Connectivity analysis with PAGA
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandMay 06, 2021
Last updated: 2021-05-06
Checks: 4 3
Knit directory: MS_lesions/
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- plot_paga_olg_wm_gm
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|---|---|---|---|---|
| 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 |
Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms09_ancombc.R')
source('code/ms11_paga.R')
use_condaenv('scanpy', required=TRUE)
source_python('code/ms11_paga_fns.py')Inputs
# define inputs
graph_f = 'output/ms04_conos/conos_graph_2021-02-11.txt'
# merged_f = 'output/ms06_sccaf/conos_merged_dt_2021-02-13.csv'
# merged_f = 'output/ms06_sccaf/conos_merged_dt_2021-03-11.csv'
merged_f = 'output/ms06_sccaf/conos_merged_dt_2021-03-18.csv'
meta_f = 'data/metadata/metadata_updated_20201127.txt'
byhand_f = 'data/byhand_markers/Copy of Copy of Marker_selection_for_validation_MS_snucseq_30102020 Ediinburgh.xlsx - final markers for Cartana panel.csv'Outputs
save_dir = 'output/ms11_paga'
if (!dir.exists(save_dir))
dir.create(save_dir)
# date_tag = '2021-03-03'
# seed = 20210303
# date_tag = '2021-03-12'
date_tag = '2021-03-18'
seed = 20210318
# define what is strange sample in neuro %
mad_cut = 2
subg_pat = sprintf('%s/subgraph_%s_%s.txt.gz', save_dir, '%s', date_tag)
subset_list = list(
all = list(),
healthy_WM = list(lesion_type = c('WM'), neuro_ok = TRUE),
MS_WM = list(lesion_type = c('NAWM', 'AL', 'CAL', 'CIL', 'RL'), neuro_ok = TRUE),
healthy_GM = list(lesion_type = c('GM'), neuro_ok = TRUE),
MS_GM = list(lesion_type = c('NAGM', 'GML'), neuro_ok = TRUE)
)
lesion_list = lapply(lesion_ord, function(l)
list(lesion_type = l, neuro_ok = TRUE)) %>%
setNames(paste0('lesion_', lesion_ord)
)
xy_ref_list = c(rep('WM', 6), rep('GM', 3)) %>%
paste0('lesion_', .) %>% setNames(names(lesion_list))
# what to look at when comparing edges across samples?
sel_broad = c('Oligodendrocytes', 'OPCs / COPs')
assert_that(all(sel_broad %in% broad_ord))[1] TRUELoad inputs
# load conos
conos_dt = merged_f %>% fread %>%
.[, .(cell_id, sample_id, conos = conos_merge)]
labels_dt = conos_dt[, .(conos)] %>% unique
byhand_dt = byhand_f %>% fread %>% janitor::clean_names(.) %>%
.[, .(
conos = cluster_id,
type_broad = broad_celltype,
type_fine = proposed_cluster_label
)]
labels_dt = merge(labels_dt, byhand_dt, by = 'conos', all = TRUE) %>%
.[, type_broad := factor(type_broad, levels = broad_ord)] %>%
.[, type_fine := fct_reorder(type_fine, as.integer(type_broad))]
conos_dt = merge(conos_dt, labels_dt, by = 'conos')
meta_dt = load_meta_dt(meta_f, outlier_samples = NULL)
conos_dt = merge(conos_dt, meta_dt, by = 'sample_id') %>%
add_neuro_props(mad_cut)Processing / calculations
# make list of all samples
samples = unique(conos_dt$sample_id)
sample_list = lapply(samples, function(s) list(sample_id = s)) %>%
setNames(paste0('sample_', samples))
# make subgraphs for each specification
set.seed(seed)
