MS: Identifying gene modules with non-negative matrix factorization
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandSeptember 14, 2021
Last updated: 2021-09-14
Checks: 4 3
Knit directory: MS_lesions/
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- plot_genes_dotplot
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2b5e1cc | Macnair | 2021-09-03 | Tweak ms08_modules |
| html | 2b5e1cc | Macnair | 2021-09-03 | Tweak ms08_modules |
| Rmd | 1342e46 | Macnair | 2021-08-24 | Update module analysis |
| html | 1342e46 | Macnair | 2021-08-24 | Update module analysis |
| Rmd | eef8a1c | Macnair | 2021-04-29 | Minor tweaks to allow rerunning on Roche servers |
| Rmd | 129c53d | Macnair | 2021-04-16 | Renamed a lot of things to add ms07_soup |
Done
- redo GSEA with FGSEA
- add FGSEA calcs to
get_pop_results - add FGSEA calc to
get_pop_results
- add FGSEA calcs to
- redo GSEA with FGSEA plus randomly ordered other genes
To-do
- try with pseudobulk instead?
Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms08_modules.R')
source_python('code/ms08_modules.py')Inputs
# base inputs
sce_f = 'data/sce_raw/ms_sce.rds'
labels_f = 'data/byhand_markers/validation_markers_2021-05-31.csv'
labelled_f = 'output/ms13_labelling/conos_labelled_2021-05-31.txt.gz'
meta_f = 'data/metadata/metadata_updated_20201127.txt'
# define pseudobulk files
soup_dir = 'output/ms07_soup'
date_soup = '2021-06-01'
pb_fine_f = sprintf('%s/pb_sum_fine_%s.rds', soup_dir, date_soup)
prop_fine_f = sprintf('%s/pb_prop_fine_%s.rds', soup_dir, date_soup)
# broad level pseudobulk files
pb_broad_f = file.path(soup_dir, 'pb_sum_broad_2021-06-01.rds')
pb_fine_f = file.path(soup_dir, 'pb_sum_fine_2021-06-01.rds')
prop_fine_f = file.path(soup_dir, 'pb_prop_fine_2021-06-01.rds')
pb_soup_f = file.path(soup_dir, 'pb_soup_broad_maximum_2021-06-01.rds')
gtf_f = 'data/gtf/Homo_sapiens.GRCh38.96.filtered.preMRNA.gtf'Outputs
# set up directory
save_dir = 'output/ms08_modules'
date_tag = '2021-08-18'
if (!dir.exists(save_dir))
dir.create(save_dir)
ncores = 12
# output file patterns
genes_pat = sprintf('%s/%s/features_%s_%s.tsv', save_dir, '%s', date_tag, '%s')
mtx_pat = sprintf('%s/%s/counts_%s_%s.mtx', save_dir, '%s', date_tag, '%s')
sce_pat = sprintf('%s/%s/sce_sub_%s_%s.rds', save_dir, '%s', date_tag, '%s')
ok_gs_pat = sprintf('%s/%s/ok_gs_%s_%s.txt', save_dir, '%s', date_tag, '%s')
pop_pat = sprintf('%s/%s/pop_%s_%s.p', save_dir, '%s', '%s', date_tag)
res_pat = sprintf('%s/%s/res_%s_%s.rds', save_dir, '%s', date_tag, '%s')
go_pat = sprintf('%s/%s/go_dt_%s_%s_%s.rds', save_dir, '%s', date_tag, '%s', '%s')
# lists of celltypes for each run
spec_list = list(
oligo_opc = list(type_broad = c('OPCs / COPs', 'Oligodendrocytes')),
micro_immune = list(type_broad = c('Microglia', 'Immune')),
excitatory = list(type_broad = 'Excitatory neurons'),
inhibitory = list(type_broad = 'Inhibitory neurons'),
astrocytes = list(type_broad = c('Astrocytes')),
endo_stromal = list(type_broad = c('Endothelial cells', 'Pericytes')),
microglia = list(type_broad = c('Microglia')),
immune = list(type_broad = c('Immune'))
)
assert_that(length(spec_list) == length(unique(names(spec_list))))[1] TRUEgroup_list = names(spec_list)
