Last updated: 2022-06-14

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Knit directory: lps/analysis/

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Rmd 5ed6a34 Peter Carbonetto 2022-06-14 workflowr::wflow_publish("check_iln_k16.Rmd", verbose = TRUE)

Here we examine in detail the topic proportions of the iLN samples.

Load the packages used in the analysis.

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

Load the count data.

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

Load the results of the topic modeling analysis.

load("../output/fit-lps-k=16.RData")
fit <- poisson2multinom(fit)

Extract the results for the iLN samples.

i <- which(samples$tissue == "iLN")
fit <- select_loadings(fit,loadings = i)

Check in detail the topic proportions of the iLN samples. The last one (iLN_d2_20) is an outlier, and indeed this shows in the estimated topics.

topic_colors <- c("darkblue","dodgerblue","darkorange","forestgreen",
                  "limegreen","tomato","darkred","olivedrab","magenta",
          "darkmagenta","sienna","royalblue","lightskyblue",
                  "gold","red","cyan")
structure_plot(fit,loadings_order = 1:28,colors = topic_colors)


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] fastTopics_0.6-131 data.table_1.12.8 
# 
# loaded via a namespace (and not attached):
#  [1] httr_1.4.2         sass_0.4.0         tidyr_1.1.3        jsonlite_1.7.2    
#  [5] viridisLite_0.3.0  R.utils_2.11.0     bslib_0.3.1        RcppParallel_5.1.5
#  [9] assertthat_0.2.1   highr_0.8          mixsqp_0.3-46      yaml_2.2.0        
# [13] progress_1.2.2     ggrepel_0.9.1      pillar_1.6.2       backports_1.1.5   
# [17] lattice_0.20-38    quadprog_1.5-8     quantreg_5.54      glue_1.4.2        
# [21] digest_0.6.23      promises_1.1.0     colorspace_1.4-1   R.oo_1.24.0       
# [25] cowplot_1.0.0      htmltools_0.5.2    httpuv_1.5.2       Matrix_1.2-18     
# [29] pkgconfig_2.0.3    invgamma_1.1       SparseM_1.78       purrr_0.3.4       
# [33] scales_1.1.0       whisker_0.4        later_1.0.0        Rtsne_0.15        
# [37] MatrixModels_0.4-1 git2r_0.29.0       tibble_3.1.3       farver_2.0.1      
# [41] generics_0.0.2     ggplot2_3.3.5      ellipsis_0.3.2     ashr_2.2-54       
# [45] pbapply_1.5-1      lazyeval_0.2.2     magrittr_2.0.1     crayon_1.4.1      
# [49] mcmc_0.9-6         evaluate_0.14      R.methodsS3_1.8.1  fs_1.5.2          
# [53] fansi_0.4.0        MASS_7.3-51.4      truncnorm_1.0-8    prettyunits_1.1.1 
# [57] tools_3.6.2        hms_1.1.0          lifecycle_1.0.0    stringr_1.4.0     
# [61] MCMCpack_1.4-5     plotly_4.9.2       munsell_0.5.0      irlba_2.3.3       
# [65] compiler_3.6.2     jquerylib_0.1.4    rlang_0.4.11       grid_3.6.2        
# [69] htmlwidgets_1.5.1  labeling_0.3       rmarkdown_2.11     gtable_0.3.0      
# [73] DBI_1.1.0          R6_2.4.1           knitr_1.37         dplyr_1.0.7       
# [77] uwot_0.1.10        fastmap_1.1.0      utf8_1.1.4         workflowr_1.7.0   
# [81] rprojroot_1.3-2    stringi_1.4.3      parallel_3.6.2     SQUAREM_2017.10-1 
# [85] Rcpp_1.0.7         vctrs_0.3.8        tidyselect_1.1.1   xfun_0.29         
# [89] coda_0.19-3