Checks: GM vs WM muscat genes
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandJuly 23, 2021
Last updated: 2021-07-23
Checks: 5 2
Knit directory: MS_lesions/
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Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms10_muscat_runs.R')
source('code/supp10_muscat.R')Inputs
Outputs
# what to do?
run_tag = 'run20'
time_stamp = '2021-07-19'Load inputs
model_dir = file.path('output/ms10_muscat', run_tag)
muscat_f = '%s/muscat_res_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
checks_f = '%s/muscat_checks_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
params_f = '%s/muscat_params_%s_%s.rds' %>%
sprintf(model_dir, run_tag, time_stamp)
mds_mat_f = '%s/mds_dist_%s_%s.rds' %>%
sprintf(model_dir, run_tag, time_stamp)
mds_all_f = '%s/mds_all_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
mds_sep_f = '%s/mds_sep_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
fgsea_pat = '%s/fgsea_dt_%s_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, '%s', time_stamp)
fgsea_fs = sapply(names(paths_list),
function(n) sprintf(fgsea_pat, n))
# check all exist
assert_that(
file.exists(muscat_f),
file.exists(params_f)
# all(file.exists(fgsea_fs))
)[1] TRUEmds_flag = all(file.exists(c(mds_mat_f, mds_all_f), mds_sep_f))
if (!mds_flag)
warning('mds files not complete; not plotting')Warning: mds files not complete; not plotting# load them
params = params_f %>% readRDS
if (is.null(params$fc_cut))
params$fc_cut = 1
# load_muscat_inputs
pb = readRDS(params$pb_f) %>%
.subset_pb(params$subset_spec) subsetting pb object restricting to samples that meet subset criteria updating factors to remove levels no longer observedlabels_dt = .load_labels_dt(labels_f, params$cluster_var)
magma_dt = .load_magma_dt(magma_f, pb)
tfs_dt = .load_tfs_dt(tfs_f, pb)
lof_dt = .load_lof_dt(lof_f, pb)
coloc_dt = .load_coloc_dt(coloc_f, pb)
# get muscat results
res_dt = muscat_f %>% fread %>%
.load_muscat_results(labels_dt, params)
# prep for stacked bars
signif_dt = res_dt[ updown_soup != 'insignif' & !is.na(p_adj.soup) ]
assert_that(all(abs(signif_dt$logFC) >= params$fc_cut))[1] TRUEuniques_dt = .calc_uniques_dt(signif_dt, params)
stacked_dt = .calc_stacked_dt(uniques_dt)
# restrict to significant genes only
top_dt = .calc_top_genes(signif_dt, res_dt, uniques_dt,
magma_dt, tfs_dt, lof_dt, coloc_dt, params$fc_cut, n_top = 10)
top_gwas_dt = .calc_top_gwas_dt(res_dt, uniques_dt, magma_dt,
tfs_dt, lof_dt, coloc_dt, fc_cut = NULL, magma_cut = 0.05)
# load GSEA results
fgsea_list = .load_fgseas_list(fgsea_fs, labels_dt)Processing / calculations
Analysis
Barplot of significant genes by contrast
for (what in c('test', 'confounder', 'all')) {
if (what == 'all') {
tmp_dt = copy(stacked_dt)
} else {
tmp_dt = stacked_dt[ var_type == what ]
}
cat('## ', what, ' variables\n')
print(plot_n_signif_barplot(tmp_dt, facet_by = 'test_var'))
cat('\n\n')
}test variables
confounder variables
all variables
Number of significant genes by contrast
print(plot_n_signif_heatmap(signif_dt, params$cluster_var))
Top GM genes, split by broad celltype
sel_test = 'GM'
n_top_paths = 40
for (b in broad_ord) {
tmp_dt = top_dt[ type_broad == b & test_var == sel_test ]
if (nrow(tmp_dt) < 10)
next
cat('### ', b, '\n')
draw(plot_top_genes_heatmap_broad(tmp_dt, res_dt, labels_dt,
n_top_paths = n_top_paths), padding = unit(c(0.5, 0.1, 0.1, 0.1), "in"))
.add_fdr_legend('log2fc')
cat('\n\n')
}OPCs / COPs
