Checks: ANCOM-BC
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandOctober 22, 2021
Last updated: 2021-10-22
Checks: 4 3
Knit directory: MS_lesions/
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- calc_wm_bootstrap
- load_conos
- load_meta
- plot_ancom_cis_ref_all
- plot_ancom_cis_ref_signif
- plot_logratio_heatmap
- plot_logratio_scatter
- plot_wm_bootstraps
- save_outputs
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6116376 | Macnair | 2021-08-05 | Update ancom analysis with a couple of new runs |
| html | 6116376 | Macnair | 2021-08-05 | Update ancom analysis with a couple of new runs |
| Rmd | 0ec42e1 | Macnair | 2021-06-20 | Update GSEA analysis, heatmaps, lesion-specific |
| html | 0ec42e1 | Macnair | 2021-06-20 | Update GSEA analysis, heatmaps, lesion-specific |
| Rmd | 129c53d | Macnair | 2021-04-16 | Renamed a lot of things to add ms07_soup |
Plan
- exclude patients w high Oligo_D
- redo CLR plots edgeR plan:
- exclude Oligo_D + COP_A2
- calc PCA on patients
- record PC1
- make new pb object: load pb_fine; sum everything except Oligo_D + COP_A2
Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms04_conos.R')
source('code/ms07_soup.R')
source('code/ms09_ancombc.R')
source('code/ms10_muscat_fns.R')
source('code/ms10_muscat_runs.R')
source('code/supp09_ancombc.R')Inputs
# 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_updated_20201127.txt'Outputs
# where to save?
ancom_dir = 'output/ms09_ancombc'
date_tag = '2021-06-15'
if (!dir.exists(ancom_dir))
dir.create(ancom_dir)
# specify celltypes
sel_broad = c('Excitatory neurons', 'Inhibitory neurons')
assert_that(all(sel_broad %in% broad_ord))[1] TRUEchangers = c('Ex_RORB_CUX2_C', 'Ex_RORB_CUX2_F', 'Ex_TLE4_A', "Inh_VIP")
not_changed = c("Ex_CUX2_A", "Ex_RORB_A", "Ex_RORB_CUX2_A", "Ex_RORB_CUX2_B",
"Ex_THEMIS_B", "Ex_TLE4_B", "Inh_Pvalb_B", 'Inh_RELN')
# vars to set things up
sample_vars = c('sample_id', 'matter', 'lesion_type',
'neuro_ok', 'neuro_prop', 'source', 'patient_id',
'sex', 'age_norm', 'pmi_cat')
mad_cut = 2# define models
subset_gm = list(matter = 'GM', neuro_ok = TRUE)
size_spec = list(min_count = 10, min_prop = 0.1)
model_name = 'GM_RELN'
ref_types = c('Inh_RELN', 'Inh_VIP', 'Ex_TLE4_A', 'Ex_CUX2_A')
formulae = sprintf('~ lesion_type + log_%s + sex + age_norm', tolower(ref_types)) %>%
setNames(ref_types)
names_list = sprintf('GM lesions, log(%s) as covariate', ref_types) %>%
setNames(ref_types)
p_cut = 0.05
# define filenames
ancom_pat = sprintf('%s/ancom_obj_%s_%s.rds', ancom_dir, date_tag, '%s')# define models
subset_wm = list(matter = 'WM', neuro_ok = TRUE)
formula_wm = '~ lesion_type + sex + age_norm'
title_wm = 'WM lesions, bootstrapped by patient'
n_boots = 100
n_cores = 8
boots_f = file.path(ancom_dir, 'wm_bootstrapped.txt')Load inputs
labels_dt = load_names_dt(labels_f) %>%
.[, cluster_id := type_fine]
conos_dt = load_labelled_dt(labelled_f, labels_f)meta_dt = load_meta_dt(meta_f)Processing / calculations
conos_dt = merge(conos_dt, meta_dt, by = 'sample_id') %>%
add_neuro_props(mad_cut = mad_cut)props_dt = conos_dt[type_broad %in% sel_broad & matter == 'GM' &
neuro_ok == TRUE] %>%
calc_props_dt(sample_vars)
logratio_dt = calc_logratio_dt(props_dt, changers)counts_wide = conos_dt %>% calc_props_dt(sample_vars) %>%
calc_counts_wide(sample_vars)# calculate ancom for each
ancom_list = lapply(seq_along(ref_types), function(i) {
# make nice name
ref_type = ref_types[[i]]
nice_name = ref_type %>% tolower %>% sprintf('log_%s', .)
