MS: Identifying contamination by ambient RNA
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandOctober 26, 2021
Last updated: 2021-10-26
Checks: 4 3
Knit directory: MS_lesions/
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- calc_contamination
- calc_contamination_negative_control
- calc_find_markers_soup
- calc_pseudobulk
- calc_soup_profile
- calc_soup_props
- check_contamination_broad
- check_contamination_fine
- load_conos
- load_gene_cats
- load_metadata
- load_soup
- load_top_markers
- plot_dotplot_findmarkers
- plot_soup_max_vs_ctrl
- plot_soup_props_by_cluster
- plot_soup_props_vs_qc_celltype
- plot_soup_props_vs_qc_max
- plot_soup_props_vs_qc_mt
- plot_soup_props_vs_qc_split_by_celltype
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- setup_input
- setup_outputs
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Modified: code/ms07_soup.R
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Modified: code/ms10_muscat_runs.R
Modified: code/ms14_lesions.R
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 13941a1 | Macnair | 2021-10-18 | Update soup and pseudobulk calcs with final metadata |
| html | 13941a1 | Macnair | 2021-10-18 | Update soup and pseudobulk calcs with final metadata |
| Rmd | 15ed138 | Macnair | 2021-10-04 | Update soup analysis with final metadata |
| html | 15ed138 | Macnair | 2021-10-04 | Update soup analysis with final metadata |
| Rmd | 5b8dcb7 | Macnair | 2021-06-03 | Updated soup & pseudobulk calcs with final type_fine |
| html | 5b8dcb7 | Macnair | 2021-06-03 | Updated soup & pseudobulk calcs with final type_fine |
| Rmd | 58205c2 | Macnair | 2021-05-21 | Update with random effects and markers |
| html | 9852840 | Macnair | 2021-05-12 | Adding docs to repo for first time - massive! |
| Rmd | eef8a1c | Macnair | 2021-04-29 | Minor tweaks to allow rerunning on Roche servers |
| Rmd | 0bd2043 | Macnair | 2021-04-19 | Improved soup coding |
| 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/ms03_SampleQC.R')
source('code/ms04_conos.R')
source('code/ms07_soup.R')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_checked_assumptions_2021-10-08.xlsx"
gtf_f = "data/gtf/Homo_sapiens.GRCh38.96.filtered.preMRNA.gtf"
soup_f = "data/ambient/ambient.100UMI.txt"
qc_dir = "output/ms03_SampleQC"
qc_f = file.path(qc_dir, "ms_qc_dt.txt")Outputs
# set up directory
save_dir = 'output/ms07_soup'
date_tag = '2021-10-11'
if (!dir.exists(save_dir))
dir.create(save_dir)
# define markers for distinguishing broad celltypes
fm_broad_f = sprintf("%s/fm_broad_for_soup_%s_all_%s.txt", save_dir, "%s", date_tag)
pval_type = 'all'
tests = c('binom', 't', 'wilcox')
n_cells = 2000
n_cores = 8
# which to use?
top_rank = 10
sel_test = 'binom'
fdr_cut = 1e-6
logfc_cut = 2
# define pseudobulk files
exp_fine_f = sprintf('%s/pb_sum_fine_%s.rds', save_dir, date_tag)
det_fine_f = sprintf('%s/pb_prop_fine_%s.rds', save_dir, date_tag)
exp_broad_f = sprintf('%s/pb_sum_broad_%s.rds', save_dir, date_tag)
det_broad_f = sprintf('%s/pb_prop_broad_%s.rds', save_dir, date_tag)
# define soup files
soup_q_pat = sprintf('%s/soup_quantities_%s_%s_%s.rds', save_dir,
'%s', '%s', date_tag)
soup_broad_f = sprintf('%s/pb_soup_broad_%s_%s.rds', save_dir,