# how many per fine celltype?
n_sample = 2e3
n_genes = 2e3
max_soup = 0.1
ok_types = 'protein_coding'
# umap params
umap_many_f = 'output/ms04_conos/conos_umap_sub_2021-02-11.txt'
# umap_ps = list(
# min_dist = 0.1,
# spread = 8
# )
umap_ps = list(
min_dist = 1,
spread = 2
)
# define xls file to save
xl_f = sprintf('%s/modules_genes_%s.xlsx', save_dir, date_tag)Load inputs
labels_dt = load_names_dt(labels_f) %>%
.[, cluster_id := type_fine]
conos_dt = load_labelled_dt(labelled_f, labels_f)
meta_dt = load_meta_dt(meta_f)pb_soup = pb_soup_f %>% readRDS
pb_broad = pb_broad_f %>% readRDS
contam_dt = calc_contam_dt(pb_soup, pb_broad, min_cells = 10)
rm(pb_soup, pb_broad)
biotypes_dt = get_biotypes_dt(gtf_f)fine_dt = load_fine_dt(pb_fine_f, prop_fine_f, labels_dt)umap_dt = umap_many_f %>% fread %>%
.[ min_dist == umap_ps$min_dist & spread == umap_ps$spread ] %>%
.[, .(cell_id, UMAP1, UMAP2)]Processing / calculations
save_sub_sces(spec_list, sce_f, sce_pat, ok_gs_pat, conos_dt, contam_dt,
biotypes_dt, n_sample, n_genes, max_soup, ok_types, save_dir)already done!NULLsave_outputs_for_popalign(group_list, sce_pat, mtx_pat, genes_pat)already done!NULLrun_onmf(save_dir, date_tag, group_list, ncores = ncores)pop_list = group_list %>%
map(~get_pop_results(.x, res_pat, sce_pat, ok_gs_pat, go_pat, pop_pat,
conos_dt)) %>% setNames(group_list)Analysis
Median module scores by celltype (scaled)
for (g in group_list) {
cat('### ', g, '\n')
hm = plot_scores_by_celltype(pop_list[[g]]$scores_dt, pop_list[[g]]$k_order,
what = 'scaled')
if (!is.null(hm))
draw(hm)
cat('\n\n')
}
Median module scores by celltype (proportions)
for (g in group_list) {
cat('### ', g, '\n')
hm = plot_scores_by_celltype(pop_list[[g]]$scores_dt, pop_list[[g]]$k_order,
what = 'propns')
if (!is.null(hm))
draw(hm)
cat('\n\n')
}
Distribution of module scores over UMAP
for (g in group_list) {
cat('### ', g, '\n')
print(plot_scores_over_umap(pop_list[[g]]$scores_dt, pop_list[[g]]$k_order, umap_dt))
cat('\n\n')
}
Most important genes per module
for (g in group_list) {
cat('### ', g, '\n')
print(plot_biggest_genes_dotplot(g, spec_list, pop_list[[g]]$w_mat,
pop_list[[g]]$k_order, fine_dt, w2_cut = 0.02))
cat('\n\n')
}
Distribution of top genes over UMAP
for (g in group_list) {
cat('### ', g, '\n')
print(plot_genes_over_umap(pop_list[[g]]$genes_dt, umap_dt))
cat('\n\n')
}
Median module scores by celltype (scaled)
for (g in group_list) {
cat('### ', g, '\n')
hm = plot_scores_by_celltype(pop_list[[g]]$scores_dt, pop_list[[g]]$k_order,
what = 'scaled')
if (!is.null(hm))
draw(hm)
cat('\n\n')
}
Enriched GO terms per module (using 2k genes for GSEA)
source('code/ms08_modules.R')
