Checks: internal comparisons of muscat outputs
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandMay 21, 2021
Last updated: 2021-05-21
Checks: 5 2
Knit directory: MS_lesions/
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| Rmd | 6dc6cc6 | Macnair | 2021-04-29 | Updated muscat comparisons code |
| Rmd | 129c53d | Macnair | 2021-04-16 | Renamed a lot of things to add ms07_soup |
Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms10_muscat_runs.R')
source('code/supp10_muscat.R')Inputs
muscat_dir = 'output/ms10_muscat'
wout_ls = list(
tag = 'run01',
stamp = '2021-05-06'
)
with_ls = list(
tag = 'run09',
stamp = '2021-05-20'
# tag = 'run07',
# stamp = '2021-05-06'
# tag = 'run03',
# stamp = '2021-05-04'
)
ls_ls = list(wout = wout_ls, with = with_ls)
params_fs = lapply(ls_ls,
function(ls) sprintf('%s/%s/muscat_params_%s_%s.rds',
muscat_dir, ls$tag, ls$tag, ls$stamp)
)
res_fs = lapply(ls_ls,
function(ls) sprintf('%s/%s/muscat_res_dt_%s_%s.txt.gz',
muscat_dir, ls$tag, ls$tag, ls$stamp)
)Outputs
Load inputs
ps_wout = readRDS(params_fs[['wout']])
ps_with = readRDS(params_fs[['with']])
res_wout = load_results(res_fs[['wout']], ps_wout)
res_with = load_results(res_fs[['with']], ps_wout)if ('prop_rnd' %in% names(res_with)) {
var_exp_dt = load_var_exp(res_fs[['with']])
} else {
var_exp_dt = NULL
}pb = ps_wout$pb_f %>% readRDS %>%
.subset_pb(ps_wout$subset_spec) subsetting pb object restricting to samples that meet subset criteria updating factors to remove levels no longer observedif (!is.null(ps_with$output_fs$mds_dist_f))
mds_dist = sprintf(ps_with$output_fs$mds_dist_f, with_ls$stamp) %>%
readRDSProcessing / calculations
comps_all = merge(res_with, res_wout,
by = c('cluster_id', 'coef', 'test_var', 'gene_id', 'symbol'),
suffixes = c('.with', '.wout'))
comps_dt = comps_all[, .N,
by = .(cluster_id, coef, test_var, fc_cat.wout, fc_cat.with)]p_cut = 0.05
fc_cut = 1
status_dt = calc_status_dt(comps_all, p_cut, fc_cut)
ranks_dt = calc_ranks_dt(status_dt, confounders, p_cut)if (exists('mds_dist'))
silh_dt = calc_mds_silhouettes(mds_dist, pb)Analysis
Heatmaps of fold changes with / without patient effect
for (cl in broad_ord) {
cat('### ', cl, '\n')
tmp_dt = comps_all[cluster_id == cl]
suppressWarnings(print(plot_comparison(tmp_dt, cl, what = 'fc')))
cat('\n\n')
}Oligodendrocytes
OPCs / COPs
Astrocytes
Microglia
Excitatory neurons
Inhibitory neurons
Endothelial cells
Pericytes
Immune
Heatmaps of p-values with / without patient effect
for (cl in broad_ord) {
cat('### ', cl, '\n')
tmp_dt = comps_all[cluster_id == cl]
suppressWarnings(print(plot_comparison(tmp_dt, cl, what = 'logp')))
cat('\n\n')
}Oligodendrocytes
OPCs / COPs
Astrocytes
Microglia
Excitatory neurons
Inhibitory neurons
Endothelial cells
Pericytes
Immune
Check genes that were lost / gained
cat('### Signif only without patient_id\n')Signif only without patient_id
suppressWarnings(print(plot_sel_genes(ranks_dt, status_dt, pb, 'wout_rank')))
| Version | Author | Date |
|---|---|---|
| 9852840 | Macnair | 2021-05-12 |
cat('\n\n')cat('### Signif only with patient_id\n')Signif only with patient_id
suppressWarnings(print(plot_sel_genes(ranks_dt, status_dt, pb, 'with_rank')))
| Version | Author | Date |
|---|---|---|
| 9852840 | Macnair | 2021-05-12 |
cat('\n\n')Genes with large / small patient effects
if (!is.null(var_exp_dt)) {
cat('### Genes most affected by patients\n')
suppressWarnings(print(plot_patient_genes(var_exp_dt, status_dt, pb, 'pat_high')))
cat('\n\n')
cat('### Genes least affected by patients\n')
suppressWarnings(print(plot_patient_genes(var_exp_dt, status_dt, pb, 'pat_low')))
cat('\n\n')
}Distributions of patient variance %
if (!is.null(var_exp_dt))
(plot_var_exp_vs_cpm(var_exp_dt))Do samples cluster more by patient or by lesion?