'maximum', date_tag)
soup_fine_f = sprintf('%s/pb_soup_fine_%s_%s.rds', save_dir,
'maximum', date_tag)
soup_broad_mito_f = sprintf('%s/pb_soup_broad_%s_%s.rds', save_dir,
'mito', date_tag)
soup_fine_mito_f = sprintf('%s/pb_soup_fine_%s_%s.rds', save_dir,
'mito', date_tag)Load inputs
conos_dt = load_labelled_dt(labelled_f, labels_f)
labels_dt = load_names_dt(labels_f)meta_dt = load_meta_dt_from_xls(meta_f, outlier_samples = NULL)
qc_stats = calc_qc_stats(qc_dir, qc_f, conos_dt)gtf_dt = load_gtf_dt(gtf_f)# do pseudobulking
pb_broad = make_pb_object(exp_broad_f, sce_f, meta_dt,
conos_dt[, .(cell_id, sample_id, type_broad)],
cluster_var = 'type_broad', fun = 'sum', n_cores = n_cores)loading pre-saved objectprops_broad = make_pb_object(det_broad_f, sce_f, meta_dt,
conos_dt[, .(cell_id, sample_id, type_broad)],
cluster_var = 'type_broad', fun = 'prop.detected', n_cores = n_cores)loading pre-saved objectpb_fine = make_pb_object(exp_fine_f, sce_f, meta_dt,
conos_dt[, .(cell_id, sample_id, type_fine)],
cluster_var = 'type_fine', fun = 'sum', n_cores = n_cores)loading pre-saved objectprops_fine = make_pb_object(det_fine_f, sce_f, meta_dt,
conos_dt[, .(cell_id, sample_id, type_fine)],
cluster_var = 'type_fine', fun = 'prop.detected', n_cores = n_cores)loading pre-saved objectsoup_dt = soup_f %>% fread %>%
.[, gene_id := paste0(symbol, '_', ensembl)] %>% setcolorder('gene_id')
assert_that(all(rownames(pb_fine) == soup_dt$gene_id))[1] TRUEassert_that(all(colnames(pb_fine) %in% names(soup_dt)[-seq.int(3)]))[1] TRUEProcessing / calculations
Calc find markers for broad celltypes
set.seed(20211011)
fm_fs = sapply(tests, function(t) sprintf(fm_broad_f, t))
calc_find_markers_soup(sce_f, fm_fs, tests, pval_type, conos_dt, n_cells, n_cores)already done! skippingNULL# define list to keep
exc_str = '(pseudogene|antisense|lincRNA)'
proper_gs = gtf_dt[!str_detect(gene_biotype, exc_str)]$symbol
top_fms = lapply(tests,
function(t) find_top_markers(fm_broad_f, t, proper_gs, top_rank)) %>%
setNames(tests)
# define list for
marker_list = calc_broad_marker_list(fm_broad_f, sel_test, proper_gs,
fdr_cut = fdr_cut, val_cut = logfc_cut)Estimate contamination
prof_fine = calc_soup_profile(pb_fine, soup_dt)
prof_broad = calc_soup_profile(pb_broad, soup_dt)# make list of objects
pb_list = list(type_fine = pb_fine, type_broad = pb_broad)
prof_list = list(prof_fine, prof_broad) %>% setNames(names(pb_list))
for (t in names(pb_list)) {
# which pseudobulk object?
this_pb = pb_list[[t]]
this_prof = prof_list[[t]]
# estimate maximum soup
soup_q_f = sprintf(soup_q_pat, t, 'maximum')
soup_max = calc_soup_quantities(soup_q_f, this_pb, this_prof,
soup_method = 'maximum', n_cores = n_cores, n_iters = 5, n_points = 10)
# estimate soup by broad markeers
soup_q_f = sprintf(soup_q_pat, t, 'control')
soup_ctrl = calc_soup_quantities(soup_q_f, this_pb, this_prof,
soup_method = 'control', marker_list = marker_list, n_cores = n_cores)
# estimate mito soup
soup_q_f = sprintf(soup_q_pat, t, 'mito')
soup_mt = calc_soup_quantities(soup_q_f, this_pb, this_prof,
soup_method = 'mito', n_cores = n_cores, n_iters = 5, n_points = 10)
}already done!
already done!
already done!
already done!
already done!