calc_subgraphs(conos_dt, subset_list, graph_f, subg_pat, n_sample = 1e5)already done!NULLcalc_subgraphs(conos_dt, lesion_list, graph_f, subg_pat, n_sample = 1e5)already done!NULLcalc_subgraphs(conos_dt, sample_list, graph_f, subg_pat, n_sample = 0)already done!NULL# run on each sample
if (!check_all_done(sample_list, save_dir, date_tag)) {
bpparam = MulticoreParam(workers = 16)
bplapply(seq_along(sample_list), function(i) {
spec_n = names(sample_list)[[i]]
subg_f = sprintf(subg_pat, spec_n)
run_paga(save_dir, spec_n, date_tag, r_to_py(as.data.frame(conos_dt)),
subg_f, group_var = 'type_fine')
}, BPPARAM = bpparam)
bpstop()
}
# run on each lesion type
if (!check_all_done(lesion_list, save_dir, date_tag)) {
bpparam = MulticoreParam(workers = length(lesion_list))
bplapply(seq_along(lesion_list), function(i) {
spec_n = names(lesion_list)[[i]]
subg_f = sprintf(subg_pat, spec_n)
run_paga(save_dir, spec_n, date_tag, r_to_py(as.data.frame(conos_dt)),
subg_f, group_var = 'type_fine')
}, BPPARAM = bpparam)
bpstop()
}
# run on each subset
if (!check_all_done(subset_list, save_dir, date_tag)) {
bpparam = MulticoreParam(workers = length(subset_list))
bplapply(seq_along(subset_list), function(i) {
# define things
spec_n = names(subset_list)[[i]]
subg_f = sprintf(subg_pat, spec_n)
# run paga
run_paga(save_dir, spec_n, date_tag, r_to_py(as.data.frame(conos_dt)),
subg_f, group_var = 'type_fine')
}, BPPARAM = bpparam)
bpstop()
}edges_all = calc_edges_over_samples(sample_list, date_tag,
sel_broad, labels_dt)
edges_all = merge(edges_all, meta_dt, by = 'sample_id')Analysis
PAGA on all celltypes
Split by GM / WM + condition
for (n in names(subset_list)) {
cat('#### ', n, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, n, date_tag, 'all',
conos_dt, subset_list[[n]], labels_dt, xy_ref = 'all'))})
cat('\n\n')
}all
healthy_WM
MS_WM
healthy_GM
MS_GM
Split by lesion type
for (n in names(lesion_list)) {
cat('#### ', n, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, n, date_tag, 'all',
conos_dt, lesion_list[[n]], labels_dt, xy_ref = xy_ref_list[[n]]))})
cat('\n\n')
}lesion_WM
lesion_NAWM
lesion_AL
lesion_CAL
lesion_CIL
lesion_RL
lesion_GM
lesion_NAGM
lesion_GML
PAGA on oligo + OPC compartment
Split by GM / WM + condition
for (n in names(subset_list)) {
cat('#### ', n, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, n, date_tag, 'olg',
conos_dt, subset_list[[n]], labels_dt, xy_ref = 'all'))})
cat('\n\n')
}all
healthy_WM
MS_WM
healthy_GM
MS_GM
Split by lesion type
for (n in names(lesion_list)) {
cat('#### ', n, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, n, date_tag, 'olg',
conos_dt, lesion_list[[n]], labels_dt, xy_ref = xy_ref_list[[n]]))})
cat('\n\n')
}lesion_WM
lesion_NAWM
lesion_AL
lesion_CAL
lesion_CIL
lesion_RL
lesion_GM
lesion_NAGM
lesion_GML
Heatmap of PAGA edges across samples, within oligo + OPC compartment
cat('### Data-driven')
draw(plot_edge_heatmap(edges_all, conos_dt, split = 'data'))
cat('\n')
cat('### Data-driven, no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE], split = 'data'))
cat('\n')
cat('### Data-driven, WM no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE & matter == 'WM'],
split = 'data'))
cat('\n')
