Cell-level QC with SampleQC
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandApril 28, 2021
Last updated: 2021-04-28
Checks: 5 2
Knit directory: MS_lesions/
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Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms03_SampleQC.R')Inputs
loom_dir = 'data/loom_files'
sce_f = 'data/sce_raw/ms_sce.rds'
dbl_f = 'output/ms02_doublet_id/scDblFinder_outputs.txt'
meta_f = 'data/metadata/metadata_updated_20201127.txt'Outputs
save_dir = 'output/ms03_SampleQC'
date_tag = '2021-02-10'
if (!dir.exists(save_dir))
dir.create(save_dir)
qc_f = file.path(save_dir, 'ms_qc_dt.txt')
cols_f = file.path(save_dir, 'cols_dt.txt')
# hard threshold for min counts
min_feats = 300
min_counts = 500
min_cells = 500
# specifications for SampleQC
# qc_names = c('log_counts', 'log_feats', 'logit_mito', 'splice_ratio')
qc_names = c('log_counts', 'logit_mito', 'splice_ratio')
annots_disc = c('patient_id', 'lesion_type', 'seq_pool', 'source', 'disease_status', 'sex')
annots_cont = c('age', 'post_mortem_m', 'med_splice')
# files to save
sampleqc_f = sprintf('%s/sampleQC_obj_%s.rds', save_dir, date_tag)
proj_name = sprintf('MS_lesions_%s', date_tag)
report_f = sprintf('%s/SampleQC_report_%s.html', save_dir, proj_name)
outliers_f = sprintf('%s/outliers_dt_%s.txt', save_dir, date_tag)
keep_f = sprintf('%s/keep_dt_%s.txt', save_dir, date_tag)
# one sample is a massive outlier
outlier_s = 'EU042'
# we want to exclude cells with high splice ratios
splice_cut = 3Load inputs
qc_dt = make_qc_dt_file(sce_f, qc_f) %>%
.[, .(cell_id,
sample_id = str_match(cell_id, '^((EU|WM)[0-9]{3})')[,2],
log_counts = log10(all_counts),
log_feats = log10(all_feats),
logit_mito = qlogis((mito_counts+1) / (all_counts + 2))
)] %>%
setkey('cell_id')extract_splice_ratios(loom_dir, save_dir)extracting spliced, unspliced dataspliced_dt = get_spliced_dt(save_dir)
assert_that( all(sort(qc_dt$cell_id) == sort(spliced_dt$cell_id)))[1] TRUEqc_dt = merge(qc_dt, spliced_dt[, -c('matter', 'barcode')], by=c('cell_id', 'sample_id'))dbl_dt = dbl_f %>% fread %>%
.[, .(cell_id, sample_id=sample, dbl_class=class)]
assert_that( all(sort(qc_dt$cell_id) == sort(dbl_dt$cell_id)))[1] TRUEcols_dt = get_cols_dt(sce_f, cols_f) %>%
setnames('library_id', 'sample_id')
patients_dt = cols_dt[, .(sample_id, patient_id)] %>% unique %>%
.[, N:=.N, by=patient_id] %>%
.[ N < 3, patient_id := NA ] %>%
.[, N := NULL]
cols_dt = merge(cols_dt[, -'patient_id'], patients_dt, by='sample_id')
assert_that( all(sort(qc_dt$cell_id) == sort(cols_dt$cell_id)))[1] TRUEqc_dt = qc_dt %>% merge(cols_dt, by=c('cell_id', 'sample_id'))Processing / calculations