already done!# mito soup calculations
soup_mt = sprintf(soup_q_pat, 'type_broad', 'mito') %>% readRDS
pb_soup = calc_pb_soup(pb_broad, soup_mt, prof_broad)
mean_mt = calc_contaminated_genes(pb_soup)
# print some outputs
message('total non-contaminated genes by broad celltype, mito calc:')total non-contaminated genes by broad celltype, mito calc:colSums(mean_mt <= 0.1, na.rm = TRUE) %>% sort %>% round(2) %>% printExcitatory neurons Inhibitory neurons Oligodendrocytes Microglia
113 1396 1643 7276
Endothelial cells Astrocytes Immune Pericytes
8111 9036 10464 11290
OPCs / COPs
16222 message('proportion non-contaminated genes by broad celltype, mito calc:')proportion non-contaminated genes by broad celltype, mito calc:colMeans(mean_mt <= 0.1, na.rm = TRUE) %>% sort %>% round(2) %>% printExcitatory neurons Inhibitory neurons Oligodendrocytes Microglia
0.00 0.05 0.05 0.25
Endothelial cells Astrocytes Immune Pericytes
0.29 0.30 0.41 0.43
OPCs / COPs
0.56 # "maximum" soup calculations
soup_max = sprintf(soup_q_pat, 'type_broad', 'maximum') %>% readRDS
pb_soup = calc_pb_soup(pb_broad, soup_max, prof_broad)
mean_cont = calc_contaminated_genes(pb_soup)
# print some outputs
message('total non-contaminated genes by broad celltype:')total non-contaminated genes by broad celltype:colSums(mean_cont <= 0.1, na.rm = TRUE) %>% sort %>% round(2) %>% print Immune Pericytes Microglia Endothelial cells
17349 19561 21843 22469
OPCs / COPs Oligodendrocytes Astrocytes Inhibitory neurons
23424 24556 25161 26931
Excitatory neurons
28811 message('proportion non-contaminated genes by broad celltype:')proportion non-contaminated genes by broad celltype:colMeans(mean_cont <= 0.1, na.rm = TRUE) %>% sort %>% round(2) %>% print Immune Microglia Pericytes Endothelial cells
0.68 0.74 0.74 0.79
Oligodendrocytes OPCs / COPs Astrocytes Inhibitory neurons
0.80 0.81 0.83 0.89
Excitatory neurons
0.92 # mito soup calculations
soup_mt = sprintf(soup_q_pat, 'type_fine', 'mito') %>% readRDS
pb_soup = calc_pb_soup(pb_fine, soup_mt, prof_fine)
mean_mt = calc_contaminated_genes(pb_soup)
# print some outputs
message('total non-contaminated genes by fine celltype, mito calc:')total non-contaminated genes by fine celltype, mito calc:colSums(mean_mt <= 0.1, na.rm = TRUE) %>% sort %>% round(2) %>% print Ex_RORB_CUX2_A Oligo_A2 Ex_RORB_A Ex_CUX2_B
1620 1700 1813 2692
Ex_RORB_CUX2_B Inh_Pvalb_A Ex_THEMIS_A Neuro_oligo
3449 3917 4005 4290
Ex_TLE4_A Ex_RORB_CUX2_C Ex_RORB_CUX2_D Microglia_B
4667 5443 5568 5709
Ex_RORB_THEMIS_B Ex_THEMIS_B Oligo_B3 Microglia_A
5755 6546 7390 7640
Ex_RORB_B Ex_RORB_CUX2_F Ex_RORB_THEMIS_A Astro_A
7707 8073 8097 8423
Endo_B Oligo_B4 Ex_CUX2_A Inh_Pvalb_B
8484 9050 9068 9070
Oligo_D Inh_SST_B Inh_SST_A Ex_TLE4_B
9147 9248 9551 9568
Endo_A T_cells Inh_VIP Ex_RORB_C
9734 10044 10180 10208
Oligo_C1 Oligo_A1 Astro_C Inh_LAMP5
10236 10241 10247 10291
Astro_D B_cells Ex_RORB_CUX2_E Inh_RELN
10445 10632 10715 10734
Astro_E Pericytes COP_A1 Oligo_B1
11190 11290 12363 12475
COP_B Oligo_B2 Oligo_C2 COP_A2
13261 14858 15659 16353
Astro_B OPC
16871 19874 message('proportion non-contaminated genes by fine celltype, mito calc:')proportion non-contaminated genes by fine celltype, mito calc:colMeans(mean_mt <= 0.1, na.rm = TRUE) %>% sort %>% round(2) %>% print Ex_RORB_CUX2_A Oligo_A2 Ex_RORB_A Ex_CUX2_B
