Supplementary: Comparison of our celltype propns with Rowitch paper
Will Macnair
Neurogenomics, Neuroscience and Rare Diseases, RocheDecember 06, 2021
Last updated: 2021-12-06
Checks: 4 3
Knit directory: MS_lesions/
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Setup / definitions
Libraries
Helper functions
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.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_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"Outputs
# where to save?
save_dir = 'output/ms09_ancombc'
date_tag = '2021-11-12'
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
gm_spec = list(
name = 'lesions_GM',
subset = list(matter = 'GM', neuro_ok = TRUE),
size = list(min_count = 10, min_prop = 0.1),
exc_regex = NULL,
formula = '~ lesion_type + sex + age_scale + pmi_cat2',
fixef_test = 'lesion_type',
fixef_covar = c('sex', 'age_scale', 'pmi_cat2'),
ranef_var = 'subject_id'
)Load inputs
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)# 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) ]rowitch_dt = rowitch_f %>% read_excel(sheet = "Sheet1") %>%
as.data.table %>%
.[, .(type_fine,
rowitch_guess = factor(rowitch_guess),
anna_group = factor(anna_protein_group)
)]
conos_dt = merge(conos_dt, rowitch_dt, by = 'type_fine', all.x = TRUE)Processing / calculations
Analysis
Abundances and proportions, split by different groupings
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')
}type_fine
rowitch_guess
anna_group
Outputs
devtools::session_info()Registered S3 method overwritten by 'cli':
method from
print.boxx spatstat.geom- Session info ---------------------------------------------------------------
setting value
version R version 4.0.5 (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 2021-12-06
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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
[41] scuttle_1.0.4 zlibbioc_1.36.0
[43] miniUI_0.1.1.1 bslib_0.3.1
[45] rpart_4.1-15 shiny_1.7.1
[47] BiocSingular_1.6.0 xfun_0.27
[49] clue_0.3-60 pkgbuild_1.2.0
[51] multtest_2.46.0 cluster_2.1.2
[53] caTools_1.18.2 TSP_1.1-11
[55] biomformat_1.18.0 tibble_3.1.5
[57] ape_5.5 listenv_0.8.0
[59] Biostrings_2.58.0 png_0.1-7
[61] permute_0.9-5 future_1.22.1
[63] withr_2.4.2 bitops_1.0-7
[65] plyr_1.8.6 cellranger_1.1.0
[67] dqrng_0.3.0 pillar_1.6.4
[69] gplots_3.1.1 GlobalOptions_0.1.2
[71] cachem_1.0.6 fs_1.5.0
[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 sessioninfo_1.1.1
[89] plotly_4.10.0 GenomeInfoDbData_1.2.4
[91] gridExtra_2.3 glmmTMB_1.1.2.3
[93] lattice_0.20-45 deldir_1.0-6
[95] utf8_1.2.2 later_1.3.0
[97] dplyr_1.0.7 jsonlite_1.7.2
[99] pbapply_1.5-0 sparseMatrixStats_1.2.1
[101] genefilter_1.72.1 lazyeval_0.2.2
[103] promises_1.2.0.1 doParallel_1.0.16
[105] R.utils_2.11.0 goftest_1.2-3
[107] spatstat.utils_2.2-0 rmarkdown_2.11
[109] cowplot_1.1.1 blme_1.0-5
[111] statmod_1.4.36 Rtsne_0.15
[113] HDF5Array_1.18.1 survival_3.2-13
[115] numDeriv_2016.8-1.1 yaml_2.2.1
[117] htmltools_0.5.2 memoise_2.0.0
[119] locfit_1.5-9.4 digest_0.6.28
[121] mime_0.12 registry_0.5-1
[123] RSQLite_2.2.8 future.apply_1.8.1
[125] remotes_2.4.1 blob_1.2.2
[127] vegan_2.5-7 R.oo_1.24.0
[129] splines_4.0.5 Rhdf5lib_1.12.1
[131] Cairo_1.5-12.2 RCurl_1.98-1.5
[133] broom_0.7.9 hms_1.1.1
[135] rhdf5_2.34.0 colorspace_2.0-2
[137] BiocManager_1.30.16 shape_1.4.6
[139] sass_0.4.0 Rcpp_1.0.7
[141] RANN_2.6.1 fansi_0.5.0
[143] parallelly_1.28.1 R6_2.5.1
[145] ggridges_0.5.3 lifecycle_1.0.1
[147] bluster_1.0.0 minqa_1.2.4
[149] testthat_3.1.0 leiden_0.3.8
[151] jquerylib_0.1.4 snakecase_0.11.0
[153] desc_1.4.0 RcppAnnoy_0.0.19
[155] iterators_1.0.13 TMB_1.7.22
[157] htmlwidgets_1.5.4 beachmat_2.6.4
[159] polyclip_1.10-0 mgcv_1.8-38
[161] globals_0.14.0 leidenAlg_0.1.1
[163] codetools_0.2-18 lubridate_1.8.0
[165] gtools_3.9.2 prettyunits_1.1.1
[167] R.methodsS3_1.8.1 gtable_0.3.0
[169] DBI_1.1.1 git2r_0.28.0
[171] tensor_1.5 httr_1.4.2
[173] highr_0.9 KernSmooth_2.23-20
[175] stringi_1.7.4 progress_1.2.2
[177] reshape2_1.4.4 farver_2.1.0
[179] annotate_1.68.0 hexbin_1.28.2
[181] colorRamps_2.3 sccore_1.0.0
[183] boot_1.3-28 grr_0.9.5
[185] BiocNeighbors_1.8.2 lme4_1.1-27.1
[187] ade4_1.7-18 geneplotter_1.68.0
[189] scattermore_0.7 DESeq2_1.30.1
[191] bit_4.0.4 spatstat.data_2.1-0
[193] janitor_2.1.0 pkgconfig_2.0.3
[195] lmerTest_3.1-3 knitr_1.36