for (g in group_list) {
cat('### ', g, '\n')
hm = plot_enriched_sets(pop_list[[g]]$go_std_dt, pop_list[[g]]$k_order)
if (!is.null(hm))
draw(hm)
cat('\n\n')
}oligo_opc
micro_immune
excitatory
inhibitory
astrocytes
endo_stromal
microglia
immune
Enriched GO terms per module (using more genes for GSEA)
for (g in group_list) {
cat('### ', g, '\n')
hm = plot_enriched_sets(pop_list[[g]]$go_all_dt, pop_list[[g]]$k_order)
if (!is.null(hm))
draw(hm)
cat('\n\n')
}oligo_opc
micro_immune
excitatory
inhibitory
astrocytes
endo_stromal
microglia
immune
UMAP celltype reference
(plot_umap_celltypes(umap_dt, conos_dt))
feat_list = scores_dt$feat %>% unique %>% sort %>% .[k_order]
for (f in feat_list) {
cat('### ', f, '\n')
print(plot_scores_over_lesions(scores_dt, f, meta_dt))
cat('\n\n')
}Distribution of fine celltypes over UMAP
for (g in group_list) {
cat('### ', g, '\n')
print(plot_celltypes_over_umap(spec_list[[g]], conos_dt, umap_dt))
cat('\n\n')
}
Outputs
save_module_genes_to_xl(pop_list, xl_f)NULLFigures
Selected genes over UMAP
g = 'oligo_opc'
sel_genes = c("TNR", "LRP1B", "CAMK2D", "QKI", "NCAM2", "NLGN1", "KIRREL3",
"MBP", "GLUL", "ELL2")
(plot_sel_genes_over_umap(g, pop_list, sce_pat, umap_dt, sel_genes))
| Version | Author | Date |
|---|---|---|
| 2b5e1cc | Macnair | 2021-09-03 |
devtools::session_info()Registered S3 method overwritten by 'cli':
method from
print.boxx spatstat.geom- Session info ---------------------------------------------------------------
setting value
version R version 4.0.5 (2021-03-31)
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 2021-09-14
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RCurl 1.98-1.3 2021-03-16 [1]
registry 0.5-1 2019-03-05 [1]
remotes 2.4.0 2021-06-02 [1]
reshape2 1.4.4 2020-04-09 [2]
reticulate * 1.20 2021-05-03 [2]
rjson 0.2.20 2018-06-08 [1]
rlang 0.4.11 2021-04-30 [2]
rmarkdown 2.10 2021-08-06 [1]
ROCR 1.0-11 2020-05-02 [2]
rpart 4.1-15 2019-04-12 [2]
rprojroot 2.0.2 2020-11-15 [2]
Rsamtools 2.6.0 2020-10-27 [1]
RSQLite 2.2.7 2021-04-22 [1]
rsvd 1.0.5 2021-04-16 [1]
rtracklayer 1.50.0 2020-10-27 [1]
Rtsne 0.15 2018-11-10 [2]
S4Vectors * 0.28.1 2020-12-09 [1]
sass 0.4.0 2021-05-12 [2]
scales * 1.1.1 2020-05-11 [2]
scater 1.18.6 2021-02-26 [1]
scattermore 0.7 2020-11-24 [2]
sctransform 0.3.2 2020-12-16 [2]
scuttle 1.0.4 2020-12-17 [1]
seriation * 1.2-9 2020-10-01 [1]
sessioninfo 1.1.1 2018-11-05 [1]
Seurat * 4.0.1 2021-03-18 [2]
SeuratObject * 4.0.1 2021-05-08 [2]
shape 1.4.6 2021-05-19 [1]
shiny 1.6.0 2021-01-25 [2]
SingleCellExperiment * 1.12.0 2020-10-27 [1]
snakecase 0.11.0 2019-05-25 [1]
sparseMatrixStats 1.2.1 2021-02-02 [1]
spatstat.core 2.1-2 2021-04-18 [2]