0.06 0.06 0.06 0.09
Ex_RORB_CUX2_B Inh_Pvalb_A Ex_THEMIS_A Ex_TLE4_A
0.12 0.14 0.14 0.16
Neuro_oligo Ex_RORB_CUX2_C Ex_RORB_CUX2_D Ex_RORB_THEMIS_B
0.17 0.19 0.20 0.20
Ex_THEMIS_B Oligo_B3 Microglia_A Ex_RORB_B
0.24 0.26 0.26 0.27
Microglia_B Astro_A Ex_RORB_THEMIS_A Ex_RORB_CUX2_F
0.27 0.28 0.29 0.30
Oligo_B4 Ex_CUX2_A Endo_B Oligo_D
0.31 0.31 0.32 0.33
Inh_SST_B Inh_SST_A Oligo_C1 Inh_VIP
0.34 0.35 0.35 0.35
Endo_A Ex_TLE4_B Inh_Pvalb_B Ex_RORB_C
0.35 0.36 0.36 0.37
Inh_LAMP5 Astro_C Oligo_A1 Inh_RELN
0.37 0.38 0.39 0.39
Ex_RORB_CUX2_E Astro_D T_cells Pericytes
0.40 0.40 0.42 0.43
Oligo_B1 COP_A1 B_cells Oligo_B2
0.45 0.47 0.48 0.52
Astro_E COP_B Astro_B Oligo_C2
0.52 0.57 0.59 0.69
OPC COP_A2
0.69 0.74 # use max value
soup_max = sprintf(soup_q_pat, 'type_fine', 'maximum') %>% readRDS
pb_soup = calc_pb_soup(pb_fine, soup_max, prof_fine)
mean_cont = calc_contaminated_genes(pb_soup)
# print some outputs
message('total non-contaminated genes by fine celltype:')total non-contaminated genes by fine celltype:colSums(mean_cont <= 0.1, na.rm = TRUE) %>% sort %>% round(2) %>% print Neuro_oligo Microglia_B Oligo_A1 Astro_E
10883 11315 13194 13368
COP_B B_cells COP_A1 Ex_RORB_CUX2_E
14624 14710 15873 15993
COP_A2 Astro_C Oligo_C2 T_cells
16074 16090 16200 16275
Inh_Pvalb_B Ex_RORB_CUX2_F Oligo_D Inh_SST_B
16281 16509 18596 19005
Oligo_A2 Endo_A Inh_SST_A Pericytes
19368 19393 19436 19561
Ex_THEMIS_B Oligo_B1 Inh_RELN Ex_RORB_CUX2_A
20383 20527 21211 21292
Ex_TLE4_B Inh_LAMP5 Microglia_A Ex_RORB_THEMIS_A
21654 21859 21861 21990
Astro_D Oligo_B2 Ex_RORB_C Oligo_B3
22213 22426 22529 22591
Endo_B Ex_RORB_CUX2_D Inh_Pvalb_A Astro_A
22633 23347 23428 23654
Ex_TLE4_A Oligo_B4 Inh_VIP Ex_RORB_THEMIS_B
23786 23829 23907 24099
Ex_RORB_B Astro_B Oligo_C1 Ex_THEMIS_A
24425 24517 24562 24738
OPC Ex_RORB_CUX2_B Ex_CUX2_A Ex_RORB_A
24774 25951 25995 26291
Ex_RORB_CUX2_C Ex_CUX2_B
26354 27222 message('proportion non-contaminated genes by fine celltype:')proportion non-contaminated genes by fine celltype:colMeans(mean_cont <= 0.1, na.rm = TRUE) %>% sort %>% round(2) %>% print Neuro_oligo Oligo_A1 Microglia_B Astro_C
0.44 0.50 0.54 0.60
COP_A1 Ex_RORB_CUX2_E Ex_RORB_CUX2_F Astro_E
0.60 0.60 0.62 0.63
COP_B Inh_Pvalb_B Oligo_D B_cells
0.63 0.65 0.66 0.67
Oligo_A2 T_cells Inh_SST_B Inh_SST_A
0.67 0.67 0.69 0.70
Endo_A Oligo_C2 COP_A2 Ex_RORB_CUX2_A
0.71 0.71 0.73 0.73
Oligo_B1 Ex_THEMIS_B Pericytes Microglia_A
0.74 0.74 0.74 0.74
Inh_RELN Oligo_B2 Astro_A Inh_LAMP5
0.77 0.78 0.79 0.79
Oligo_B3 Ex_RORB_THEMIS_A Ex_RORB_C Ex_TLE4_B
0.79 0.79 0.81 0.81
Oligo_B4 Ex_TLE4_A Inh_Pvalb_A Inh_VIP
0.81 0.82 0.82 0.83
Ex_RORB_CUX2_D Endo_B Oligo_C1 Astro_B
0.84 0.84 0.85 0.85
Ex_RORB_B Ex_THEMIS_A Ex_RORB_THEMIS_B Astro_D
0.85 0.85 0.86 0.86
OPC Ex_CUX2_A Ex_RORB_A Ex_RORB_CUX2_B
0.86 0.89 0.89 0.89
Ex_RORB_CUX2_C Ex_CUX2_B
0.90 0.91 # estimate maximum soup
soup_q_f = sprintf(soup_q_pat, 'type_fine', 'max_random')
set.seed(20211011)
mt_idx = rownames(prof_fine) %>% str_detect('^(MT-|MALAT1)')
assert_that(sum(mt_idx) == 14)[1] TRUEprof_rand = prof_fine %>% apply(2, function(col) {
# make output
out = numeric(length(col))
# fix the mito reads
out[mt_idx] = col[mt_idx]
# permute the rest
out[!mt_idx] = col[!mt_idx] %>% sample(length(.))