keep_ids = dbl_dt[ dbl_class == 'singlet' ]$cell_id
qc_dt = qc_dt[ cell_id %in% keep_ids ]qc_dt = qc_dt[(log_counts >= log10(min_counts)) &
(log_feats >= log10(min_feats))]
n_dt = qc_dt[, .N, by=sample_id]
keep_s = n_dt[N >= min_cells]$sample_id
qc_dt = qc_dt[sample_id %in% keep_s]qc_dt[, med_splice := median(splice_ratio), by=sample_id]qc_dt = make_qc_dt(qc_dt, qc_names=qc_names)# define number of components
K_list = c(4, 4, 3, 2, 1, 3, 4, 4, 4)
if (!file.exists(sampleqc_f)) {
# calculate sample-sample distances
set.seed(20210210)
qc_obj = calculate_sample_to_sample_MMDs(qc_dt, qc_names,
annots_disc = annots_disc, annots_cont = annots_cont,
n_cores = 16)
print(table(colData(qc_obj)$group_id))
# fit models
set.seed(20210210)
qc_obj = fit_sampleQC(qc_obj, K_list = K_list)
# save results
saveRDS(qc_obj, file=sampleqc_f)
} else {
# load results
qc_obj = readRDS(sampleqc_f)
}# extract component means
group_ids = levels(colData(qc_obj)$group_id)
beta_ks = group_ids %>%
lapply(function(g) {
beta_k = metadata(qc_obj)$fit_list[[g]]$beta_k
mu_0 = metadata(qc_obj)$fit_list[[g]]$mu_0
beta_k = sweep(beta_k, 2, mu_0, '+')
assert_that( all(abs(colMeans(beta_k) - mu_0) < 1e-10) )
return(beta_k)
})
# get clusters with positive splice ratio
splice_idx = qc_names == 'splice_ratio'
exc_clusters = lapply(beta_ks,
function(beta_k_mat) which(beta_k_mat[, splice_idx] > splice_cut)
) %>% setNames(group_ids)
exc_clusters = exc_clusters[ sapply(exc_clusters, function(x) length(x) > 0) ]
assert_that(all(sapply(exc_clusters, length) == 1))[1] TRUE# get outliers
outliers_dt = get_outliers(qc_obj, exc_clusters=exc_clusters)
outliers_dt[ sample_id == outlier_s, outlier := TRUE ]
# check no samples very small
n_kept_dt = outliers_dt[outlier == FALSE, .N, by=sample_id]
assert_that( all(n_kept_dt$N > min_cells) )[1] TRUEkeep_dt = outliers_dt[outlier == FALSE, .(sample_id, cell_id)]Analysis
(plot_spliced_distns(spliced_dt))
Plot SampleQC model fits and outliers over QC biaxials
group_list = levels(colData(qc_obj)$group_id)
for (g in group_list) {
cat('### ', g, '{.tabset}\n')
# which samples?
samples_g = sort(colData(qc_obj)$sample_id[ colData(qc_obj)$group_id == g ])
for (s in samples_g) {
cat('#### ', s, ' \n')
g_fit = plot_fit_over_biaxials_one_sample(qc_obj, s)
g_out = plot_outliers_one_sample(qc_obj, s, outliers_dt=outliers_dt)
g = g_fit / g_out
print(g)
cat('\n\n')
}
}SG1
EU006
EU013
WM102
WM103
WM104
WM105
WM112
WM113
WM116
WM117
WM119
WM122
WM123
WM130
WM132
WM133
WM134
WM135
WM143
WM147
WM153
WM156
WM157
WM158
WM162
WM164
WM168
WM169
WM170
WM175
WM177
WM184
WM190
WM195
WM196
