Topic modeling analysis of Buenrostro et al (2018) data with k = 8 topics
Peter Carbonetto
Last updated: 2022-05-03
Checks: 7 0
Knit directory: scATACseq-topics/
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Load the packages used in the analysis.
library(Matrix)
library(fastTopics)
library(ggplot2)
library(cowplot)Load the count data and the \(k = 8\) multinomial topic model fit to these data:
load("data/Buenrostro_2018/processed_data/Buenrostro_2018_binarized.RData")
fit <- readRDS(
file.path("output/Buenrostro_2018/binarized/filtered_peaks",
"fit-Buenrostro2018-binarized-filtered-scd-ex-k=8.rds"))$fit
fit <- poisson2multinom(fit)Visualize the structure identified in the FACS cell populations using a Structure plot:
set.seed(1)
celltypes = factor(samples$label)
topic_colors <- c("darkorange","limegreen","magenta","gold","skyblue",
"darkblue","dodgerblue","darkmagenta")
custom_embed_method <- function (fit, ...) {
y0 <- pca_from_topics(fit,dims = 1)
return(tsne_from_topics(fit,dims = 1,Y_init = matrix(y0)))
}
structure_plot(fit,grouping = celltypes,perplexity = 20,n = Inf,gap = 20,
verbose = FALSE,colors = topic_colors,
embed_method = custom_embed_method)
# Perplexity automatically changed to 24 because the original setting of 100 was too large for the number of samples (78)
# Perplexity automatically changed to 52 because the original setting of 100 was too large for the number of samples (160)
# Perplexity automatically changed to 44 because the original setting of 100 was too large for the number of samples (138)
# Perplexity automatically changed to 20 because the original setting of 100 was too large for the number of samples (64)
# Perplexity automatically changed to 46 because the original setting of 100 was too large for the number of samples (142)
# Perplexity automatically changed to 45 because the original setting of 100 was too large for the number of samples (141)
# Perplexity automatically changed to 18 because the original setting of 100 was too large for the number of samples (60)
# Read the 78 x 8 data matrix successfully!
# Using user supplied starting positions
# OpenMP is working. 1 threads.
# Using no_dims = 1, perplexity = 24.000000, and theta = 0.100000
# Computing input similarities...
# Building tree...
# Done in 0.00 seconds (sparsity = 0.978961)!
# Learning embedding...
# Iteration 50: error is 0.074802 (50 iterations in 0.00 seconds)
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# Using user supplied starting positions
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# Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
# Computing input similarities...
# Building tree...
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# Learning embedding...
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# Using user supplied starting positions
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# Using no_dims = 1, perplexity = 100.000000, and theta = 0.100000
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# Learning embedding...
# Iteration 50: error is 0.579402 (50 iterations in 0.05 seconds)
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# Learning embedding...
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# Learning embedding...
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# Computing input similarities...
# Building tree...
# Done in 0.01 seconds (sparsity = 0.990128)!
# Learning embedding...
# Iteration 50: error is 0.155247 (50 iterations in 0.01 seconds)
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# Using no_dims = 1, perplexity = 20.000000, and theta = 0.100000
# Computing input similarities...
# Building tree...
# Done in 0.00 seconds (sparsity = 0.980469)!
# Learning embedding...
# Iteration 50: error is 0.147598 (50 iterations in 0.00 seconds)
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# Using user supplied starting positions
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# Learning embedding...
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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_3.3.5 fastTopics_0.6-117 Matrix_1.2-18
#
# 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 bslib_0.3.1 RcppParallel_5.1.5 assertthat_0.2.1
# [9] highr_0.8 mixsqp_0.3-46 yaml_2.2.0 progress_1.2.2
# [13] ggrepel_0.9.1 pillar_1.6.2 backports_1.1.5 lattice_0.20-38
# [17] quadprog_1.5-8 quantreg_5.54 glue_1.4.2 digest_0.6.23
# [21] promises_1.1.0 colorspace_1.4-1 htmltools_0.5.2 httpuv_1.5.2
# [25] pkgconfig_2.0.3 invgamma_1.1 SparseM_1.78 purrr_0.3.4
# [29] scales_1.1.0 whisker_0.4 later_1.0.0 Rtsne_0.15
# [33] MatrixModels_0.4-1 git2r_0.26.1 tibble_3.1.3 farver_2.0.1
# [37] generics_0.0.2 ellipsis_0.3.2 withr_2.5.0 ashr_2.2-54
# [41] pbapply_1.5-1 lazyeval_0.2.2 magrittr_2.0.1 crayon_1.4.1
# [45] mcmc_0.9-6 evaluate_0.14 fs_1.5.2 fansi_0.4.0
# [49] MASS_7.3-51.4 truncnorm_1.0-8 prettyunits_1.1.1 tools_3.6.2
# [53] data.table_1.12.8 hms_1.1.0 lifecycle_1.0.0 stringr_1.4.0
# [57] MCMCpack_1.4-5 plotly_4.9.2 munsell_0.5.0 irlba_2.3.3
# [61] compiler_3.6.2 jquerylib_0.1.4 rlang_0.4.11 grid_3.6.2
# [65] htmlwidgets_1.5.1 labeling_0.3 rmarkdown_2.11 gtable_0.3.0
# [69] DBI_1.1.0 R6_2.4.1 knitr_1.37 dplyr_1.0.7
# [73] uwot_0.1.10 fastmap_1.1.0 utf8_1.1.4 workflowr_1.7.0
# [77] rprojroot_1.3-2 stringi_1.4.3 parallel_3.6.2 SQUAREM_2017.10-1
# [81] Rcpp_1.0.7 vctrs_0.3.8 tidyselect_1.1.1 xfun_0.29
# [85] coda_0.19-3