return(out)
}) %>% set_rownames(rownames(prof_fine))
soup_rand = calc_soup_quantities(soup_q_f, pb_fine, prof_rand,
soup_method = 'maximum', n_cores = n_cores, n_iters = 5, n_points = 10)already done!# get soup
soup_ctrl = sprintf(soup_q_pat, 'type_fine', 'control') %>% readRDS
soup_max = sprintf(soup_q_pat, 'type_fine', 'maximum') %>% readRDS
soup_mt = sprintf(soup_q_pat, 'type_fine', 'mito') %>% readRDS
soup_rand = sprintf(soup_q_pat, 'type_fine', 'max_random') %>% readRDS
# calc props
props_ctrl = calc_soup_props(pb_fine, soup_ctrl, meta_dt, labels_dt)
props_max = calc_soup_props(pb_fine, soup_max, meta_dt, labels_dt)
props_mt = calc_soup_props(pb_fine, soup_mt, meta_dt, labels_dt)
props_rand = calc_soup_props(pb_fine, soup_rand, meta_dt, labels_dt)Analysis
Dotplots of (calced) marker genes
for (t in tests) {
cat('### Markers via', t, 'test\n')
top_dt = top_fms[[t]]
suppressWarnings(print(plot_dotplot(pb_fine, props_fine, top_dt, labels_dt,
row_split = 'broad_marker')))
cat('\n\n')
}Markers via binom test
Markers via t test
How similar are soup estimates?
plot_soup_max_vs_ctrl(soup_max, soup_ctrl, labels_dt)Warning in sqrt(x): NaNs producedWarning: Removed 1603 rows containing missing values (geom_point).
Distributions of soup proportions by cluster
cat('\n### Maximum soup')Maximum soup
print(plot_soup_contribution_by_cluster(props_max))
cat('\n\n')cat('\n### Celltype-based soup')
Check of (celltype) contamination against QC metrics (oligos only)
b = 'Oligodendrocytes'
for (t in unique(props_max[type_broad == b]$type_fine)) {
cat('### ', t, '\n')
suppressWarnings(print(plot_soup_props_vs_qc(props_ctrl[type_fine == t], qc_stats)))
cat('\n\n')
}Oligo_A1
Oligo_A2
Oligo_B1
Oligo_B2
Oligo_B3
Oligo_B4
Oligo_C1
Oligo_C2
Correlations of “maximum” contamination with QC metrics
for (b in broad_ord) {
cat('### ', b, '{.tabset}\n')
for (t in levels(fct_drop(props_max[type_broad == b]$type_fine))) {
cat('#### ', t, '\n')
suppressWarnings(print(plot_soup_props_vs_qc(props_max[type_fine == t], qc_stats)))
cat('\n\n')
}
}OPCs / COPs
OPC
COP_A1
COP_A2
COP_B
Oligodendrocytes
Oligo_A1
Oligo_A2
Oligo_B1
Oligo_B2
Oligo_B3
Oligo_B4
Oligo_C1
Oligo_C2
Oligo_D
Astrocytes
Astro_A
Astro_B
Astro_C
Astro_D
Astro_E
Microglia
Microglia_A
Microglia_B
Excitatory neurons
Ex_CUX2_A
Ex_CUX2_B
Ex_RORB_A
Ex_RORB_B
Ex_RORB_C
Ex_RORB_CUX2_A
Ex_RORB_CUX2_B
Ex_RORB_CUX2_C
Ex_RORB_CUX2_D
Ex_RORB_CUX2_E
Ex_RORB_CUX2_F
Ex_RORB_THEMIS_A
Ex_RORB_THEMIS_B
Ex_THEMIS_A
Ex_THEMIS_B
Ex_TLE4_A
Ex_TLE4_B
Neuro_oligo
Inhibitory neurons
Inh_LAMP5
Inh_Pvalb_A
Inh_Pvalb_B
Inh_RELN
Inh_SST_A
Inh_SST_B
Inh_VIP
Endothelial cells
Endo_A
Endo_B
