Last updated: 2026-07-04
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immgenT-GP-analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 8f0198e | Ziang Zhang | 2026-07-03 | Build site. |
| Rmd | 9944479 | Ziang Zhang | 2026-07-03 | Drop Condition column from TableS1 (would require healthy-only |
| html | 8b50a78 | Ziang Zhang | 2026-07-03 | Build site. |
| html | 92021bf | Ziang Zhang | 2026-07-03 | Build site. |
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| Rmd | f9db962 | Ziang Zhang | 2026-07-02 | Simplify layout: drop old code/script folders, rename |
| html | c6e5086 | Ziang Zhang | 2026-07-02 | Build site. |
| html | 5a79883 | Ziang Zhang | 2026-07-02 | Build site. |
| Rmd | 2b0e445 | Ziang Zhang | 2026-07-02 | Fix GitHub source links to point at the new |
| html | cf1d0ac | Ziang Zhang | 2026-07-02 | Build site. |
| Rmd | 06b2461 | Ziang Zhang | 2026-07-02 | Initial commit: immgenT-GP-analysis |
| html | 06b2461 | Ziang Zhang | 2026-07-02 | Initial commit: immgenT-GP-analysis |
Produced by script/TableS1.R:
# Supplementary Table 1: per-GP summary.
#
# One row per GP: which Level-1/Level-2/organ categories it predicts well
# (AUC > 0.8, driven by high loading), plus its top positive and negative
# signature genes. Unlike Figure2.R (2A) and Figure4.R, these three AUC
# computations are restricted to non-thymocyte cells only, WITHOUT an
# additional healthy-only restriction (see code/pipeline/02_compute_auc.R) --
# confirmed against the published Table S1.xlsx, whose Organ column includes
# disease-specific sites (SLO, prostate, pancreas, synovial fluid) that only
# exist in this broader, non-healthy-restricted population.
#
# GAP: the published table also has a Condition column (per-GP AUC against
# condition_detailed, same non-thymocyte-only restriction) that isn't
# reproduced here -- recomputing it takes ~3 hours, deferred for now (see
# code/pipeline/02_compute_auc.R's header for detail).
#
# Source: ported from Supplement_Table1.R (unchanged apart from the output
# path, the AUC-file family used, the GP naming fix below, and the
# still-missing condition column, see above).
#
# Required inputs (data/) -- see code/README.md's "Data provenance" table
# for the full picture.
data_path <- "data/"
output_path <- "figures/generated/"
L_pm_filtered <- readRDS(paste0(data_path, "L_pm_filtered.rds")) # code/pipeline/01b_filter_cells.R
F_pm_filtered <- readRDS(paste0(data_path, "F_pm_filtered.rds")) # code/pipeline/01b_filter_cells.R
level_1_AUC_list <- readRDS(paste0(data_path, "level_1_AUC_list_figure_no_thymocytes.rds")) # code/pipeline/02_compute_auc.R
level_2_AUC_list <- readRDS(paste0(data_path, "level_2_AUC_list_figure_no_thymocytes.rds")) # code/pipeline/02_compute_auc.R
organ_simplified_AUC_list_figure <- readRDS(paste0(data_path, "organ_simplified_AUC_list_figure_no_thymocytes.rds")) # code/pipeline/02_compute_auc.R
# Normalize F_pm_filtered such that each column has max abs value of 1
F_pm_filtered <- apply(F_pm_filtered, 2, function(x) x / max(abs(x)))
colnames(F_pm_filtered) <- colnames(L_pm_filtered)