SG2
EU009
EU012
EU014
EU020
EU021
EU026
EU027
EU051
EU052
EU053
EU054
EU057
EU059
EU060
EU061
EU062
EU063
EU065
EU074
WM109
WM124
WM131
WM139
WM151
SG3
EU056
WM107
WM108
WM118
WM120
WM121
WM137
WM152
WM154
WM155
WM160
WM163
WM171
WM179
WM182
WM183
WM185
WM186
WM187
WM188
WM193
WM194
WM199
SG4
EU005
EU008
EU010
EU019
EU023
EU028
EU029
EU030
EU031
EU035
EU036
EU037
EU041
WM128
WM136
WM145
WM166
WM167
WM172
WM191
WM192
SG5
EU015
EU016
EU042
EU043
EU044
WM106
WM129
WM138
WM140
WM141
WM142
WM146
WM159
WM161
WM181
WM197
WM198
SG6
EU018
EU024
EU033
EU038
EU039
EU040
EU045
EU047
EU055
EU068
WM110
WM125
WM127
WM148
WM150
WM173
SG7
EU017
EU022
EU025
EU032
EU046
EU048
EU050
EU058
EU064
EU066
EU067
EU071
EU072
EU077
WM174
SG8
EU007
EU011
EU034
EU069
EU070
EU073
EU075
EU076
EU078
WM101
SG9
WM126
WM149
WM176
WM178
WM180
WM189
QC metrics scatterplots
samples = qc_dt$sample_id %>% unique %>% sort
# qc_names = c('log_counts', 'logit_mito', 'splice_ratio')
qc_names = c('log_counts', 'log_feats', 'logit_mito', 'splice_ratio')
for (s in samples) {
cat('### ', s, '\n')
print(plot_qc_metric_scatter(qc_dt[ sample_id == s ], qc_names))
cat('\n\n')
}EU005
EU006
EU007
EU008
EU009
EU010
EU011
EU012
EU013
EU014
EU015
EU016
EU017
EU018
EU019
EU020
EU021
EU022
EU023
EU024
EU025
EU026
EU027
EU028
EU029
EU030
EU031
EU032
EU033
EU034
EU035
EU036
EU037
EU038
EU039
EU040
EU041
EU042
EU043
EU044
EU045
EU046
EU047
EU048
EU050
EU051
EU052
EU053
EU054
EU055
EU056
EU057
EU058
EU059
EU060
EU061
EU062
EU063
EU064
EU065
EU066
EU067
EU068
EU069
EU070
EU071
EU072
EU073
EU074
EU075
EU076
EU077
EU078
WM101
WM102
WM103
WM104
WM105
WM106
WM107
WM108
WM109
WM110
WM112
WM113
WM116
WM117
WM118
WM119
WM120
WM121
WM122
WM123
WM124
WM125
WM126
WM127
WM128
WM129
WM130
WM131
WM132
WM133
WM134
WM135
WM136
WM137
WM138
WM139
WM140
WM141
WM142
WM143
WM145
WM146
WM147
WM148
WM149
WM150
WM151
WM152
WM153
WM154
WM155
WM156
WM157
WM158
WM159
WM160
WM161
WM162
WM163
WM164
WM166
WM167
WM168
WM169
WM170
WM171
WM172
WM173
WM174
WM175
WM176
WM177
WM178
WM179
WM180
WM181
WM182
WM183
WM184
WM185
WM186
WM187
WM188
WM189
WM190
WM191
WM192
WM193
WM194
WM195
WM196
WM197
WM198
WM199
How many cells and samples retained?
(plot_totals_split_by_meta(pre_dt = dbl_dt, post_dt = keep_dt, meta_f))Warning in melt.data.table(., measure = c("n_cells", "n_samples",
"n_patients"), : 'measure.vars' [n_cells, n_samples, n_patients, ...] are not
all of the same type. By order of hierarchy, the molten data value column will
be of type 'double'. All measure variables not of type 'double' will be coerced
too. Check DETAILS in ?melt.data.table for more on coercion.
