Evaluation of fastTopics DE analysis methods in simulations—case of 2 topics
Peter Carbonetto
Last updated: 2022-01-05
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Knit directory: single-cell-topics/analysis/
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Here we evaluate performance of the grade-of-membership DE methods in simulations, focussing on the case of \(K = 2\) topics. A smaller simulation with \(K = 2\) topics was first performed here, and based on this initial simulation we have conducted a larger set of simulations using the run_sims.R script. Here we summarize the results of this larger set of simulations.
Load the packages needed for this analysis, and some additional functions used to compile the results and generate the plots.
library(Matrix)
library(fastTopics)
library(ggplot2)
library(cowplot)
source("../code/de_analysis_functions.R")Load the results of the simulations.
load("../output/sims/sims-k=2.RData")These were the R packages used to run the simulations:
res$session.info
# R version 4.1.0 (2021-05-18)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Scientific Linux 7.4 (Nitrogen)
#
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
#
# locale:
# [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
# [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
# [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
# [9] LC_ADDRESS=C LC_TELEPHONE=C
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#
# attached base packages:
# [1] stats4 tools stats graphics grDevices utils datasets
# [8] methods base
#
# other attached packages:
# [1] fastTopics_0.6-97 MAST_1.20.0
# [3] SeuratObject_4.0.2 Seurat_4.0.3
# [5] DESeq2_1.34.0 scran_1.22.1
# [7] scuttle_1.4.0 SingleCellExperiment_1.16.0
# [9] SummarizedExperiment_1.24.0 Biobase_2.54.0
# [11] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
# [13] IRanges_2.28.0 S4Vectors_0.32.0
# [15] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
# [17] matrixStats_0.60.1 Matrix_1.3-3
#
# loaded via a namespace (and not attached):
# [1] utf8_1.2.1 reticulate_1.20
# [3] tidyselect_1.1.1 RSQLite_2.2.8
# [5] AnnotationDbi_1.56.1 htmlwidgets_1.5.3
# [7] grid_4.1.0 BiocParallel_1.28.0
# [9] Rtsne_0.15 munsell_0.5.0
# [11] ScaledMatrix_1.2.0 codetools_0.2-18
# [13] ica_1.0-2 statmod_1.4.36
# [15] future_1.21.0 miniUI_0.1.1.1
# [17] colorspace_2.0-2 rstudioapi_0.13
# [19] ROCR_1.0-11 tensor_1.5
# [21] listenv_0.8.0 GenomeInfoDbData_1.2.7
# [23] mixsqp_0.3-43 polyclip_1.10-0
# [25] MCMCpack_1.6-0 bit64_4.0.5
# [27] coda_0.19-4 parallelly_1.26.1
# [29] vctrs_0.3.8 generics_0.1.0
# [31] R6_2.5.0 rsvd_1.0.5
# [33] invgamma_1.1 locfit_1.5-9.4
# [35] bitops_1.0-7 spatstat.utils_2.2-0
# [37] cachem_1.0.5 DelayedArray_0.20.0
# [39] assertthat_0.2.1 promises_1.2.0.1
# [41] scales_1.1.1 gtable_0.3.0
# [43] beachmat_2.10.0 globals_0.14.0
# [45] conquer_1.0.2 goftest_1.2-2
# [47] mcmc_0.9-7 rlang_0.4.11
# [49] MatrixModels_0.5-0 genefilter_1.76.0
# [51] splines_4.1.0 lazyeval_0.2.2
# [53] spatstat.geom_2.2-0 reshape2_1.4.4
# [55] abind_1.4-5 httpuv_1.6.1
# [57] ggplot2_3.3.5 ellipsis_0.3.2
# [59] spatstat.core_2.2-0 RColorBrewer_1.1-2
# [61] ggridges_0.5.3 Rcpp_1.0.7
# [63] plyr_1.8.6 sparseMatrixStats_1.6.0
# [65] progress_1.2.2 zlibbioc_1.40.0
# [67] purrr_0.3.4 RCurl_1.98-1.5
# [69] prettyunits_1.1.1 rpart_4.1-15
# [71] deldir_0.2-10 pbapply_1.4-3
# [73] ashr_2.2-51 cowplot_1.1.1
# [75] zoo_1.8-9 ggrepel_0.9.1
# [77] cluster_2.1.2 magrittr_2.0.1
# [79] data.table_1.14.0 scattermore_0.7
# [81] SparseM_1.81 lmtest_0.9-38
# [83] RANN_2.6.1 truncnorm_1.0-8
# [85] SQUAREM_2021.1 