Pericytes
Pericytes
Immune
B_cells
T_cells
Correlations of mito contamination with QC metrics
for (b in broad_ord) {
cat('### ', b, '{.tabset}\n')
for (t in levels(fct_drop(props_mt[type_broad == b]$type_fine))) {
cat('#### ', t, '\n')
suppressWarnings(print(plot_soup_props_vs_qc(props_mt[type_fine == t], qc_stats)))
cat('\n\n')
}
}OPCs / COPs
OPC
COP_A1
COP_A2
COP_B
Oligodendrocytes
Oligo_A1
Oligo_A2
Oligo_B1
Oligo_B2
Oligo_B3
Oligo_B4
Oligo_C1
Oligo_C2
Oligo_D
Astrocytes
Astro_A
Astro_B
Astro_C
Astro_D
Astro_E
Microglia
Microglia_A
Microglia_B
Excitatory neurons
Ex_CUX2_A
Ex_CUX2_B
Ex_RORB_A
Ex_RORB_B
Ex_RORB_C
Ex_RORB_CUX2_A
Ex_RORB_CUX2_B
Ex_RORB_CUX2_C
Ex_RORB_CUX2_D
Ex_RORB_CUX2_E
Ex_RORB_CUX2_F
Ex_RORB_THEMIS_A
Ex_RORB_THEMIS_B
Ex_THEMIS_A
Ex_THEMIS_B
Ex_TLE4_A
Ex_TLE4_B
Neuro_oligo
Inhibitory neurons
Inh_LAMP5
Inh_Pvalb_A
Inh_Pvalb_B
Inh_RELN
Inh_SST_A
Inh_SST_B
Inh_VIP
Endothelial cells
Endo_A
Endo_B
Pericytes
Pericytes
Immune
B_cells
T_cells
Correlations of “maximum” contamination with QC metrics (comparison of celltypes)
cat('### Random soup (-ve control)\n')Random soup (-ve control)
print(plot_soup_props_vs_qc_by_celltype(props_rand, qc_stats))
cat('\n\n')cat('### All samples\n')All samples
print(plot_soup_props_vs_qc_by_celltype(props_max, qc_stats))
cat('\n\n')cat('### >= 10 cells in pseudobulk\n')>= 10 cells in pseudobulk
big_cls = qc_stats[qc_var == 'logN' & qc_val >= log10(10), .(sample_id, conos)]
stats_big = merge(qc_stats, big_cls, by = c('sample_id', 'conos'))
print(plot_soup_props_vs_qc_by_celltype(props_max, stats_big))
cat('\n\n')Correlations of mito contamination with QC metrics (comparison of celltypes)
cat('### All samples\n')All samples
print(plot_soup_props_vs_qc_by_celltype(props_mt, qc_stats))
cat('\n\n')cat('### >= 10 cells in pseudobulk\n')>= 10 cells in pseudobulk
big_cls = qc_stats[qc_var == 'logN' & qc_val >= log10(10), .(sample_id, conos)]
stats_big = merge(qc_stats, big_cls, by = c('sample_id', 'conos'))
print(plot_soup_props_vs_qc_by_celltype(props_mt, stats_big))
cat('\n\n')Outputs
# save soup proportions, broad
soup_broad = sprintf(soup_q_pat, 'type_broad', 'maximum') %>% readRDS
pb_soup_broad = calc_pb_soup(pb_broad, soup_broad, prof_broad)
saveRDS(pb_soup_broad, file = soup_broad_f, compress = FALSE)
# save soup proportions, fine
soup_fine = sprintf(soup_q_pat, 'type_fine', 'maximum') %>% readRDS
pb_soup_fine = calc_pb_soup(pb_fine, soup_fine, prof_fine)
saveRDS(pb_soup_fine, file = soup_fine_f, compress = FALSE)
# save soup proportions, broad
soup_broad = sprintf(soup_q_pat, 'type_broad', 'mito') %>% readRDS