if (exists('silh_dt')) {
for (cl in broad_ord) {
cat('### ', cl, '\n')
suppressWarnings(print(plot_mds_silhouettes(silh_dt, cl)))
cat('\n\n')
}
}
Do samples cluster more by patient or by lesion V2?
if (exists('silh_dt'))
print(plot_mds_silhouettes_2(silh_dt))
| Version | Author | Date |
|---|---|---|
| 9852840 | Macnair | 2021-05-12 |
Outputs
devtools::session_info()- Session info ---------------------------------------------------------------
setting value
version R version 4.0.3 (2020-10-10)
os CentOS Linux 7 (Core)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype C
tz Europe/Zurich
date 2021-05-21
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[1] /pstore/home/macnairw/lib/conda_r3.12
[2] /pstore/home/macnairw/.conda/envs/r_4.0.3/lib/R/library
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /pstore/home/macnairw/.conda/envs/r_4.0.3/lib/libopenblasp-r0.3.12.so
locale:
[1] LC_CTYPE=C 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] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] cluster_2.1.2 ggbeeswarm_0.6.0
[3] rmarkdown_2.8 patchwork_1.1.1
[5] writexl_1.4.0 ComplexHeatmap_2.6.2
[7] fgsea_1.16.0 tictoc_1.0.1
[9] edgeR_3.32.1 limma_3.46.0
[11] reshape2_1.4.4 scater_1.18.6
[13] Matrix.utils_0.9.8 Matrix_1.3-3
[15] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[17] Biobase_2.50.0 MatrixGenerics_1.2.1
[19] matrixStats_0.58.0 seriation_1.2-9
[21] UpSetR_1.4.0 BiocParallel_1.24.1
[23] muscat_1.5.1 dplyr_1.0.6
[25] purrr_0.3.4 readr_1.4.0
[27] tidyr_1.1.3 tibble_3.1.2
[29] tidyverse_1.3.1 rtracklayer_1.50.0
[31] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[33] IRanges_2.24.1 S4Vectors_0.28.1
[35] BiocGenerics_0.36.1 forcats_0.5.1
[37] ggplot2_3.3.3 scales_1.1.1
[39] viridis_0.6.1 viridisLite_0.4.0
[41] assertthat_0.2.1 stringr_1.4.0
[43] data.table_1.14.0 magrittr_2.0.1
[45] circlize_0.4.12 RColorBrewer_1.1-2
[47] BiocStyle_2.18.1 colorout_1.2-2
[49] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.1 bit64_4.0.5
[3] knitr_1.33 irlba_2.3.3
[5] DelayedArray_0.16.3 R.utils_2.10.1
[7] RCurl_1.98-1.3 doParallel_1.0.16
[9] generics_0.1.0 callr_3.7.0
[11] usethis_2.0.1 RSQLite_2.2.7
[13] future_1.21.0 bit_4.0.4
[15] xml2_1.3.2 lubridate_1.7.10
[17] httpuv_1.6.1 xfun_0.23
[19] hms_1.1.0 jquerylib_0.1.4