fitdistrplus_1.1-5
# [87] hms_1.1.0 patchwork_1.1.1
# [89] mime_0.11 xtable_1.8-4
# [91] XML_3.99-0.6 gridExtra_2.3
# [93] compiler_4.1.0 tibble_3.1.2
# [95] KernSmooth_2.23-20 crayon_1.4.1
# [97] htmltools_0.5.1.1 mgcv_1.8-35
# [99] later_1.2.0 tidyr_1.1.3
# [101] geneplotter_1.72.0 RcppParallel_5.1.4
# [103] DBI_1.1.1 MASS_7.3-54
# [105] quadprog_1.5-8 parallel_4.1.0
# [107] metapod_1.2.0 igraph_1.2.6
# [109] pkgconfig_2.0.3 plotly_4.9.4.1
# [111] spatstat.sparse_2.0-0 annotate_1.72.0
# [113] dqrng_0.3.0 XVector_0.34.0
# [115] stringr_1.4.0 digest_0.6.27
# [117] sctransform_0.3.2 RcppAnnoy_0.0.18
# [119] spatstat.data_2.1-0 Biostrings_2.62.0
# [121] leiden_0.3.8 uwot_0.1.10
# [123] edgeR_3.36.0 DelayedMatrixStats_1.16.0
# [125] shiny_1.6.0 quantreg_5.86
# [127] lifecycle_1.0.0 nlme_3.1-152
# [129] jsonlite_1.7.2 BiocNeighbors_1.12.0
# [131] viridisLite_0.4.0 limma_3.50.0
# [133] fansi_0.5.0 pillar_1.6.1
# [135] lattice_0.20-44 KEGGREST_1.34.0
# [137] fastmap_1.1.0 httr_1.4.2
# [139] survival_3.2-11 glue_1.4.2
# [141] png_0.1-7 bluster_1.4.0
# [143] bit_4.0.4 stringi_1.6.2
# [145] blob_1.2.1 BiocSingular_1.10.0
# [147] memoise_2.0.0 dplyr_1.0.7
# [149] irlba_2.3.3 future.apply_1.7.0Before comparing the methods, we first assess accuracy of the Monte Carlo computations by comparing the estimates from two independent MCMC runs.
pdat <- data.frame(lfc1 = combine_sim_res(res,function (x) x$de1$postmean[,2]),
lfc2 = combine_sim_res(res,function (x) x$de2$postmean[,2]),
z1 = combine_sim_res(res,function (x) x$de1$z[,2]),
z2 = combine_sim_res(res,function (x) x$de2$z[,2]))
p1 <- ggplot(pdat,aes(x = lfc1,y = lfc2)) +
geom_point(color = "dodgerblue",shape = 4,size = 0.75) +
geom_abline(intercept = 0,slope = 1,linetype = "dotted") +
labs(x = "first posterior mean",y = "second posterior mean") +
theme_cowplot(font_size = 12)
p2 <- ggplot(pdat,aes(x = z1,y = z2)) +
geom_point(color = "dodgerblue",shape = 4,size = 0.75) +
geom_abline(intercept = 0,slope = 1,linetype = "dotted") +
labs(x = "first z-score estimate",y = "second z-score estimate") +
theme_cowplot(font_size = 12)
plot_grid(p1,p2)
The estimates of the posterior mean LFCs and z-scores generated by both MCMC runs are very similar.
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_0.6-96 Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] httr_1.4.2 tidyr_1.1.3 jsonlite_1.7.2 viridisLite_0.3.0
# [5] RcppParallel_4.4.2 assertthat_0.2.1 mixsqp_0.3-46 yaml_2.2.0
# [9] progress_1.2.2 ggrepel_0.9.1 pillar_1.6.2 backports_1.1.5
# [13] lattice_0.20-38 quantreg_5.54 glue_1.4.2 quadprog_1.5-8
# [17] digest_0.6.23 promises_1.1.0 colorspace_1.4-1 htmltools_0.4.0
# [21] httpuv_1.5.2 pkgconfig_2.0.3 invgamma_1.1 SparseM_1.78
# [25] purrr_0.3.4 scales_1.1.0 whisker_0.4 later_1.0.0
# [29] Rtsne_0.15 MatrixModels_0.4-1 git2r_0.26.1 tibble_3.1.3
# [33] farver_2.0.1 generics_0.0.2 ellipsis_0.3.2 withr_2.4.2
# [37] ashr_2.2-51 pbapply_1.5-1 lazyeval_0.2.2 magrittr_2.0.1
# [41] crayon_1.4.1 mcmc_0.9-6 evaluate_0.14 fs_1.3.1
# [45] fansi_0.4.0 MASS_7.3-51.4 truncnorm_1.0-8 tools_3.6.2
# [49] data.table_1.12.8 prettyunits_1.1.1 hms_1.1.0 lifecycle_1.0.0
# [53] stringr_1.4.0 MCMCpack_1.4-5 plotly_4.9.2 munsell_0.5.0
# [57] irlba_2.3.3 compiler_3.6.2 systemfonts_1.0.2 rlang_0.4.11
# [61] grid_3.6.2 htmlwidgets_1.5.1 labeling_0.3 rmarkdown_2.3
# [65] gtable_0.3.0 DBI_1.1.0 R6_2.4.1 knitr_1.26
# [69] dplyr_1.0.7 uwot_0.1.10 utf8_1.1.4 workflowr_1.6.2
# [73] rprojroot_1.3-2 ragg_0.3.1 stringi_1.4.3 parallel_3.6.2
# [77] SQUAREM_2017.10-1 Rcpp_1.0.7 vctrs_0.3.8 tidyselect_1.1.1
# [81] xfun_0.11 coda_0.19-3