spatstat.data 2.1-0 2021-03-21 [2]
spatstat.geom 2.1-0 2021-04-15 [2]
spatstat.sparse 2.0-0 2021-03-16 [2]
spatstat.utils 2.2-0 2021-06-14 [2]
stringi 1.7.3 2021-07-16 [1]
stringr * 1.4.0 2019-02-10 [2]
SummarizedExperiment * 1.20.0 2020-10-27 [1]
survival 3.2-11 2021-04-26 [2]
tensor 1.5 2012-05-05 [2]
testthat 3.0.3 2021-06-16 [2]
tibble 3.1.3 2021-07-23 [1]
tidyr 1.1.3 2021-03-03 [2]
tidyselect 1.1.1 2021-04-30 [2]
TMB 1.7.20 2021-04-08 [1]
TSP 1.1-10 2020-04-17 [1]
usethis 2.0.1 2021-02-10 [1]
utf8 1.2.2 2021-07-24 [1]
uwot 0.1.10 2020-12-15 [2]
variancePartition 1.20.0 2020-10-27 [1]
vctrs 0.3.8 2021-04-29 [2]
vipor 0.4.5 2017-03-22 [1]
viridis * 0.6.1 2021-05-11 [1]
viridisLite * 0.4.0 2021-04-13 [1]
whisker 0.4 2019-08-28 [1]
withr 2.4.2 2021-04-18 [2]
workflowr * 1.6.2 2020-04-30 [1]
writexl * 1.4.0 2021-04-20 [1]
xfun 0.25 2021-08-06 [1]
XML 3.99-0.6 2021-03-16 [1]
xtable 1.8-4 2019-04-21 [2]
XVector 0.30.0 2020-10-27 [1]
yaml 2.2.1 2020-02-01 [2]
zlibbioc 1.36.0 2020-10-27 [1]
zoo 1.8-9 2021-03-09 [2]
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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
R -- Package was removed from disk.
sessionInfo()R version 4.0.5 (2021-03-31)
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] writexl_1.4.0 reticulate_1.20
[3] fgsea_1.16.0 BiocParallel_1.24.1
[5] ggplot.multistats_1.0.0 seriation_1.2-9
[7] ComplexHeatmap_2.6.2 SeuratObject_4.0.1
[9] Seurat_4.0.1 future_1.21.0
[11] Matrix_1.3-4 SingleCellExperiment_1.12.0
[13] SummarizedExperiment_1.20.0 Biobase_2.50.0
[15] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[17] IRanges_2.24.1 S4Vectors_0.28.1
[19] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
[21] matrixStats_0.60.0 purrr_0.3.4
[23] forcats_0.5.1 ggplot2_3.3.5
[25] scales_1.1.1 viridis_0.6.1
[27] viridisLite_0.4.0 assertthat_0.2.1
[29] stringr_1.4.0 data.table_1.14.0
[31] magrittr_2.0.1 circlize_0.4.13
[33] RColorBrewer_1.1-2 BiocStyle_2.18.1
[35] colorout_1.2-2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 rtracklayer_1.50.0
[3] scattermore_0.7 R.methodsS3_1.8.1
[5] tidyr_1.1.3 bit64_4.0.5
[7] knitr_1.33 irlba_2.3.3
[9] DelayedArray_0.16.3 R.utils_2.10.1
[11] rpart_4.1-15 doParallel_1.0.16
[13] RCurl_1.98-1.3 generics_0.1.0
[15] callr_3.7.0 cowplot_1.1.1
[17] usethis_2.0.1 RSQLite_2.2.7
[19] RANN_2.6.1 bit_4.0.4
[21] spatstat.data_2.1-0 lubridate_1.7.10
[23] httpuv_1.6.1 xfun_0.25
[25] hms_1.1.0 jquerylib_0.1.4
[27] evaluate_0.14 promises_1.2.0.1
[29] TSP_1.1-10 fansi_0.5.0
[31] progress_1.2.2 caTools_1.18.2
[33] igraph_1.2.6 DBI_1.1.1
[35] geneplotter_1.68.0 htmlwidgets_1.5.3
[37] spatstat.geom_2.1-0 ellipsis_0.3.2
