Last updated: 2021-12-08
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Knit directory: MS_lesions/
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Rmd | aa64e51 | wmacnair | 2021-12-06 | Add comparison of abundances with Rowitch clusters |
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source('code/ms00_utils.R')
source('code/ms04_conos.R')
source('code/ms07_soup.R')
source('code/ms09_ancombc_mixed.R')
source('code/supp09_ancombc_rowitch.R')
# 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"
# define pseudobulk data
soup_dir = 'output/ms07_soup'
pb_broad_f = file.path(soup_dir, 'pb_sum_broad_2021-10-11.rds')
pb_fine_f = file.path(soup_dir, 'pb_sum_fine_2021-10-11.rds')
pb_f_ls = c(broad = pb_broad_f, fine = pb_fine_f)
rowitch_f = "data/rowitch/cross_study_neuron_labelling_2021-12-03.xlsx"
# where to save?
save_dir = 'output/ms09_ancombc'
date_tag = '2021-12-07'
if (!dir.exists(save_dir))
dir.create(save_dir)
# sample variables
sample_vars = c('sample_id', 'matter', 'lesion_type',
'neuro_ok', 'neuro_prop', 'sample_source', 'subject_id',
'sex', 'age_scale', 'pmi_cat', 'pmi_cat2')
# identifying strange samples
neuro_mad_cut = 2
log_n_mad_cut = 3
# define how to select PCs
cut_var_exp = 0.01
cut_layer_cor = 0.2
# define how to select PCs
gm_pc_spec = list(
name_str = 'lesions_GM_',
subset = list(matter = 'GM', neuro_ok = TRUE),
size = list(min_count = 10, min_prop = 0.1),
exc_regex = NULL,
formula_pat = '~ lesion_type + %s + sex + age_scale + pmi_cat2',
fixef_test = 'lesion_type',
fixef_covar = c('sex', 'age_scale', 'pmi_cat2'),
ranef_var = 'subject_id',
broad_sel = c("Excitatory neurons", "Inhibitory neurons"),
lesion_ctrl = "GM",
n_pcs = NA
)
sel_pcs = 4
ancom_pat = file.path(save_dir, 'ancombc_rowitch_standard_%s_%s.rds')
boots_pat = file.path(save_dir, 'ancombc_rowitch_bootstrap_%s_%s.txt.gz')
# bootstrapping parameters
n_boots = 1e4
n_cores = 16
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)
# load other groupings
rowitch_dt = rowitch_f %>% read_excel(sheet = "Sheet1") %>%
as.data.table %>%
.[, .(type_broad, type_fine,
rowitch_guess = factor(rowitch_guess),
anna_group = factor(anna_protein_group)
)]
# 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:")
these samples excluded to outlier sample sizes:
print(size_chks[ size_ok == FALSE ])
matter sample_id N med_log_N mad_log_N size_ok
1: GM EU034 911 8.519391 0.423111 FALSE
2: GM EU043 687 8.519391 0.423111 FALSE
# exclude them from conos
ok_samples = size_chks[ size_ok == TRUE ]$sample_id
conos_dt = conos_dt[ (sample_id %in% ok_samples) & (neuro_ok == TRUE) ]
# calc neuronal proportions
props_neu = conos_dt %>%
.[ (type_broad %in% gm_pc_spec$broad_sel) ] %>%
calc_props_dt(sample_vars)
# calc PCAs
ctrl_pcs_dt = props_neu %>%
.[ lesion_type == gm_pc_spec$lesion_ctrl ] %>%
calc_ctrl_pcs_dt(layers_dt)
props_dt = calc_props_dt(conos_dt, sample_vars)
wide_dt = calc_counts_wide(props_dt, sample_vars)
# apply pcs
all_pcs_dt = apply_ctrl_pcs(props_neu[ matter == "GM" ], ctrl_pcs_dt,
cut_var_exp, cut_layer_cor)
wide_neu = merge(all_pcs_dt, wide_dt, 'sample_id')
pc_vars = str_subset(names(wide_neu), "ctrl_PC")