Oligodendrocytes
Astrocytes
Microglia
Excitatory neurons
Pericytes
Summary of GSEA results
sel_sets = c('go_bp', 'hallmark', 'TFs')
this_type = 'test'
for (b in broad_ord) {
cat('## ', b, '\n', sep = '')
print(plot_gsea_dotplot_summary(fgsea_list, labels_dt, sel_sets, this_type,
sel_broad = b, fgsea_cut = params$fgsea_cut))
cat('\n\n')
}OPCs / COPs
Oligodendrocytes
Astrocytes
Microglia
Excitatory neurons
Inhibitory neurons
Endothelial cells
Pericytes
Immune
NULL
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 2021-07-23
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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 ggrepel_0.9.1
[5] patchwork_1.1.1 writexl_1.4.0
[7] readxl_1.3.1 ComplexHeatmap_2.6.2
[9] fgsea_1.16.0 tictoc_1.0.1
[11] performance_0.7.2 edgeR_3.32.1
[13] limma_3.46.0 reshape2_1.4.4
[15] scater_1.18.6 Matrix.utils_0.9.8
[17] Matrix_1.3-4 SingleCellExperiment_1.12.0
[19] SummarizedExperiment_1.20.0 Biobase_2.50.0
[21] MatrixGenerics_1.2.1 matrixStats_0.59.0
[23] seriation_1.2-9 UpSetR_1.4.0
[25] BiocParallel_1.24.1 muscat_1.5.1
[27] dplyr_1.0.7 purrr_0.3.4
[29] readr_1.4.0 tidyr_1.1.3
[31] tibble_3.1.2 tidyverse_1.3.1
[33] rtracklayer_1.50.0 GenomicRanges_1.42.0
[35] GenomeInfoDb_1.26.7 IRanges_2.24.1
[37] S4Vectors_0.28.1 BiocGenerics_0.36.1
[39] forcats_0.5.1 ggplot2_3.3.4
[41] scales_1.1.1 viridis_0.6.1
[43] viridisLite_0.4.0 assertthat_0.2.1
[45] stringr_1.4.0 data.table_1.14.0
[47] magrittr_2.0.1 circlize_0.4.13
[49] RColorBrewer_1.1-2 BiocStyle_2.18.1
[51] colorout_1.2-2 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_0.24
[19] hms_1.1.0 jquerylib_0.1.4
[21] evaluate_0.14 promises_1.2.0.1
[23] TSP_1.1-10 fansi_0.5.0
[25] progress_1.2.2 caTools_1.18.2
[27] dbplyr_2.1.1 DBI_1.1.1
[29] geneplotter_1.68.0 ellipsis_0.3.2
[31] backports_1.2.1 insight_0.14.2
[33] annotate_1.68.0 sparseMatrixStats_1.2.1
[35] vctrs_0.3.8 remotes_2.4.0
[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 labeling_0.4.2
[47] pkgconfig_2.0.3 pkgload_1.2.1
[49] nlme_3.1-152 vipor_0.4.5
[51] devtools_2.4.2 blme_1.0-5
[53] rlang_0.4.11 globals_0.14.0
[55] lifecycle_1.0.0 registry_0.5-1
[57] modelr_0.1.8 rsvd_1.0.5
[59] cellranger_1.1.0 rprojroot_2.0.2
[61] boot_1.3-28 reprex_2.0.0
[63] beeswarm_0.4.0 processx_3.5.2
[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_1.4.6
[75] parallelly_1.26.0 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_1.1-27.1 DESeq2_1.30.1
[85] Rsamtools_2.6.0 snakecase_0.11.0
[87] cli_2.5.0 XVector_0.30.0
[89] lmerTest_3.1-3 listenv_0.8.0
[91] ps_1.6.0 TMB_1.7.20
[93] MASS_7.3-54 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] janitor_2.1.0 gridExtra_2.3
[109] farver_2.1.0 digest_0.6.27
[111] BiocManager_1.30.16 Rcpp_1.0.6
[113] broom_0.7.7 scuttle_1.0.4
[115] later_1.2.0 httr_1.4.2
[117] AnnotationDbi_1.52.0 colorspace_2.0-1
[119] rvest_1.0.0 XML_3.99-0.6
[121] fs_1.5.0 splines_4.0.3
[123] sessioninfo_1.1.1 xtable_1.8-4
[125] jsonlite_1.7.2 nloptr_1.2.2.2
[127] testthat_3.0.3 R6_2.5.0
[129] pillar_1.6.1 htmltools_0.5.1.1
[131] glue_1.4.2 fastmap_1.1.0
[133] minqa_1.2.4 BiocNeighbors_1.8.2
[135] codetools_0.2-18 pkgbuild_1.2.0
[137] utf8_1.2.1 lattice_0.20-44
[139] bslib_0.2.5.1 numDeriv_2016.8-1.1
[141] pbkrtest_0.5.1 colorRamps_2.3
[143] gtools_3.9.2 survival_3.2-11
[145] glmmTMB_1.0.2.1 desc_1.3.0
[147] munsell_0.5.0 GetoptLong_1.0.5
[149] GenomeInfoDbData_1.2.4 iterators_1.0.13
[151] variancePartition_1.20.0 haven_2.4.1
[153] gtable_0.3.0