cat('### Data-driven, GM no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE & matter == 'GM'],
split = 'data'))
cat('\n')
cat('### Split by lesion')
draw(plot_edge_heatmap(edges_all, conos_dt, split = 'lesion_type'))
cat('\n')
cat('### Split by lesion, no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE], split = 'lesion_type'))
cat('\n')Barplot of oligo proportions split by sample
for (m in c('WM', 'GM')) {
cat('### ', m, '\n')
print(plot_sample_splits(conos_dt[matter == m],
types = c('Oligodendrocytes', 'OPCs / COPs')))
cat('\n\n')
}WM
GM
Barplot of neuron proportions split by sample
for (m in c('WM', 'GM')) {
cat('### ', m, '\n')
print(plot_sample_splits(conos_dt[matter == m],
types = c('Excitatory neurons', 'Inhibitory neurons')))
cat('\n\n')
}WM
GM
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 2021-05-06
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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 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] BiocParallel_1.24.1 ComplexHeatmap_2.6.2 ggrepel_0.9.1
[4] reticulate_1.18 MASS_7.3-53.1 phyloseq_1.34.0
[7] ANCOMBC_1.0.5 purrr_0.3.4 patchwork_1.1.1
[10] forcats_0.5.1 ggplot2_3.3.3 scales_1.1.1
[13] viridis_0.6.0 viridisLite_0.4.0 assertthat_0.2.1
[16] stringr_1.4.0 data.table_1.14.0 magrittr_2.0.1
[19] circlize_0.4.12 RColorBrewer_1.1-2 BiocStyle_2.18.1
[22] colorout_1.2-2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-0 rjson_0.2.20
[4] ellipsis_0.3.1 rprojroot_2.0.2 XVector_0.30.0
[7] GlobalOptions_0.1.2 fs_1.5.0 rstudioapi_0.13
[10] clue_0.3-59 farver_2.1.0 remotes_2.3.0
[13] fansi_0.4.2 codetools_0.2-18 splines_4.0.3
[16] cachem_1.0.4 knitr_1.32 pkgload_1.2.1
[19] ade4_1.7-16 jsonlite_1.7.2 nloptr_1.2.2.2
[22] Cairo_1.5-12.2 cluster_2.1.2 png_0.1-7
[25] BiocManager_1.30.12 compiler_4.0.3 fastmap_1.1.0
[28] Matrix_1.3-2 cli_2.4.0 later_1.1.0.1
[31] htmltools_0.5.1.1 prettyunits_1.1.1 tools_4.0.3
[34] igraph_1.2.6 gtable_0.3.0 glue_1.4.2
[37] reshape2_1.4.4 dplyr_1.0.5 rappdirs_0.3.3
[40] Rcpp_1.0.6 Biobase_2.50.0 jquerylib_0.1.3
[43] vctrs_0.3.7 Biostrings_2.58.0 rhdf5filters_1.2.0
[46] multtest_2.46.0 ape_5.4-1 nlme_3.1-152
[49] iterators_1.0.13 xfun_0.22 ps_1.6.0
[52] rbibutils_2.1 testthat_3.0.2 lifecycle_1.0.0
[55] devtools_2.4.0 zlibbioc_1.36.0 hms_1.0.0
[58] promises_1.2.0.1 parallel_4.0.3 biomformat_1.18.0
[61] rhdf5_2.34.0 yaml_2.2.1 memoise_2.0.0
[64] gridExtra_2.3 sass_0.3.1 stringi_1.5.3
[67] highr_0.9 desc_1.3.0 S4Vectors_0.28.1
[70] foreach_1.5.1 permute_0.9-5 BiocGenerics_0.36.1
[73] pkgbuild_1.2.0 shape_1.4.5 Rdpack_2.1.1
[76] rlang_0.4.10 pkgconfig_2.0.3 matrixStats_0.58.0
[79] evaluate_0.14 lattice_0.20-41 Rhdf5lib_1.12.1
[82] processx_3.5.1 tidyselect_1.1.0 plyr_1.8.6
[85] R6_2.5.0 IRanges_2.24.1 generics_0.1.0
[88] DBI_1.1.1 pillar_1.6.0 whisker_0.4
[91] withr_2.4.2 mgcv_1.8-35 survival_3.2-10
[94] tibble_3.1.1 crayon_1.4.1 utf8_1.2.1
[97] microbiome_1.12.0 rmarkdown_2.7 usethis_2.0.1
[100] GetoptLong_1.0.5 progress_1.2.2 callr_3.6.0
[103] git2r_0.28.0 vegan_2.5-7 digest_0.6.27
[106] tidyr_1.1.3 httpuv_1.5.5 stats4_4.0.3
[109] munsell_0.5.0 bslib_0.2.4 sessioninfo_1.1.1