[21] evaluate_0.14 promises_1.2.0.1
[23] TSP_1.1-10 fansi_0.4.2
[25] progress_1.2.2 caTools_1.18.2
[27] dbplyr_2.1.1 readxl_1.3.1
[29] DBI_1.1.1 geneplotter_1.68.0
[31] ellipsis_0.3.2 backports_1.2.1
[33] annotate_1.68.0 sparseMatrixStats_1.2.1
[35] vctrs_0.3.8 remotes_2.3.0
[37] Cairo_1.5-12.2 cachem_1.0.5
[39] withr_2.4.2 grr_0.9.5
[41] sctransform_0.3.2 GenomicAlignments_1.26.0
[43] prettyunits_1.1.1 crayon_1.4.1
[45] genefilter_1.72.1 pkgconfig_2.0.3
[47] pkgload_1.2.1 nlme_3.1-152
[49] vipor_0.4.5 blme_1.0-5
[51] devtools_2.4.1 rlang_0.4.11
[53] globals_0.14.0 lifecycle_1.0.0
[55] registry_0.5-1 modelr_0.1.8
[57] rsvd_1.0.5 cellranger_1.1.0
[59] rprojroot_2.0.2 boot_1.3-28
[61] reprex_2.0.0 beeswarm_0.3.1
[63] processx_3.5.2 whisker_0.4
[65] GlobalOptions_0.1.2 png_0.1-7
[67] rjson_0.2.20 bitops_1.0-7
[69] R.oo_1.24.0 KernSmooth_2.23-20
[71] Biostrings_2.58.0 blob_1.2.1
[73] DelayedMatrixStats_1.12.3 shape_1.4.5
[75] parallelly_1.25.0 beachmat_2.6.4
[77] memoise_2.0.0 plyr_1.8.6
[79] gplots_3.1.1 zlibbioc_1.36.0
[81] compiler_4.0.3 clue_0.3-59
[83] lme4_1.1-27 DESeq2_1.30.1
[85] Rsamtools_2.6.0 cli_2.5.0
[87] XVector_0.30.0 lmerTest_3.1-3
[89] listenv_0.8.0 ps_1.6.0
[91] TMB_1.7.20 MASS_7.3-54
[93] mgcv_1.8-35 tidyselect_1.1.1
[95] stringi_1.6.2 highr_0.9
[97] yaml_2.2.1 BiocSingular_1.6.0
[99] locfit_1.5-9.4 sass_0.4.0
[101] fastmatch_1.1-0 tools_4.0.3
[103] future.apply_1.7.0 rstudioapi_0.13
[105] foreach_1.5.1 git2r_0.28.0
[107] gridExtra_2.3 farver_2.1.0
[109] digest_0.6.27 BiocManager_1.30.15
[111] Rcpp_1.0.6 broom_0.7.6
[113] scuttle_1.0.4 later_1.2.0
[115] httr_1.4.2 AnnotationDbi_1.52.0
[117] colorspace_2.0-1 rvest_1.0.0
[119] XML_3.99-0.6 fs_1.5.0
[121] splines_4.0.3 sessioninfo_1.1.1
[123] xtable_1.8-4 jsonlite_1.7.2
[125] nloptr_1.2.2.2 testthat_3.0.2
[127] R6_2.5.0 pillar_1.6.1
[129] htmltools_0.5.1.1 glue_1.4.2
[131] fastmap_1.1.0 minqa_1.2.4
[133] BiocNeighbors_1.8.2 codetools_0.2-18
[135] pkgbuild_1.2.0 utf8_1.2.1
[137] lattice_0.20-44 bslib_0.2.5
[139] numDeriv_2016.8-1.1 pbkrtest_0.5.1
[141] colorRamps_2.3 gtools_3.8.2
[143] survival_3.2-11 glmmTMB_1.0.2.1
[145] desc_1.3.0 munsell_0.5.0
[147] GetoptLong_1.0.5 GenomeInfoDbData_1.2.4
[149] iterators_1.0.13 variancePartition_1.20.0
[151] haven_2.4.1 gtable_0.3.0