Connectivity analysis with PAGA - superclean
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandJanuary 05, 2022
Last updated: 2022-01-05
Checks: 5 2
Knit directory: MS_lesions/
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Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms09_ancombc_mixed.R')
source('code/ms11_paga.R')
use_condaenv('scanpy', required=TRUE)
source_python('code/ms11_paga_fns.py')
source('code/ms03_SampleQC.R')Inputs
# define inputs
qc_dir = 'output/ms03_SampleQC'
qc_f = 'output/ms03_SampleQC/ms_qc_dt.txt'
graph_f = 'output/ms04_conos/conos_graph_2021-02-11.txt'
# define run
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_checked_assumptions_2021-10-08.xlsx"Outputs
save_dir = 'output/ms11_paga'
if (!dir.exists(save_dir))
dir.create(save_dir)
date_tag = '2022-01-04_clean'
seed = 20220104
# do in parallel
n_cores = 16
# identifying strange samples
neuro_mad_cut = 2
log_n_mad_cut = 3
min_counts = 2000
min_feats = 500
max_mito = 0.1
max_splice = 2
min_cells = 500
# where to save files with subgraphs?
subg_pat = sprintf('%s/subgraph_%s_%s.txt.gz', save_dir, '%s', date_tag)
# define subsets to run
subset_list = list(
all = list(),
healthy_WM = list(lesion_type = c('WM')),
MS_WM = list(lesion_type = c('NAWM', 'AL', 'CAL', 'CIL', 'RL')),
healthy_GM = list(lesion_type = c('GM')),
MS_GM = list(lesion_type = c('NAGM', 'GML'))
)
lesion_list = lapply(lesion_ord, function(l)
list(lesion_type = l)) %>%
setNames(paste0('lesion_', lesion_ord)
)
xy_ref_list = c(rep('WM', 6), rep('GM', 3)) %>%
paste0('healthy_', .) %>% 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] TRUE# define ancom outputs to use
ancom_dir = 'output/ms09_ancombc'
ancom_stamp = '2021-11-12'
ancom_tags = c(WM = "lesions_WM", GM = "lesions_GM_4pcs")
ancom_fs = sapply(ancom_tags, function(t)
sprintf('%s/ancombc_bootstrap_%s_%s.txt.gz', ancom_dir, t, ancom_stamp)) %>%
setNames(names(ancom_tags))Load inputs
qc_dt = get_qc_dt(qc_dir, qc_f)
qc_keep = qc_dt[
(log_counts >= log10(min_counts)) &
(log_feats >= log10(min_feats)) &
(logit_mito <= qlogis(max_mito)) &
(splice_ratio <= max_splice)
] %>%
.[, N_sample := .N, by = sample_id] %>%
.[ N_sample >= min_cells ]meta_dt = load_meta_dt_from_xls(meta_f)
labels_dt = load_names_dt(labels_f) %>%
.[, cluster_id := type_fine]
conos_dt = load_labelled_dt(labelled_f, labels_f) %>%
merge(meta_dt, by = 'sample_id') %>%
add_neuro_props(mad_cut = neuro_mad_cut)# add lib size cutoff
conos_dt = conos_dt %>%
merge(qc_keep, by = c("sample_id", "cell_id"))# # check for any outliers
# size_chks = calc_size_outliers(conos_dt, mad_cut = log_n_mad_cut)
# message("these samples excluded to outlier sample sizes:")
# print(size_chks[ size_ok == FALSE ])
# # exclude them from conos
# ok_samples = size_chks[ size_ok == TRUE ]$sample_id
# conos_dt = conos_dt[ (sample_id %in% ok_samples) & (neuro_ok == TRUE) ]
conos_dt = conos_dt[ (neuro_ok == TRUE) ]# make list of all samples
samples = unique(conos_dt$sample_id)
sample_list = lapply(samples, function(s) list(sample_id = s)) %>%
setNames(paste0('sample_', samples))
conos_tidy = conos_dt[, .(sample_id, matter, lesion_type, cell_id, type_broad, type_fine)]ancom_ls = ancom_fs %>% lapply(function(f) fread(f) %>%