message('pre QC:')pre QC:print(calc_summary_dt(dbl_dt, meta_f)) matter condition n_cells n_samples n_patients
1: WM healthy 86997 17 17
2: WM MS 346512 82 33
3: GM healthy 119310 18 17
4: GM MS 397625 56 21message('post QC:')post QC:print(calc_summary_dt(keep_dt, meta_f)) matter condition n_cells n_samples n_patients
1: WM healthy 71624 16 16
2: WM MS 283389 78 33
3: GM healthy 83309 16 15
4: GM MS 313127 56 21Outputs
if (!file.exists(report_f)) {
# render the report
make_SampleQC_report(qc_obj, save_dir, proj_name)
}fwrite(outliers_dt, file=outliers_f)
fwrite(keep_dt, file=keep_f)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 2021-04-28
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CRAN (R 4.0.3)
CRAN (R 4.0.3)
Github (mojaveazure/loomR@df0144b)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
Bioconductor
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.0)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.0)
CRAN (R 4.0.3)
CRAN (R 4.0.0)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.0)
CRAN (R 4.0.3)
CRAN (R 4.0.0)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
Bioconductor
Github (wmacnair/SampleQC@5e3f021)
CRAN (R 4.0.3)
CRAN (R 4.0.0)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.3)
CRAN (R 4.0.0)
Bioconductor
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.0)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
Bioconductor
CRAN (R 4.0.3)
Bioconductor
[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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] SampleQC_0.4.5 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_0.58.0 Matrix_1.3-2
[13] loomR_0.2.0 itertools_0.1-3
[15] iterators_1.0.13 hdf5r_1.3.3
[17] R6_2.5.0 patchwork_1.1.1
[19] forcats_0.5.1 ggplot2_3.3.3
[21] scales_1.1.1 viridis_0.6.0
[23] viridisLite_0.4.0 assertthat_0.2.1
[25] stringr_1.4.0 data.table_1.14.0
[27] magrittr_2.0.1 circlize_0.4.12
[29] RColorBrewer_1.1-2 BiocStyle_2.18.1
[31] colorout_1.2-2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ellipsis_0.3.1 mclust_5.4.7
[4] rprojroot_2.0.2 XVector_0.30.0 GlobalOptions_0.1.2
[7] fs_1.5.0 rstudioapi_0.13 farver_2.1.0
[10] remotes_2.3.0 bit64_4.0.5 fansi_0.4.2
[13] mvtnorm_1.1-1 codetools_0.2-18 splines_4.0.3
[16] cachem_1.0.4 knitr_1.32 pkgload_1.2.1
[19] jsonlite_1.7.2 kernlab_0.9-29 uwot_0.1.10
[22] BiocManager_1.30.12 compiler_4.0.3 fastmap_1.1.0
[25] cli_2.4.0 later_1.1.0.1 htmltools_0.5.1.1
[28] prettyunits_1.1.1 tools_4.0.3 igraph_1.2.6
[31] gtable_0.3.0 glue_1.4.2 GenomeInfoDbData_1.2.4
[34] dplyr_1.0.5 Rcpp_1.0.6 jquerylib_0.1.3
[37] vctrs_0.3.7 xfun_0.22 ps_1.6.0
[40] testthat_3.0.2 lifecycle_1.0.0 gtools_3.8.2
[43] devtools_2.4.0 zlibbioc_1.36.0 MASS_7.3-53.1
[46] promises_1.2.0.1 yaml_2.2.1 mvnfast_0.2.5.1
[49] memoise_2.0.0 gridExtra_2.3 sass_0.3.1
[52] segmented_1.3-3 stringi_1.5.3 highr_0.9
[55] desc_1.3.0 pkgbuild_1.2.0 BiocParallel_1.24.1
[58] shape_1.4.5 rlang_0.4.10 pkgconfig_2.0.3
[61] bitops_1.0-6 evaluate_0.14 lattice_0.20-41
[64] purrr_0.3.4 labeling_0.4.2 bit_4.0.4
[67] tidyselect_1.1.0 processx_3.5.1 generics_0.1.0
[70] DelayedArray_0.16.3 DBI_1.1.1 pillar_1.6.0
[73] whisker_0.4 withr_2.4.2 mixtools_1.2.0
[76] survival_3.2-10 RCurl_1.98-1.3 tibble_3.1.1
[79] crayon_1.4.1 utf8_1.2.1 rmarkdown_2.7
[82] usethis_2.0.1 grid_4.0.3 callr_3.6.0
[85] git2r_0.28.0 digest_0.6.27 httpuv_1.5.5
[88] munsell_0.5.0 bslib_0.2.4 sessioninfo_1.1.1