# L_pm_filtered/F_pm_filtered and the cached AUC matrices all share the raw
# "K1", "K2", ... column names (from the underlying flashier fit); rename to
# "GP1", "GP2", ... only for display, after all matching against those raw
# names is done.
gps <- colnames(F_pm_filtered)
gp_labels <- paste0("GP", seq_along(gps))
get_passing_categories <- function(auc_list, gps, L_mat, threshold = 0.8) {
auc_mat <- auc_list$auc
thr_mat <- auc_list$threshold
gp_median_loading <- apply(L_mat, 2, median)
lapply(gps, function(gp) {
if (!gp %in% colnames(auc_mat)) return("")
auc_vals <- auc_mat[, gp]
thr_vals <- thr_mat[, gp]
med_load <- gp_median_loading[gp]
# Keep only categories predicted by HIGH loading (threshold >= median loading)
cats <- rownames(auc_mat)[auc_vals > threshold & thr_vals >= med_load]
paste(cats, collapse = "; ")
})
}
level1_cats <- get_passing_categories(level_1_AUC_list, gps, L_pm_filtered)
level2_cats <- get_passing_categories(level_2_AUC_list, gps, L_pm_filtered)
organ_cats <- get_passing_categories(organ_simplified_AUC_list_figure, gps, L_pm_filtered)
sig_genes_pos <- lapply(gps, function(gp) {
vals <- F_pm_filtered[, gp]
candidates <- names(vals)[vals > 0.25]
top <- head(candidates[order(vals[candidates], decreasing = TRUE)], 5)
paste(top, collapse = "; ")
})
sig_genes_neg <- lapply(gps, function(gp) {
vals <- F_pm_filtered[, gp]
candidates <- names(vals)[vals < -0.25]
top <- head(candidates[order(abs(vals[candidates]), decreasing = TRUE)], 5)
paste(top, collapse = "; ")
})
supp_table <- data.frame(
GP = gp_labels,
Level1 = unlist(level1_cats),
Level2 = unlist(level2_cats),
Organ = unlist(organ_cats),
Signature_Genes_Pos = unlist(sig_genes_pos),
Signature_Genes_Neg = unlist(sig_genes_neg),
stringsAsFactors = FALSE
)
write.csv(supp_table, file = paste0(output_path, "Supplementary_Table1_GP_summary.csv"), row.names = FALSE)Showing the first 20 rows (of 200); download the full table at figures/generated/Supplementary_Table1_GP_summary.csv.
| GP | Level1 | Level2 | Organ | Signature_Genes_Pos | Signature_Genes_Neg |
|---|---|---|---|---|---|
| GP1 | CD8.K; CD4.Y; CD4.P; Treg.P; gdT.B; gdT.C; gdT.D; gdT.P; CD8aa.C; CD8aa.D; CD8aa.P; DP.wY; DP.wP | CT010467.1; Tmsb4x; Actb; Tmsb10; Eef1a1 | |||
| GP2 | CD8.wM; CD4.wM; Treg.wM; gdT.E; gdT.wM; CD8aa.wM; Tz.wM; DN.wM; DP.wC; DP.wM | Arhgap15; Dock2; Mbnl1; Inpp4b; Foxp1 | Tmsb10; Tmsb4x; Fau; Pfn1; Ppia | ||
| GP3 | gdT.A; gdT.B; gdT.C; gdT.D; gdT.E; gdT.F; gdT.Z; CD8aa.C; CD8aa.D; CD8aa.E; DP.wY | small intestine epi | Gzma; Cd7; Ccl5; Fcer1g; Tyrobp | Tmsb10; Tpt1; CT010467.1; Actb; Tmsb4x | |
| GP4 | CD4.wN | SLO | Tmsb10; Fau; CT010467.1; Pfn1; Ppia | Dock2; Rack1; Arhgap15; Myh9; Ptprc | |
| GP5 | CD8.K; CD8.P; CD4.P; Treg.P; gdT.P; CD8aa.P; Tz.P; DN.P; DP.wP | H2ac8; H1f5; H1f4; H4c4; Hist1h2ap | |||