layer_spec = make_layer_pc_spec(gm_pc_spec, pc_vars, n_pcs = sel_pcs)
# make wide things with alternative groupings
wide_rowitch = conos_dt %>%
add_rowitch_labels(rowitch_dt[, .(type_fine, new_group = rowitch_guess)],
sample_vars, all_pcs_dt)
wide_anna = conos_dt %>%
add_rowitch_labels(rowitch_dt[, .(type_fine, new_group = anna_group)],
sample_vars, all_pcs_dt)
# put into nice list
wide_ls = list(
type_fine = wide_neu,
rowitch_guess = wide_rowitch,
anna_group = wide_anna
)
# loop through specified models
for (nn in names(wide_ls)) {
# make file
ancom_f = sprintf(ancom_pat, nn, date_tag)
# if necessary, run thing
if (!file.exists(ancom_f)) {
# do bootstrapping, save results
message('running standard ANCOM-BC for ', nn)
ancom_neu = calc_ancom_standard(wide_ls[[ nn ]], c(sample_vars, pc_vars),
layer_spec$subset, layer_spec$size, layer_spec$exc_regex,
layer_spec$inc_regex, layer_spec$ref_type,
layer_spec$fixef_test, layer_spec$fixef_covar)
saveRDS(ancom_neu, file = ancom_f)
}
}
for (nn in names(wide_ls)) {
# make file
boots_f = sprintf(boots_pat, nn, date_tag)
# if necessary, run thing
if (file.exists(boots_f)) {
message('bootstrapped ANCOM-BC for ', nn, ' already done')
} else {
# do bootstrapping, save resulst
message('running bootstrapped ANCOM-BC for ', nn)
t_start = Sys.time()
boots_neu = calc_ancom_bootstrap(wide_ls[[ nn ]], c(sample_vars, pc_vars),
layer_spec$subset, layer_spec$size, layer_spec$exc_regex,
layer_spec$inc_regex, layer_spec$ref_type,
layer_spec$fixef_test, layer_spec$fixef_covar, layer_spec$ranef_var,
seed = 1, n_boots, n_cores)
t_stop = Sys.time()
fwrite(boots_neu, file = boots_f)
# report how long it took
t_elapsed = difftime(t_stop, t_start, units = 'mins') %>% unclass
message(sprintf(
paste0(' (bootstrapping %d boots with %d cores took %.1f minutes;',
' %.1f boots / min / core)'),
n_boots, n_cores, t_elapsed, n_boots / t_elapsed / n_cores))
}
}
bootstrapped ANCOM-BC for type_fine already done
bootstrapped ANCOM-BC for rowitch_guess already done
bootstrapped ANCOM-BC for anna_group already done
# load std
ancom_ls = lapply(names(wide_ls), function(nn) {
# make file
ancom_obj = sprintf(ancom_pat, nn, date_tag) %>%
readRDS
return(ancom_obj)
}) %>% setNames(names(wide_ls))
# load boots
boots_ls = lapply(names(wide_ls), function(nn) {
# make file
boots_dt = sprintf(boots_pat, nn, date_tag) %>% fread
return(boots_dt)
}) %>% setNames(names(wide_ls))
conos_gm = conos_dt[ matter == "GM" ] %>%
.[, lesion_type := fct_drop(lesion_type) ]
sel_broad = c("Excitatory neurons", "Inhibitory neurons")
for (v in c('type_fine', 'rowitch_guess', 'anna_group')) {
cat('### ', v, '\n')
print(plot_alternative_celltypes(conos_gm, sel_broad, group_var = v))
cat('\n\n')
}
for (nn in names(ancom_ls)) {
cat('### ', nn, '\n')
print(plot_ancombc_ci(ancom_ls[[nn]],
coef_filter = "lesion_type", q_cut = 0.05))
cat('\n\n')
}
for (nn in names(boots_ls)) {
cat('### ', nn, '\n')
print(plot_boots_dt(boots_ls[[nn]],
coef_filter = "lesion_type", min_effect = 0.2))
cat('\n\n')
}
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 2021-12-08
- Packages -------------------------------------------------------------------
! package * version date lib
