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immgenT-GP-analysis/analysis/
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| Rmd | fb8c855 | Ziang Zhang | 2026-07-08 | Add Extended Data Table 8: comprehensive gene signature matrix (long + wide) |
Alternate, per-GP-column layout of Extended Data Table 8; produced by script/ExtendedDataTable8_gene_signature_matrix_wide.R:
# Extended Data Table 8 (wide format): comprehensive gene signature matrix.
#
# For every GP, its full signature gene list (not just the top 5 shown in
# Extended Data Table 1): every gene with |score| > 0.1 on the same
# max|.|=1-per-GP-scaled gene factor matrix Table 1 uses, ranked by |score|
# within direction and capped at the top 100 up- and top 100 down-regulated
# genes. When a direction has more than 100 qualifying genes, one extra row
# right after the (capped) gene rows -- e.g. row 101 after 100 up-regulated
# genes -- notes how many more were left out (see
# code/R/gene_signature_helpers.R::build_gp_gene_signature_blocks()), so a GP
# contributes at most 202 rows. Different GPs have different signature-gene
# counts, so this is a ragged "wide" table: one 3-column (Gene, Direction,
# Score) block per GP, side by side, padded with blank cells up to the
# tallest block. A flat CSV can't express that per-GP grouping, so the output
# is an .xlsx with a merged GP header spanning each block.
#
# This is the wide, per-GP-column layout; script/ExtendedDataTable8_gene_signature_matrix_long.R
# produces the same signature genes as a tidy long table (one row per gene)
# instead.
#
# Required inputs (data/) -- see code/README.md's "Data provenance" table.
data_path <- "data/"
output_path <- "figures/final-selected/"
if (!requireNamespace("openxlsx", quietly = TRUE)) {
stop("Package 'openxlsx' is required. Please install it with install.packages('openxlsx').")
}
source("code/R/gene_signature_helpers.R")
F_pm_filtered <- readRDS(paste0(data_path, "F_pm_filtered.rds"))
# Normalize so each GP column has max|score| = 1 (same normalization Extended
# Data Table 1 uses for its gene signatures).
F_pm_filtered <- apply(F_pm_filtered, 2, function(x) x / max(abs(x)))
# F_pm_filtered's raw columns ("F1".."F200") are already in the same factor
# order as L_pm_filtered's "K1".."K200" (same underlying flashier fit --
# 01b_filter_cells.R only filters L's rows/cells, never F's columns), so
# column i is simply GPi; no cross-matrix name matching needed here.
n_gp <- ncol(F_pm_filtered)
gp_labels <- paste0("GP", seq_len(n_gp))
gp_blocks <- build_gp_gene_signature_blocks(F_pm_filtered, cutoff = 0.1, cap = 100)
max_rows <- max(vapply(gp_blocks, nrow, integer(1)))
pad_block <- function(block, n) {
if (nrow(block) < n) {
block <- rbind(block, data.frame(
Gene = rep(NA_character_, n - nrow(block)),
Direction = rep(NA_character_, n - nrow(block)),
Score = rep(NA_real_, n - nrow(block)),
stringsAsFactors = FALSE
))
}
block
}
wide_df <- do.call(cbind, lapply(gp_blocks, pad_block, n = max_rows))
# Write as .xlsx with a merged GP header spanning each Gene/Direction/Score
# triplet -- a flat CSV can't express this meta-column grouping.
wb <- openxlsx::createWorkbook()
sheet <- "Gene signatures"
openxlsx::addWorksheet(wb, sheet)
for (i in seq_len(n_gp)) {
col_start <- (i - 1) * 3 + 1
openxlsx::mergeCells(wb, sheet, cols = col_start:(col_start + 2), rows = 1)
openxlsx::writeData(wb, sheet, gp_labels[i], startCol = col_start, startRow = 1, colNames = FALSE)
}
openxlsx::writeData(
wb, sheet,
matrix(rep(c("Gene", "Direction", "Score"), n_gp), nrow = 1),
startCol = 1, startRow = 2, colNames = FALSE
)
openxlsx::writeData(wb, sheet, wide_df, startCol = 1, startRow = 3, colNames = FALSE, na.string = "")
bold_center <- openxlsx::createStyle(textDecoration = "bold", halign = "center")
bold <- openxlsx::createStyle(textDecoration = "bold")
openxlsx::addStyle(wb, sheet, bold_center, rows = 1, cols = seq_len(n_gp * 3), gridExpand = TRUE)
openxlsx::addStyle(wb, sheet, bold, rows = 2, cols = seq_len(n_gp * 3), gridExpand = TRUE)
openxlsx::freezePane(wb, sheet, firstActiveRow = 3, firstActiveCol = 1)
openxlsx::saveWorkbook(
wb,
file = paste0(output_path, "ExtendedDataTable8_gene_signature_matrix_wide.xlsx"),
overwrite = TRUE
)This table is a per-GP grouping (200 GPs x 3 columns each: Gene,
Direction, Score) rather than one row per GP, so it’s distributed as an
.xlsx with a merged GP header rather than a flat CSV. Pick a GP below to
browse its full signature gene list (up to 202 rows: up to 100 up- and
100 down-regulated, plus a truncation note if either was capped – see
below); sort by clicking a column header, or use the search boxes under
Gene / Direction to filter further.
Download the full spreadsheet at figures/final-selected/ExtendedDataTable8_gene_signature_matrix_wide.xlsx.
Extended Data Table 8 (wide format). The comprehensive signature gene list for every GP, one 3-column block per GP (Gene, Direction, Score), listing every gene with |score| > 0.1 on the same max|.|=1-per-GP-scaled gene factor matrix Extended Data Table 1 uses, ranked by |score| within direction (up-regulated genes first, then down-regulated), and capped at the top 100 up- and top 100 down-regulated genes per GP. When a direction has more than 100 qualifying genes, one extra row immediately follows its (capped) gene rows – e.g. row 101 after 100 up-regulated genes – noting how many more were left out (Score blank for that row). Table 1’s Top Genes +/- columns are this table’s top 5 per direction; this table is the full list. Different GPs have different signature-gene counts, so shorter blocks are blank-padded in the downloadable spreadsheet. See Extended Data Table 8 for the same data as a tidy long table (one row per gene).
sessionInfo()R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.6.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
[1] en_CA/en_CA/en_CA/C/en_CA/en_CA
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] crosstalk_1.2.2 DT_0.34.0
loaded via a namespace (and not attached):
[1] jsonlite_2.0.0 compiler_4.5.1 promises_1.5.0 Rcpp_1.1.1-1.1
[5] stringr_1.6.0 git2r_0.36.2 later_1.4.4 jquerylib_0.1.4
[9] yaml_2.3.12 fastmap_1.2.0 mime_0.13 R6_2.6.1
[13] workflowr_1.7.2 knitr_1.50 htmlwidgets_1.6.4 tibble_3.3.0
[17] rprojroot_2.1.1 shiny_1.12.1 bslib_0.9.0 pillar_1.11.1
[21] rlang_1.2.0 cachem_1.1.0 stringi_1.8.7 httpuv_1.6.16
[25] xfun_0.55 fs_1.6.6 sass_0.4.10 lazyeval_0.2.2
[29] otel_0.2.0 cli_3.6.6 magrittr_2.0.5 digest_0.6.39
[33] xtable_1.8-4 lifecycle_1.0.5 vctrs_0.7.3 evaluate_1.0.5
[37] glue_1.8.1 whisker_0.4.1 rmarkdown_2.30 tools_4.5.1
[41] pkgconfig_2.0.3 htmltools_0.5.9