pb_soup_broad = calc_pb_soup(pb_broad, soup_broad, prof_broad)
saveRDS(pb_soup_broad, file = soup_broad_mito_f, compress = FALSE)
# save soup proportions, fine
soup_fine = sprintf(soup_q_pat, 'type_fine', 'mito') %>% readRDS
pb_soup_fine = calc_pb_soup(pb_fine, soup_fine, prof_fine)
saveRDS(pb_soup_fine, file = soup_fine_mito_f, compress = FALSE)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 2021-10-26
- Packages -------------------------------------------------------------------
! package * version date lib
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backports 1.2.1 2020-12-09 [2]
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colorout * 1.2-2 2021-04-15 [1]
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source
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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] nnls_1.4 muscat_1.5.1
[3] DropletUtils_1.10.3 edgeR_3.32.1
[5] limma_3.46.0 googlesheets_0.3.0
[7] scran_1.18.7 uwot_0.1.10
[9] scater_1.18.6 BiocParallel_1.24.1
[11] ggplot.multistats_1.0.0 seriation_1.3.1
[13] ComplexHeatmap_2.6.2 SeuratObject_4.0.2
[15] Seurat_4.0.5 conos_1.4.3
[17] igraph_1.2.7 SampleQC_0.6.1
[19] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[21] Biobase_2.50.0 GenomicRanges_1.42.0
[23] GenomeInfoDb_1.26.7 IRanges_2.24.1
[25] S4Vectors_0.28.1 BiocGenerics_0.36.1
[27] MatrixGenerics_1.2.1 matrixStats_0.61.0
[29] Matrix_1.3-4 loomR_0.2.0
[31] itertools_0.1-3 iterators_1.0.13
[33] hdf5r_1.3.3 R6_2.5.1
[35] patchwork_1.1.1 readxl_1.3.1
[37] forcats_0.5.1 ggplot2_3.3.5
[39] scales_1.1.1 viridis_0.6.2
[41] viridisLite_0.4.0 assertthat_0.2.1
[43] stringr_1.4.0 data.table_1.14.2
[45] magrittr_2.0.1 circlize_0.4.13
[47] RColorBrewer_1.1-2 BiocStyle_2.18.1
[49] colorout_1.2-2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-2
[3] ps_1.6.0 Rsamtools_2.6.0
[5] foreach_1.5.1 lmtest_0.9-38
[7] rprojroot_2.0.2 crayon_1.4.1
[9] spatstat.core_2.3-0 MASS_7.3-54
[11] rhdf5filters_1.2.1 Matrix.utils_0.9.8
[13] nlme_3.1-153 backports_1.2.1
[15] rlang_0.4.12 XVector_0.30.0
[17] ROCR_1.0-11 irlba_2.3.3
[19] callr_3.7.0 nloptr_1.2.2.2
[21] rjson_0.2.20 bit64_4.0.5
[23] glue_1.4.2 sctransform_0.3.2
[25] processx_3.5.2 pbkrtest_0.5.1
[27] vipor_0.4.5 spatstat.sparse_2.0-0
[29] AnnotationDbi_1.52.0 spatstat.geom_2.3-0
[31] tidyselect_1.1.1 usethis_2.1.2
[33] fitdistrplus_1.1-6 variancePartition_1.20.0
[35] XML_3.99-0.8 tidyr_1.1.4
[37] zoo_1.8-9 GenomicAlignments_1.26.0
[39] xtable_1.8-4 evaluate_0.14
[41] cli_3.0.1 scuttle_1.0.4
[43] zlibbioc_1.36.0 miniUI_0.1.1.1
[45] whisker_0.4 bslib_0.3.1
[47] rpart_4.1-15 shiny_1.7.1
[49] BiocSingular_1.6.0 xfun_0.27