abind 1.4-5 2016-07-21 [2]
ade4 1.7-18 2021-09-16 [1]
ANCOMBC * 1.0.5 2021-03-09 [1]
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backports 1.2.1 2020-12-09 [2]
beachmat 2.6.4 2020-12-20 [1]
beeswarm 0.4.0 2021-06-01 [1]
Biobase * 2.50.0 2020-10-27 [1]
BiocGenerics * 0.36.1 2021-04-16 [1]
BiocManager 1.30.16 2021-06-15 [1]
BiocNeighbors 1.8.2 2020-12-07 [1]
BiocParallel * 1.24.1 2020-11-06 [1]
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biomformat 1.18.0 2020-10-27 [1]
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bit 4.0.4 2020-08-04 [2]
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reshape2 1.4.4 2020-04-09 [2]
reticulate * 1.22 2021-09-17 [2]
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scattermore 0.7 2020-11-24 [2]
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scran * 1.18.7 2021-04-16 [1]
sctransform 0.3.2 2020-12-16 [2]
scuttle 1.0.4 2020-12-17 [1]
seriation * 1.3.1 2021-10-16 [1]
sessioninfo 1.1.1 2018-11-05 [1]
Seurat * 4.0.5 2021-10-17 [2]
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shape 1.4.6 2021-05-19 [1]
shiny 1.7.1 2021-10-02 [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 2.3-0 2021-07-16 [2]
spatstat.data 2.1-0 2021-03-21 [2]
spatstat.geom 2.3-0 2021-10-09 [2]
spatstat.sparse 2.0-0 2021-03-16 [2]
spatstat.utils 2.2-0 2021-06-14 [2]
statmod 1.4.36 2021-05-10 [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]
tensor 1.5 2012-05-05 [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]
TMB 1.7.22 2021-09-28 [1]
TSP 1.1-11 2021-10-06 [1]
usethis 2.1.2 2021-10-25 [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]
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]
whisker 0.4 2019-08-28 [1]
withr 2.4.2 2021-04-18 [2]
workflowr * 1.6.2 2020-04-30 [1]
xfun 0.27 2021-10-18 [1]
XML 3.99-0.8 2021-09-17 [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] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] ggbeeswarm_0.6.0 ggrepel_0.9.1
[3] reticulate_1.22 MASS_7.3-54
[5] phyloseq_1.34.0 ANCOMBC_1.0.5
[7] ica_1.0-2 purrr_0.3.4
[9] nnls_1.4 muscat_1.5.1
[11] DropletUtils_1.10.3 edgeR_3.32.1
[13] limma_3.46.0 googlesheets_0.3.0
[15] scran_1.18.7 uwot_0.1.10
[17] scater_1.18.6 SingleCellExperiment_1.12.0
[19] SummarizedExperiment_1.20.0 Biobase_2.50.0
[21] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[23] IRanges_2.24.1 S4Vectors_0.28.1
[25] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
[27] matrixStats_0.61.0 BiocParallel_1.24.1
[29] ggplot.multistats_1.0.0 patchwork_1.1.1
[31] seriation_1.3.1 ComplexHeatmap_2.6.2
[33] SeuratObject_4.0.2 Seurat_4.0.5
[35] conos_1.4.3 igraph_1.2.7
[37] Matrix_1.3-4 readxl_1.3.1
[39] forcats_0.5.1 ggplot2_3.3.5
[41] scales_1.1.1 viridis_0.6.2
[43] viridisLite_0.4.0 assertthat_0.2.1
[45] stringr_1.4.0 data.table_1.14.2
[47] magrittr_2.0.1 circlize_0.4.13
[49] RColorBrewer_1.1-2 BiocStyle_2.18.1
[51] colorout_1.2-2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ps_1.6.0
[3] foreach_1.5.1 lmtest_0.9-38
[5] rprojroot_2.0.2 crayon_1.4.1
[7] spatstat.core_2.3-0 rbibutils_2.2.4
[9] rhdf5filters_1.2.1 Matrix.utils_0.9.8
[11] nlme_3.1-153 backports_1.2.1
[13] rlang_0.4.12 XVector_0.30.0