[39] backports_1.2.1 dplyr_1.0.7
[41] annotate_1.68.0 deldir_0.2-10
[43] sparseMatrixStats_1.2.1 vctrs_0.3.8
[45] remotes_2.4.0 Cairo_1.5-12.2
[47] ROCR_1.0-11 abind_1.4-5
[49] cachem_1.0.5 withr_2.4.2
[51] sctransform_0.3.2 GenomicAlignments_1.26.0
[53] prettyunits_1.1.1 goftest_1.2-2
[55] cluster_2.1.2 lazyeval_0.2.2
[57] crayon_1.4.1 genefilter_1.72.1
[59] labeling_0.4.2 edgeR_3.32.1
[61] pkgconfig_2.0.3 pkgload_1.2.1
[63] vipor_0.4.5 nlme_3.1-152
[65] devtools_2.4.2 blme_1.0-5
[67] rlang_0.4.11 globals_0.14.0
[69] lifecycle_1.0.0 miniUI_0.1.1.1
[71] registry_0.5-1 rsvd_1.0.5
[73] rprojroot_2.0.2 polyclip_1.10-0
[75] lmtest_0.9-38 boot_1.3-28
[77] zoo_1.8-9 beeswarm_0.4.0
[79] processx_3.5.2 whisker_0.4
[81] ggridges_0.5.3 GlobalOptions_0.1.2
[83] png_0.1-7 rjson_0.2.20
[85] bitops_1.0-7 R.oo_1.24.0
[87] KernSmooth_2.23-20 Biostrings_2.58.0
[89] blob_1.2.1 DelayedMatrixStats_1.12.3
[91] shape_1.4.6 parallelly_1.26.0
[93] beachmat_2.6.4 memoise_2.0.0
[95] plyr_1.8.6 hexbin_1.28.2
[97] ica_1.0-2 gplots_3.1.1
[99] zlibbioc_1.36.0 compiler_4.0.5
[101] clue_0.3-59 lme4_1.1-27.1
[103] DESeq2_1.30.1 fitdistrplus_1.1-5
[105] cli_3.0.1 Rsamtools_2.6.0
[107] snakecase_0.11.0 XVector_0.30.0
[109] lmerTest_3.1-3 listenv_0.8.0
[111] ps_1.6.0 patchwork_1.1.1
[113] pbapply_1.4-3 TMB_1.7.20
[115] MASS_7.3-54 mgcv_1.8-36
[117] tidyselect_1.1.1 stringi_1.7.3
[119] highr_0.9 yaml_2.2.1
[121] BiocSingular_1.6.0 locfit_1.5-9.4
[123] ggrepel_0.9.1 muscat_1.5.1
[125] sass_0.4.0 fastmatch_1.1-0
[127] tools_4.0.5 future.apply_1.7.0
[129] foreach_1.5.1 git2r_0.28.0
[131] janitor_2.1.0 gridExtra_2.3
[133] farver_2.1.0 Rtsne_0.15
[135] digest_0.6.27 BiocManager_1.30.16
[137] shiny_1.6.0 Rcpp_1.0.7
[139] broom_0.7.7 scuttle_1.0.4
[141] later_1.2.0 RcppAnnoy_0.0.19
[143] httr_1.4.2 AnnotationDbi_1.52.0
[145] colorspace_2.0-2 XML_3.99-0.6
[147] fs_1.5.0 tensor_1.5
[149] splines_4.0.5 uwot_0.1.10
[151] spatstat.utils_2.2-0 scater_1.18.6
[153] sessioninfo_1.1.1 plotly_4.9.3
[155] xtable_1.8-4 jsonlite_1.7.2
[157] nloptr_1.2.2.2 testthat_3.0.3
[159] R6_2.5.0 pillar_1.6.2
[161] htmltools_0.5.1.1 mime_0.11
[163] glue_1.4.2 fastmap_1.1.0
[165] minqa_1.2.4 BiocNeighbors_1.8.2
[167] codetools_0.2-18 pkgbuild_1.2.0
[169] utf8_1.2.2 lattice_0.20-44
[171] bslib_0.2.5.1 spatstat.sparse_2.0-0
[173] tibble_3.1.3 pbkrtest_0.5.1
[175] numDeriv_2016.8-1.1 ggbeeswarm_0.6.0
[177] colorRamps_2.3 leiden_0.3.8
[179] gtools_3.9.2 survival_3.2-11
[181] limma_3.46.0 glmmTMB_1.0.2.1
[183] rmarkdown_2.10 desc_1.3.0
[185] munsell_0.5.0 GetoptLong_1.0.5
[187] GenomeInfoDbData_1.2.4 iterators_1.0.13
[189] variancePartition_1.20.0 reshape2_1.4.4
[191] gtable_0.3.0 spatstat.core_2.1-2