Last updated: 2021-01-08
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Knit directory: single-cell-topics/analysis/
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| Rmd | b9b3185 | Peter Carbonetto | 2020-12-30 | A little more re-organizing. |
Here we identify clusters of cells from the mixture proportions estimated in the 68k PBMC data.
Load the packages used in the analysis below, as well as additional functions that we will use to generate some of the plots.
library(Matrix)
library(fastTopics)
library(ggplot2)
library(cowplot)Load the count data.
load("../data/pbmc_68k.RData")Load the \(K = 12\) Poisson NMF model fit.
fit <- readRDS("../output/pbmc-68k/rds/fit-pbmc-68k-scd-ex-k=12.rds")$fit
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] progress_1.2.2 tidyselect_0.2.5 xfun_0.11
# [4] purrr_0.3.3 lattice_0.20-38 vctrs_0.2.1
# [7] colorspace_1.4-1 viridisLite_0.3.0 htmltools_0.4.0
# [10] yaml_2.2.0 MCMCpack_1.4-5 plotly_4.9.2
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# [16] withr_2.1.2 glue_1.3.1 lifecycle_0.1.0
# [19] stringr_1.4.0 MatrixModels_0.4-1 munsell_0.5.0
# [22] gtable_0.3.0 workflowr_1.6.2.9000 htmlwidgets_1.5.1
# [25] coda_0.19-3 evaluate_0.14 knitr_1.26
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# [31] irlba_2.3.3 Rcpp_1.0.5 promises_1.1.0
# [34] scales_1.1.0 backports_1.1.5 jsonlite_1.6
# [37] RcppParallel_4.4.2 fs_1.3.1 mcmc_0.9-6
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# [55] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3
# [58] MASS_7.3-51.4 prettyunits_1.1.1 data.table_1.12.8
# [61] assertthat_0.2.1 rmarkdown_2.3 httr_1.4.2
# [64] R6_2.4.1 git2r_0.26.1 compiler_3.6.2