# add reln as covariate
reln_dt = calc_props_dt(conos_dt, sample_vars) %>%
.[ type_fine == ref_type, .(sample_id, log_ref = log_p) ] %>%
setnames('log_ref', nice_name)
props_tmp = conos_dt[ type_fine != ref_type ] %>%
calc_props_dt(sample_vars) %>%
merge(reln_dt, by = 'sample_id')
# do ANCOM
s_vars_reln = c(sample_vars, nice_name)
wide_reln = calc_counts_wide(props_tmp, s_vars_reln)
ancom_obj = fit_ancombc_fn(ancom_pat, nice_name, wide_reln, subset_gm,
size_spec = NULL, formulae[[i]], s_vars_reln, seed = 123)
return(ancom_obj)
}) %>% setNames(ref_types)already done!
already done!
already done!running ANCOM-BC for log_ex_cux2_a making phyloseq object running ANCOM-BCif (file.exists(boots_f)) {
boots_dt = boots_f %>% fread
} else {
boots_dt = calc_ancom_bootstrap(counts_wide, subset_wm, sample_vars,
formula_wm, seed = 1, n_boots, n_cores)
fwrite(boots_dt, file = boots_f)
}Analysis
Heatmap of log-ratios relative to possible reference neurons
draw(plot_logratio_heatmap(logratio_dt))
Scatter of log-props relative to possible reference neurons
print(plot_logratio_scatter(logratio_dt, not_changed))
ANCOM-BC on GM using reference neurons as covariate
Significant results only
for (nn in ref_types) {
cat('#### ', nn, '\n')
print(plot_ancombc_ci(ancom_list[[nn]], counts_wide, names_list[[nn]],
whatplot = 'lesion_type', reported_only = TRUE))
cat('\n\n')
}
All celltypes
for (nn in ref_types) {
cat('#### ', nn, '\n')
print(plot_ancombc_ci(ancom_list[[nn]], counts_wide, names_list[[nn]],
whatplot = 'lesion_type', reported_only = FALSE))
cat('\n\n')
}
WM bootstrap results
(plot_boots_dt(boots_dt, title_wm, q_cut = 0.05, signif_cut = 0.8))
| Version | Author | Date |
|---|---|---|
| 0ec42e1 | Macnair | 2021-06-20 |
Outputs
devtools::session_info()Registered S3 method overwritten by 'cli':
method from
print.boxx spatstat.geom- 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 2021-06-17
- Packages -------------------------------------------------------------------
package * version date lib
abind 1.4-5 2016-07-21 [2]
ade4 1.7-16 2020-10-28 [1]
ANCOMBC * 1.0.5 2021-03-09 [1]
ape 5.5 2021-04-25 [1]
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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 2.1-2 2021-04-18 [2]
spatstat.data 2.1-0 2021-03-21 [2]
spatstat.geom 2.1-0 2021-04-15 [2]
spatstat.sparse 2.0-0 2021-03-16 [2]
spatstat.utils 2.1-0 2021-03-15 [2]
statmod 1.4.36 2021-05-10 [1]
stringi 1.6.2 2021-05-17 [2]
stringr * 1.4.0 2019-02-10 [2]
SummarizedExperiment * 1.20.0 2020-10-27 [1]
survival 3.2-11 2021-04-26 [2]
tensor 1.5 2012-05-05 [2]
testthat 3.0.2 2021-02-14 [2]
tibble 3.1.2 2021-05-16 [2]
tidyr 1.1.3 2021-03-03 [2]
tidyselect 1.1.1 2021-04-30 [2]
TSP 1.1-10 2020-04-17 [1]
usethis 2.0.1 2021-02-10 [1]
utf8 1.2.1 2021-03-12 [2]
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 * 0.6.1 2021-05-11 [1]
viridisLite * 0.4.0 2021-04-13 [2]
whisker 0.4 2019-08-28 [1]
withr 2.4.2 2021-04-18 [2]
workflowr * 1.6.2 2020-04-30 [1]
xfun 0.23 2021-05-15 [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
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 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] ggrepel_0.9.1 reticulate_1.20
[3] MASS_7.3-54 phyloseq_1.34.0
[5] ANCOMBC_1.0.5 purrr_0.3.4
[7] scran_1.18.7 uwot_0.1.10
[9] scater_1.18.6 SingleCellExperiment_1.12.0
[11] SummarizedExperiment_1.20.0 Biobase_2.50.0
[13] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[15] IRanges_2.24.1 S4Vectors_0.28.1