.calc_boots_sum(min_effect = 0.1, q_cut = 0.05, signif_cut = 0.8)) %>%
setNames(names(ancom_fs))Processing / calculations
# make subgraphs for each specification
set.seed(seed)
calc_subgraphs(conos_tidy, subset_list, graph_f, subg_pat, n_sample = 1e5)already done!NULLcalc_subgraphs(conos_tidy, lesion_list, graph_f, subg_pat, n_sample = 1e5)already done!NULLcalc_subgraphs(conos_tidy, 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 = n_cores)
bpstop()
bpstart()
ignore_me = 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_tidy)),
subg_f, group_var = 'type_fine')
}, BPPARAM = bpparam)
bpstop()
}
# run on each lesion type
if (!check_all_done(lesion_list, save_dir, date_tag)) {
n_lesions = length(lesion_list)
bpparam = MulticoreParam(workers = min(n_lesions, n_cores))
bpstop()
bpstart()
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_tidy)),
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))
bpstop()
bpstart()
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_tidy)),
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 (nn in names(subset_list)) {
cat('#### ', nn, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, nn, date_tag, 'all',
conos_dt, subset_list[[nn]], labels_dt, xy_ref_n = nn))})
cat('\n\n')
}all
healthy_WM
MS_WM
healthy_GM
MS_GM
Split by lesion type
for (nn in names(lesion_list)) {
cat('#### ', nn, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, nn, date_tag, 'all',
conos_dt, lesion_list[[nn]], labels_dt, xy_ref_n = 'all'))})
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 (nn in names(subset_list)) {
cat('#### ', nn, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, nn, date_tag, 'olg',
conos_dt, subset_list[[nn]], labels_dt, xy_ref_n = nn))})
cat('\n\n')
}all
healthy_WM
MS_WM
healthy_GM
MS_GM
Split by lesion type
for (nn in names(lesion_list)) {
cat('#### ', nn, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, nn, date_tag, 'olg',
conos_dt, lesion_list[[nn]], labels_dt, xy_ref_n = 'all'))})
cat('\n\n')
}lesion_WM
lesion_NAWM
lesion_AL
lesion_CAL
lesion_CIL
lesion_RL
lesion_GM
lesion_NAGM
lesion_GML
Split by lesion type, annotated by ANCOM coefficients
for (nn in names(lesion_list)) {
cat('#### ', nn, '\n')
ctrl_sample = xy_ref_list[[nn]] %>% str_match("(lesion|healthy)_(.+)") %>% .[3]
ancom_dt = ancom_ls[[ ctrl_sample ]]
suppressWarnings({print(plot_paga_w_ancom(save_dir, nn, date_tag, 'olg',
conos_dt, lesion_list[[nn]], labels_dt, xy_ref_n = "all",
ancom_dt, max_fc = 8))})
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')Outputs
devtools::session_info()- 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 2022-01-05
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segmented 1.3-4 2021-04-22 [1]
sessioninfo 1.1.1 2018-11-05 [1]
shape 1.4.6 2021-05-19 [1]
SingleCellExperiment * 1.12.0 2020-10-27 [1]
snakecase 0.11.0 2019-05-25 [1]
stringi 1.7.4 2021-08-25 [1]
stringr * 1.4.0 2019-02-10 [2]
SummarizedExperiment * 1.20.0 2020-10-27 [1]
survival 3.2-13 2021-08-24 [2]
testthat 3.1.0 2021-10-04 [2]
tibble 3.1.5 2021-09-30 [1]
tidyr 1.1.4 2021-09-27 [2]
tidyselect 1.1.1 2021-04-30 [2]
usethis 2.1.2 2021-10-25 [1]
utf8 1.2.2 2021-07-24 [1]
uwot 0.1.10 2020-12-15 [2]
vctrs 0.3.8 2021-04-29 [2]
vegan 2.5-7 2020-11-28 [1]