[51] clue_0.3-60 pkgbuild_1.2.0
[53] cluster_2.1.2 caTools_1.18.2
[55] TSP_1.1-11 tibble_3.1.5
[57] ggrepel_0.9.1 listenv_0.8.0
[59] Biostrings_2.58.0 png_0.1-7
[61] future_1.22.1 withr_2.4.2
[63] bitops_1.0-7 plyr_1.8.6
[65] cellranger_1.1.0 dqrng_0.3.0
[67] pillar_1.6.4 gplots_3.1.1
[69] GlobalOptions_0.1.2 cachem_1.0.6
[71] fs_1.5.0 kernlab_0.9-29
[73] GetoptLong_1.0.5 DelayedMatrixStats_1.12.3
[75] vctrs_0.3.8 ellipsis_0.3.2
[77] generics_0.1.1 devtools_2.4.2
[79] tools_4.0.5 beeswarm_0.4.0
[81] munsell_0.5.0 DelayedArray_0.16.3
[83] pkgload_1.2.3 fastmap_1.1.0
[85] compiler_4.0.5 abind_1.4-5
[87] httpuv_1.6.3 rtracklayer_1.50.0
[89] segmented_1.3-4 sessioninfo_1.1.1
[91] plotly_4.10.0 GenomeInfoDbData_1.2.4
[93] gridExtra_2.3 glmmTMB_1.1.2.3
[95] lattice_0.20-45 deldir_1.0-6
[97] utf8_1.2.2 later_1.3.0
[99] dplyr_1.0.7 jsonlite_1.7.2
[101] pbapply_1.5-0 sparseMatrixStats_1.2.1
[103] genefilter_1.72.1 lazyeval_0.2.2
[105] promises_1.2.0.1 doParallel_1.0.16
[107] R.utils_2.11.0 goftest_1.2-3
[109] spatstat.utils_2.2-0 reticulate_1.22
[111] rmarkdown_2.11 cowplot_1.1.1
[113] blme_1.0-5 statmod_1.4.36
[115] Rtsne_0.15 HDF5Array_1.18.1
[117] survival_3.2-13 numDeriv_2016.8-1.1
[119] yaml_2.2.1 htmltools_0.5.2
[121] memoise_2.0.0 locfit_1.5-9.4
[123] digest_0.6.28 mime_0.12
[125] registry_0.5-1 RSQLite_2.2.8
[127] future.apply_1.8.1 remotes_2.4.1
[129] blob_1.2.2 R.oo_1.24.0
[131] mvnfast_0.2.7 labeling_0.4.2
[133] splines_4.0.5 Rhdf5lib_1.12.1
[135] Cairo_1.5-12.2 mixtools_1.2.0
[137] RCurl_1.98-1.5 broom_0.7.9
[139] hms_1.1.1 rhdf5_2.34.0
[141] colorspace_2.0-2 BiocManager_1.30.16
[143] ggbeeswarm_0.6.0 shape_1.4.6
[145] sass_0.4.0 Rcpp_1.0.7
[147] mclust_5.4.7 RANN_2.6.1
[149] mvtnorm_1.1-3 fansi_0.5.0
[151] parallelly_1.28.1 ggridges_0.5.3
[153] lifecycle_1.0.1 bluster_1.0.0
[155] minqa_1.2.4 testthat_3.1.0
[157] leiden_0.3.8 jquerylib_0.1.4
[159] snakecase_0.11.0 desc_1.4.0
[161] RcppAnnoy_0.0.19 TMB_1.7.22
[163] htmlwidgets_1.5.4 beachmat_2.6.4
[165] polyclip_1.10-0 purrr_0.3.4
[167] mgcv_1.8-38 globals_0.14.0
[169] leidenAlg_0.1.1 codetools_0.2-18
[171] lubridate_1.8.0 gtools_3.9.2
[173] prettyunits_1.1.1 R.methodsS3_1.8.1
[175] gtable_0.3.0 DBI_1.1.1
[177] git2r_0.28.0 tensor_1.5
[179] httr_1.4.2 highr_0.9
[181] KernSmooth_2.23-20 stringi_1.7.4
[183] progress_1.2.2 farver_2.1.0
[185] reshape2_1.4.4 annotate_1.68.0
[187] hexbin_1.28.2 colorRamps_2.3
[189] sccore_1.0.0 boot_1.3-28
[191] grr_0.9.5 BiocNeighbors_1.8.2
[193] lme4_1.1-27.1 geneplotter_1.68.0
[195] scattermore_0.7 DESeq2_1.30.1
[197] bit_4.0.4 spatstat.data_2.1-0
[199] janitor_2.1.0 pkgconfig_2.0.3
[201] lmerTest_3.1-3 knitr_1.36