[17] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
[19] matrixStats_0.58.0 BiocParallel_1.24.1
[21] ggplot.multistats_1.0.0 patchwork_1.1.1
[23] seriation_1.2-9 ComplexHeatmap_2.6.2
[25] SeuratObject_4.0.1 Seurat_4.0.1
[27] conos_1.4.1 igraph_1.2.6
[29] Matrix_1.3-3 forcats_0.5.1
[31] ggplot2_3.3.3 scales_1.1.1
[33] viridis_0.6.1 viridisLite_0.4.0
[35] assertthat_0.2.1 stringr_1.4.0
[37] data.table_1.14.0 magrittr_2.0.1
[39] circlize_0.4.12 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] scattermore_0.7 R.methodsS3_1.8.1
[3] tidyr_1.1.3 knitr_1.33
[5] R.utils_2.10.1 irlba_2.3.3
[7] DelayedArray_0.16.3 rpart_4.1-15
[9] RCurl_1.98-1.3 generics_0.1.0
[11] callr_3.7.0 cowplot_1.1.1
[13] microbiome_1.12.0 usethis_2.0.1
[15] RANN_2.6.1 future_1.21.0
[17] spatstat.data_2.1-0 lubridate_1.7.10
[19] httpuv_1.6.1 xfun_0.23
[21] hms_1.1.0 jquerylib_0.1.4
[23] evaluate_0.14 promises_1.2.0.1
[25] TSP_1.1-10 fansi_0.4.2
[27] progress_1.2.2 DBI_1.1.1
[29] htmlwidgets_1.5.3 spatstat.geom_2.1-0
[31] ellipsis_0.3.2 dplyr_1.0.6
[33] permute_0.9-5 deldir_0.2-10
[35] sparseMatrixStats_1.2.1 vctrs_0.3.8
[37] remotes_2.3.0 Cairo_1.5-12.2
[39] ROCR_1.0-11 abind_1.4-5
[41] cachem_1.0.5 withr_2.4.2
[43] grr_0.9.5 sctransform_0.3.2
[45] vegan_2.5-7 prettyunits_1.1.1
[47] goftest_1.2-2 cluster_2.1.2
[49] ape_5.5 lazyeval_0.2.2
[51] crayon_1.4.1 edgeR_3.32.1
[53] pkgconfig_2.0.3 pkgload_1.2.1
[55] nlme_3.1-152 vipor_0.4.5
[57] devtools_2.4.1 rlang_0.4.11
[59] globals_0.14.0 lifecycle_1.0.0
[61] miniUI_0.1.1.1 registry_0.5-1
[63] rsvd_1.0.5 rprojroot_2.0.2
[65] polyclip_1.10-0 lmtest_0.9-38
[67] Rhdf5lib_1.12.1 zoo_1.8-9
[69] Matrix.utils_0.9.8 beeswarm_0.3.1
[71] processx_3.5.2 whisker_0.4
[73] ggridges_0.5.3 GlobalOptions_0.1.2
[75] png_0.1-7 rjson_0.2.20
[77] bitops_1.0-7 R.oo_1.24.0
[79] KernSmooth_2.23-20 rhdf5filters_1.2.1
[81] Biostrings_2.58.0 DelayedMatrixStats_1.12.3
[83] shape_1.4.5 parallelly_1.25.0
[85] sccore_0.1.3 beachmat_2.6.4
[87] memoise_2.0.0 plyr_1.8.6
[89] hexbin_1.28.2 ica_1.0-2
[91] zlibbioc_1.36.0 compiler_4.0.3
[93] dqrng_0.3.0 clue_0.3-59
[95] fitdistrplus_1.1-3 cli_2.5.0
[97] snakecase_0.11.0 ade4_1.7-16
[99] XVector_0.30.0 listenv_0.8.0
[101] ps_1.6.0 pbapply_1.4-3
[103] mgcv_1.8-35 tidyselect_1.1.1
[105] stringi_1.6.2 highr_0.9
[107] yaml_2.2.1 BiocSingular_1.6.0
[109] locfit_1.5-9.4 sass_0.4.0
[111] tools_4.0.3 future.apply_1.7.0
[113] rstudioapi_0.13 bluster_1.0.0
[115] foreach_1.5.1 git2r_0.28.0
[117] janitor_2.1.0 gridExtra_2.3
[119] farver_2.1.0 Rtsne_0.15
[121] digest_0.6.27 BiocManager_1.30.15
[123] shiny_1.6.0 Rcpp_1.0.6
[125] scuttle_1.0.4 later_1.2.0
[127] RcppAnnoy_0.0.18 httr_1.4.2
[129] Rdpack_2.1.1 colorspace_2.0-1
[131] fs_1.5.0 tensor_1.5
[133] splines_4.0.3 statmod_1.4.36
[135] spatstat.utils_2.1-0 multtest_2.46.0
[137] sessioninfo_1.1.1 plotly_4.9.3
[139] xtable_1.8-4 jsonlite_1.7.2
[141] nloptr_1.2.2.2 leidenAlg_0.1.1
[143] testthat_3.0.2 R6_2.5.0
[145] pillar_1.6.1 htmltools_0.5.1.1
[147] mime_0.10 glue_1.4.2
[149] fastmap_1.1.0 BiocNeighbors_1.8.2
[151] codetools_0.2-18 pkgbuild_1.2.0
[153] utf8_1.2.1 lattice_0.20-44
[155] bslib_0.2.5 spatstat.sparse_2.0-0
[157] tibble_3.1.2 ggbeeswarm_0.6.0
[159] leiden_0.3.7 survival_3.2-11
[161] limma_3.46.0 rmarkdown_2.8
[163] desc_1.3.0 biomformat_1.18.0
[165] munsell_0.5.0 GetoptLong_1.0.5
[167] rhdf5_2.34.0 GenomeInfoDbData_1.2.4
[169] iterators_1.0.13 reshape2_1.4.4
[171] gtable_0.3.0 rbibutils_2.1.1
[173] spatstat.core_2.1-2