| GP6 | CD4.T; CD4.U; CD4.X; CD4.Y; CD4.P; Treg.D; Treg.E; gdT.Q; gdT.R; gdT.S; gdT.T; gdT.U; gdT.V; gdT.X; gdT.Z; Tz.D | S100a6; Gzmb; Lgals1; Vim; Crip1 | Tmsb10; CT010467.1; Eef1a1; Cmss1; Cdk8 | ||
| GP7 | gdT.X; gdT.Z; DP.wG | CT010467.1; Ccl5; Cmss1; Cdk8; Crip1 | Igfbp4; Ccr7; Limd2 | ||
| GP8 | gdT.Z | Fth1; S100a6; Thy1; Ctla2a; Gem | Ccl5; Gzma; Gzmb; AW112010; Tyrobp | ||
| GP9 | CD8.wU; CD8.wV; DP.wC; DP.wD; DP.wE | Cdk8; Cmss1; CT010467.1; Eef2; Myh9 | Tmsb10; Fau; Tmsb4x; Cd52 | ||
| GP10 | CD8.G; CD8.H; CD8.I; CD8.J; CD8.K; CD8.wU; CD8.wY; CD4.S; DN.F | Ccl5; Gzma; Lgals1; Nkg7; Ly6c2 | |||
| GP11 | placenta; kidney | Vps37b; Gramd3; Dusp10; Junb; Ccr7 | Cd52; Ltb; Ptprcap | ||
| GP12 | CD8.C; CD8.wX; CD4.G; CD4.H; CD4.I; CD4.U; Treg.B; DN.A; DN.G | Izumo1r; Cdk8; Tox; Tbc1d4; Pdcd1 | Crip1; Ccl5; Satb1; Igfbp4; Selplg | ||
| GP13 | CD4.X; CD4.Y; gdT.Q; gdT.R; gdT.S; gdT.T; gdT.U; gdT.V; Tz.D | Tmem176b; Tmem176a; Il7r; CT010467.1; Cd163l1 | Ms4a4b; Inpp4b; Ctla4; Ifi27l2a; Cd2 | ||
| GP14 | DP.wF; DP.wG | Cd8b1; Cd8a; Satb1; Rag1; Themis | H2-K1; Crip1; H2-D1; B2m; Eef1a1 | ||
| GP15 | CD8.D; CD8.F; CD4.F; Treg.C; gdT.X; DN.G; DN.H; DP.wH | Eif5a; Npm1; Hsp90ab1; Ncl; Mif | Malat1; Tmsb4x; Itm2b; Cd52; Ypel3 | ||
| GP16 | CD8.F; CD4.D; DN.I; DN.J | Ifi27l2a; Isg15; Ly6a; Bst2; Stat1 | |||
| GP17 | Ifi27l2a; Atp5e; Tmsb4x; Ripor2; Lef1 | Actg1; Ubb; Hspa8; Eef1a1; Il31ra | |||
| GP18 | Cpa3; CT010467.1; Crip1; Ccl5; Tmsb10 | Ifi27l2a; Ccr7; Ly6d; Rgcc | |||
| GP19 | Prl8a9; Tpbpa; Psg21; Prl3b1; Creg1 | Cts6; Ccr10 | |||
| GP20 | CD8.K | Cenpa; Ube2c; Cdc20; Cenpf; Arl6ip1 | H2ac8; H1f5; H1f4; Hist1h2ap; H1f3 |
Table S1. One row per GP: which Level-1 (major
lineage), Level-2 (sub-lineage), and organ categories it predicts well
(AUC > 0.8, driven by high loading, restricted to non-thymocyte cells
– healthy and diseased together, unlike Figure2/Figure4’s healthy-only
restriction), plus its top 5 positive and negative signature genes
(|score| > 0.25). A Condition column (per-GP AUC against detailed
condition) is not yet reproduced here – see code/pipeline/02_compute_auc.R
for why.
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: Asia/Tokyo
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] vctrs_0.7.3 cli_3.6.6 knitr_1.50 rlang_1.2.0
[5] xfun_0.55 stringi_1.8.7 otel_0.2.0 promises_1.5.0
[9] jsonlite_2.0.0 workflowr_1.7.2 glue_1.8.1 rprojroot_2.1.1
[13] git2r_0.36.2 htmltools_0.5.9 httpuv_1.6.16 sass_0.4.10
[17] rmarkdown_2.30 evaluate_1.0.5 jquerylib_0.1.4 tibble_3.3.0
[21] fastmap_1.2.0 yaml_2.3.12 lifecycle_1.0.5 whisker_0.4.1
[25] stringr_1.6.0 compiler_4.5.1 fs_1.6.6 Rcpp_1.1.1-1.1
[29] pkgconfig_2.0.3 later_1.4.4 digest_0.6.39 R6_2.6.1
[33] pillar_1.11.1 magrittr_2.0.5 bslib_0.9.0 tools_4.5.1
[37] cachem_1.1.0