit
fit <- poisson2multinom(fit)

Calculate the multinomial topic model likelihood for each cell.

loglik <- loglik_multinom_topic_model(counts,fit)

These likelihoods can be used to assess how well the topic model “fits” each cell:

pdat <- data.frame(loglik)
p1 <- ggplot(pdat,aes(loglik)) +
  geom_histogram(bins = 80,color = "white",fill = "black") +
  labs(y = "number of cells") +
  theme_cowplot(font_size = 10)
print(p1)


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_3.3.0     fastTopics_0.4-13 Matrix_1.2-18    
# 
# 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_1.4.2          
# [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         whisker_0.4          rmarkdown_2.3       
# [25] labeling_0.3         Rtsne_0.15           stringr_1.4.0       
# [28] htmlwidgets_1.5.1    munsell_0.5.0        compiler_3.6.2      
# [31] httpuv_1.5.2         xfun_0.11            pkgconfig_2.0.3     
# [34] mcmc_0.9-6           htmltools_0.4.0      tidyselect_0.2.5    
# [37] tibble_2.1.3         workflowr_1.6.2.9000 quadprog_1.5-8      
# [40] viridisLite_0.3.0    crayon_1.3.4         dplyr_0.8.3         
# [43] withr_2.1.2          later_1.0.0          MASS_7.3-51.4       
# [46] grid_3.6.2           jsonlite_1.6         gtable_0.3.0        
# [49] lifecycle_0.1.0      git2r_0.26.1         magrittr_1.5        
# [52] scales_1.1.0         RcppParallel_4.4.2   stringi_1.4.3       
# [55] farver_2.0.1         fs_1.3.1             promises_1.1.0      
# [58] vctrs_0.2.1          tools_3.6.2          glue_1.3.1          
# [61] purrr_0.3.3          hms_0.5.2            yaml_2.2.0          
# [64] colorspace_1.4-1     plotly_4.9.2         knitr_1.26          
# [67] quantreg_5.54        MCMCpack_1.4-5