Last updated: 2022-08-01
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Knit directory: scATACseq-topics/
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Add text here summarizing the analysis.
Load the packages used in the analysis below.
library(Matrix)
library(fastTopics)
library(ggplot2)
Load the sample meta-data:
tissues <-
c("BoneMarrow_62016","BoneMarrow_62216",
"HeartA_62816","Kidney_62016","Testes_62016","SmallIntestine_62816",
"LargeIntestineA_62816",
"LargeIntestineB_62816","Liver_62016",
"Lung1_62216","Lung2_62216",
"Spleen_62016",
"Thymus_62016","PreFrontalCortex_62216",
"Cerebellum_62216","WholeBrainA_62216","WholeBrainA_62816")
load("data/Cusanovich_2018/processed_data/Cusanovich_2018_metadata_only.RData")
samples <- transform(samples,
tissue.replicate = factor(tissue.replicate,tissues))
Next load the \(k = 13\) multinomial topic model fit:
fit <- readRDS("output/Cusanovich_2018/fit-Cusanovich2018-scd-ex-k=13.rds")$fit
Add text here.
set.seed(1)
topic_colors <- c("royalblue","gold","red","sienna","limegreen",
"plum","tomato","purple","dodgerblue","forestgreen",
"darkblue","darkorange","lightskyblue")
topics <- c(1,2,5,6,9,11,4,10,8,3,7,12,13)
p1 <- structure_plot(fit,grouping = samples$tissue.replicate,gap = 30,n = 4000,
perplexity = 30,colors = topic_colors,topics = topics,
verbose = FALSE)
print(p1)
Version | Author | Date |
---|---|---|
52d4663 | Peter Carbonetto | 2022-08-01 |
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] ggplot2_3.3.6 fastTopics_0.6-131 Matrix_1.2-18 workflowr_1.7.0
#
# loaded via a namespace (and not attached):
# [1] mcmc_0.9-6 fs_1.5.2 progress_1.2.2 httr_1.4.2
# [5] rprojroot_1.3-2 tools_3.6.2 backports_1.1.5 bslib_0.3.1
# [9] utf8_1.1.4 R6_2.4.1 irlba_2.3.3 uwot_0.1.10
# [13] DBI_1.1.0 lazyeval_0.2.2 colorspace_1.4-1 withr_2.5.0
# [17] tidyselect_1.1.1 prettyunits_1.1.1 processx_3.5.2 compiler_3.6.2
# [21] git2r_0.29.0 quantreg_5.54 SparseM_1.78 plotly_4.9.2
# [25] labeling_0.3 sass_0.4.0 scales_1.1.0 SQUAREM_2017.10-1
# [29] quadprog_1.5-8 callr_3.7.0 pbapply_1.5-1 mixsqp_0.3-46
# [33] systemfonts_1.0.2 stringr_1.4.0 digest_0.6.23 rmarkdown_2.11
# [37] MCMCpack_1.4-5 pkgconfig_2.0.3 htmltools_0.5.2 fastmap_1.1.0
# [41] invgamma_1.1 highr_0.8 htmlwidgets_1.5.1 rlang_0.4.11
# [45] rstudioapi_0.13 jquerylib_0.1.4 generics_0.0.2 farver_2.0.1
# [49] jsonlite_1.7.2 dplyr_1.0.7 magrittr_2.0.1 Rcpp_1.0.8
# [53] munsell_0.5.0 fansi_0.4.0 lifecycle_1.0.0 stringi_1.4.3
# [57] whisker_0.4 yaml_2.2.0 MASS_7.3-51.4 Rtsne_0.15
# [61] grid_3.6.2 parallel_3.6.2 promises_1.1.0 ggrepel_0.9.1
# [65] crayon_1.4.1 lattice_0.20-38 cowplot_1.1.1 hms_1.1.0
# [69] knitr_1.37 ps_1.6.0 pillar_1.6.2 glue_1.4.2
# [73] evaluate_0.14 getPass_0.2-2 data.table_1.12.8 RcppParallel_5.1.5
# [77] vctrs_0.3.8 httpuv_1.5.2 MatrixModels_0.4-1 gtable_0.3.0
# [81] purrr_0.3.4 tidyr_1.1.3 assertthat_0.2.1 ashr_2.2-54
# [85] xfun_0.29 coda_0.19-3 later_1.0.0 ragg_0.3.1
# [89] viridisLite_0.3.0 truncnorm_1.0-8 tibble_3.1.3 ellipsis_0.3.2