vipor 0.4.5 2017-03-22 [1]
viridis * 0.6.2 2021-10-13 [1]
viridisLite * 0.4.0 2021-04-13 [1]
withr 2.4.2 2021-04-18 [2]
workflowr * 1.6.2 2020-04-30 [1]
xfun 0.27 2021-10-18 [1]
XVector 0.30.0 2020-10-27 [1]
yaml 2.2.1 2020-02-01 [2]
zlibbioc 1.36.0 2020-10-27 [1]
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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 4.0.5 (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] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] SampleQC_0.6.1 SingleCellExperiment_1.12.0
[3] SummarizedExperiment_1.20.0 Biobase_2.50.0
[5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[7] IRanges_2.24.1 S4Vectors_0.28.1
[9] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
[11] matrixStats_0.61.0 Matrix_1.3-4
[13] loomR_0.2.0 itertools_0.1-3
[15] iterators_1.0.13 hdf5r_1.3.3
[17] R6_2.5.1 BiocParallel_1.24.1
[19] ComplexHeatmap_2.6.2 ggbeeswarm_0.6.0
[21] ggrepel_0.9.1 reticulate_1.22
[23] MASS_7.3-54 phyloseq_1.34.0
[25] ANCOMBC_1.0.5 ica_1.0-2
[27] purrr_0.3.4 patchwork_1.1.1
[29] readxl_1.3.1 forcats_0.5.1
[31] ggplot2_3.3.5 scales_1.1.1
[33] viridis_0.6.2 viridisLite_0.4.0
[35] assertthat_0.2.1 stringr_1.4.0
[37] data.table_1.14.2 magrittr_2.0.1
[39] circlize_0.4.13 RColorBrewer_1.1-2
[41] BiocStyle_2.18.1 colorout_1.2-2
[43] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.2 R.utils_2.11.0 tidyselect_1.1.1
[4] Rtsne_0.15 devtools_2.4.2 munsell_0.5.0
[7] codetools_0.2-18 withr_2.4.2 colorspace_2.0-2
[10] highr_0.9 knitr_1.36 Rdpack_2.1.2
[13] labeling_0.4.2 git2r_0.28.0 GenomeInfoDbData_1.2.4
[16] bit64_4.0.5 farver_2.1.0 rhdf5_2.34.0
[19] rprojroot_2.0.2 vctrs_0.3.8 generics_0.1.1
[22] xfun_0.27 clue_0.3-60 bitops_1.0-7
[25] rhdf5filters_1.2.1 microbiome_1.12.0 cachem_1.0.6
[28] DelayedArray_0.16.3 promises_1.2.0.1 beeswarm_0.4.0
[31] gtable_0.3.0 Cairo_1.5-12.2 processx_3.5.2
[34] rlang_0.4.12 GlobalOptions_0.1.2 splines_4.0.5
[37] BiocManager_1.30.16 yaml_2.2.1 reshape2_1.4.4
[40] httpuv_1.6.3 tools_4.0.5 usethis_2.1.2
[43] ellipsis_0.3.2 jquerylib_0.1.4 biomformat_1.18.0
[46] sessioninfo_1.1.1 Rcpp_1.0.7 plyr_1.8.6
[49] zlibbioc_1.36.0 RCurl_1.98-1.5 ps_1.6.0
[52] prettyunits_1.1.1 GetoptLong_1.0.5 cluster_2.1.2
[55] fs_1.5.0 here_1.0.1 mvnfast_0.2.7
[58] mvtnorm_1.1-3 pkgload_1.2.3 evaluate_0.14
[61] mclust_5.4.7 gridExtra_2.3 shape_1.4.6
[64] testthat_3.1.0 compiler_4.0.5 tibble_3.1.5
[67] crayon_1.4.1 R.oo_1.24.0 htmltools_0.5.2
[70] segmented_1.3-4 mgcv_1.8-38 later_1.3.0
[73] tidyr_1.1.4 lubridate_1.8.0 DBI_1.1.1
[76] rappdirs_0.3.3 ade4_1.7-18 permute_0.9-5
[79] cli_3.0.1 R.methodsS3_1.8.1 rbibutils_2.2.4
[82] igraph_1.2.7 pkgconfig_2.0.3 foreach_1.5.1
[85] vipor_0.4.5 bslib_0.3.1 multtest_2.46.0
[88] XVector_0.30.0 snakecase_0.11.0 callr_3.7.0
[91] digest_0.6.28 vegan_2.5-7 janitor_2.1.0
[94] Biostrings_2.58.0 rmarkdown_2.11 cellranger_1.1.0
[97] uwot_0.1.10 kernlab_0.9-29 gtools_3.9.2
[100] rjson_0.2.20 nloptr_1.2.2.2 lifecycle_1.0.1
[103] nlme_3.1-153 jsonlite_1.7.2 Rhdf5lib_1.12.1
[106] desc_1.4.0 fansi_0.5.0 pillar_1.6.4
[109] lattice_0.20-45 fastmap_1.1.0 pkgbuild_1.2.0
[112] survival_3.2-13 glue_1.4.2 remotes_2.4.1
[115] png_0.1-7 bit_4.0.4 stringi_1.7.4
[118] sass_0.4.0 mixtools_1.2.0 memoise_2.0.0
[121] dplyr_1.0.7 ape_5.5