[15] ROCR_1.0-11 microbiome_1.12.0
[17] irlba_2.3.3 callr_3.7.0
[19] nloptr_1.2.2.2 rjson_0.2.20
[21] bit64_4.0.5 glue_1.4.2
[23] sctransform_0.3.2 processx_3.5.2
[25] pbkrtest_0.5.1 vipor_0.4.5
[27] spatstat.sparse_2.0-0 AnnotationDbi_1.52.0
[29] spatstat.geom_2.3-0 tidyselect_1.1.1
[31] usethis_2.1.2 fitdistrplus_1.1-6
[33] variancePartition_1.20.0 XML_3.99-0.8
[35] tidyr_1.1.4 zoo_1.8-9
[37] xtable_1.8-4 evaluate_0.14
[39] cli_3.0.1 Rdpack_2.1.2
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[43] miniUI_0.1.1.1 whisker_0.4
[45] bslib_0.3.1 rpart_4.1-15
[47] shiny_1.7.1 BiocSingular_1.6.0
[49] xfun_0.27 clue_0.3-60
[51] pkgbuild_1.2.0 multtest_2.46.0
[53] cluster_2.1.2 caTools_1.18.2
[55] TSP_1.1-11 biomformat_1.18.0
[57] tibble_3.1.5 ape_5.5
[59] listenv_0.8.0 Biostrings_2.58.0
[61] png_0.1-7 permute_0.9-5
[63] future_1.22.1 withr_2.4.2
[65] bitops_1.0-7 plyr_1.8.6
[67] cellranger_1.1.0 dqrng_0.3.0
[69] pillar_1.6.4 gplots_3.1.1
[71] GlobalOptions_0.1.2 cachem_1.0.6
[73] fs_1.5.0 GetoptLong_1.0.5
[75] DelayedMatrixStats_1.12.3 vctrs_0.3.8
[77] ellipsis_0.3.2 generics_0.1.1
[79] devtools_2.4.2 tools_4.0.5
[81] beeswarm_0.4.0 munsell_0.5.0
[83] DelayedArray_0.16.3 pkgload_1.2.3
[85] fastmap_1.1.0 compiler_4.0.5
[87] abind_1.4-5 httpuv_1.6.3
[89] sessioninfo_1.1.1 plotly_4.10.0
[91] GenomeInfoDbData_1.2.4 gridExtra_2.3
[93] glmmTMB_1.1.2.3 lattice_0.20-45
[95] deldir_1.0-6 utf8_1.2.2
[97] later_1.3.0 dplyr_1.0.7
[99] jsonlite_1.7.2 pbapply_1.5-0
[101] sparseMatrixStats_1.2.1 genefilter_1.72.1
[103] lazyeval_0.2.2 promises_1.2.0.1
[105] doParallel_1.0.16 R.utils_2.11.0
[107] goftest_1.2-3 spatstat.utils_2.2-0
[109] rmarkdown_2.11 cowplot_1.1.1
[111] blme_1.0-5 statmod_1.4.36
[113] Rtsne_0.15 HDF5Array_1.18.1
[115] survival_3.2-13 numDeriv_2016.8-1.1
[117] yaml_2.2.1 htmltools_0.5.2
[119] memoise_2.0.0 locfit_1.5-9.4
[121] digest_0.6.28 mime_0.12
[123] registry_0.5-1 RSQLite_2.2.8
[125] future.apply_1.8.1 remotes_2.4.1
[127] blob_1.2.2 vegan_2.5-7
[129] R.oo_1.24.0 splines_4.0.5
[131] Rhdf5lib_1.12.1 Cairo_1.5-12.2
[133] RCurl_1.98-1.5 broom_0.7.9
[135] hms_1.1.1 rhdf5_2.34.0
[137] colorspace_2.0-2 BiocManager_1.30.16
[139] shape_1.4.6 sass_0.4.0
[141] Rcpp_1.0.7 RANN_2.6.1
[143] fansi_0.5.0 parallelly_1.28.1
[145] R6_2.5.1 ggridges_0.5.3
[147] lifecycle_1.0.1 bluster_1.0.0
[149] minqa_1.2.4 testthat_3.1.0
[151] leiden_0.3.8 jquerylib_0.1.4
[153] snakecase_0.11.0 desc_1.4.0
[155] RcppAnnoy_0.0.19 iterators_1.0.13
[157] TMB_1.7.22 htmlwidgets_1.5.4
[159] beachmat_2.6.4 polyclip_1.10-0
[161] mgcv_1.8-38 globals_0.14.0
[163] leidenAlg_0.1.1 codetools_0.2-18
[165] lubridate_1.8.0 gtools_3.9.2
[167] prettyunits_1.1.1 R.methodsS3_1.8.1
[169] gtable_0.3.0 DBI_1.1.1
[171] git2r_0.28.0 tensor_1.5
[173] httr_1.4.2 highr_0.9
[175] KernSmooth_2.23-20 stringi_1.7.4
[177] progress_1.2.2 reshape2_1.4.4
[179] farver_2.1.0 annotate_1.68.0
[181] hexbin_1.28.2 colorRamps_2.3
[183] sccore_1.0.0 boot_1.3-28
[185] grr_0.9.5 BiocNeighbors_1.8.2
[187] lme4_1.1-27.1 ade4_1.7-18
[189] geneplotter_1.68.0 scattermore_0.7
[191] DESeq2_1.30.1 bit_4.0.4
[193] spatstat.data_2.1-0 janitor_2.1.0
[195] pkgconfig_2.0.3 lmerTest_3.1-3
[197] knitr_1.36