Last updated: 2026-07-05
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immgenT-GP-analysis/analysis/
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Produced by script/ExtendedDataTable5_GP_during_activation.R:
# Extended Data Table 5: GP during activation.
#
# One row per GP (GP1..GP200) summarizing how each GP's loading changes with
# T-cell activation, computed separately in CD4 and CD8: the mean change in
# loading (activated minus resting), the average loading over activated+resting
# cells (AveExpr), the standardized mean difference d (= change / pooled SD),
# and the CD8/CD4 ratio of the mean loading changes. This reproduces the
# `GP_activation_summary` object built in script/Figure3.R (panel 3a) from
# code/R/activation_shared_setup.R.
#
# Note vs. the published Table S2: the mean_change_loadings / AveExpr / Ratio
# columns reproduce exactly (identical CD4/CD8 activated-vs-resting populations),
# but the published z_CD4 / z_CD8 were a large-sample z-test statistic from an
# older limma-style DGE that the analysis has since replaced with the
# standardized mean difference d_CD4 / d_CD8 (change / pooled loading SD; see
# activation_shared_setup.R::std_mean_diff) -- the same statistic Figure 3a
# plots. This table therefore carries d_CD4 / d_CD8, the current quantity, not
# the retired z.
library(dplyr)
data_path <- "data/"
output_path <- "figures/generated/"
source("code/R/plot_utils.R") # scale_cols(), used by activation_shared_setup.R
source("code/R/setup_data.R")
gp_data <- load_gp_data(data_path = data_path)
L_pm_filtered <- gp_data$L_pm_filtered
F_pm_filtered <- gp_data$F_pm_filtered
seurat_meta_filtered <- gp_data$seurat_meta_filtered
source("code/R/activation_shared_setup.R") # -> diff_factors_merged, d_factors_merged
GP_activation_summary <- diff_factors_merged %>%
dplyr::inner_join(d_factors_merged %>% dplyr::select(SYMBOL, d_CD4, d_CD8), by = "SYMBOL") %>%
dplyr::mutate(Ratio_CD8_CD4 = mean_change_loadings_CD8 / mean_change_loadings_CD4) %>%
dplyr::select(
GP = SYMBOL,
mean_change_loadings_CD4, mean_change_loadings_CD8,
AveExpr_CD4, AveExpr_CD8,
d_CD4, d_CD8,
Ratio_CD8_CD4
)
write.csv(
GP_activation_summary,
file = paste0(output_path, "ExtendedDataTable5_GP_during_activation.csv"),
row.names = FALSE
)Showing the first 20 rows (of 200); download the full table at figures/generated/ExtendedDataTable5_GP_during_activation.csv.
| GP | mean_change_loadings_CD4 | mean_change_loadings_CD8 | AveExpr_CD4 | AveExpr_CD8 | d_CD4 | d_CD8 | Ratio_CD8_CD4 |
|---|---|---|---|---|---|---|---|
| GP1 | 0.0329 | 0.0254 | 0.3501 | 0.3464 | 0.5328 | 0.4118 | 0.7728 |
| GP2 | -0.0110 | -0.0108 | 0.0193 | 0.0132 | -0.2758 | -0.2714 | 0.9841 |
| GP3 | 0.0046 | 0.0102 | 0.0016 | 0.0038 | 0.2100 | 0.4615 | 2.1973 |
| GP4 | 0.0146 | 0.0378 | 0.1613 | 0.1760 | 0.1229 | 0.3171 | 2.5801 |
| GP5 | 0.0015 | 0.0018 | 0.0008 | 0.0015 | 0.1079 | 0.1340 | 1.2421 |
| GP6 | 0.0731 | 0.0270 | 0.0248 | 0.0106 | 1.1495 | 0.4241 | 0.3690 |
| GP7 | 0.0000 | -0.0001 | 0.0001 | 0.0001 | -0.0092 | -0.0235 | 2.5494 |
| GP8 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0235 | 0.0234 | 0.9958 |
| GP9 | -0.0466 | -0.0422 | 0.1003 | 0.0848 | -0.3301 | -0.2993 | 0.9066 |
| GP10 | 0.0128 | 0.1335 | 0.0045 | 0.0498 | 0.1353 | 1.4134 | 10.4482 |
| GP11 | -0.0293 | -0.0224 | 0.0985 | 0.0801 | -0.2610 | -0.1993 | 0.7635 |
| GP12 | 0.1063 | 0.0078 | 0.0641 | 0.0327 | 1.4449 | 0.1062 | 0.0735 |
| GP13 | 0.0105 | 0.0005 | 0.0035 | 0.0003 | 0.6904 | 0.0309 | 0.0447 |
| GP14 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | -0.0213 | -0.0174 | 0.8166 |
| GP15 | -0.0023 | -0.0217 | 0.0541 | 0.0678 | -0.0366 | -0.3375 | 9.2272 |
| GP16 | 0.0034 | 0.0007 | 0.0189 | 0.0165 | 0.0616 | 0.0128 | 0.2074 |
| GP17 | -0.0006 | 0.0030 | 0.0200 | 0.0196 | -0.0471 | 0.2371 | -5.0291 |
| GP18 | -0.0001 | -0.0002 | 0.0002 | 0.0003 | -0.0077 | -0.0309 | 4.0064 |
| GP19 | -0.0001 | 0.0000 | 0.0001 | 0.0000 | -0.0129 | -0.0039 | 0.3041 |
| GP20 | 0.0012 | 0.0029 | 0.0005 | 0.0013 | 0.0863 | 0.1982 | 2.2969 |
Extended Data Table 5. One row per GP describing how
its loading changes with T-cell activation, computed separately in CD4
and CD8: mean_change_loadings (activated minus resting mean
loading), AveExpr (mean loading over activated + resting
cells), d (standardized mean difference = change / pooled
loading SD, the statistic plotted in Figure 3a), and
Ratio_CD8_CD4 (ratio of the CD8 to CD4 mean loading
change). This reproduces the GP_activation_summary of
Figure 3a; note that the d_CD4 / d_CD8 columns
replace the retired limma-style z_CD4 / z_CD8
of the earlier table version.
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 stringr_1.6.0
[25] compiler_4.5.1 fs_1.6.6 Rcpp_1.1.1-1.1 pkgconfig_2.0.3
[29] later_1.4.4 digest_0.6.39 R6_2.6.1 pillar_1.11.1
[33] magrittr_2.0.5 bslib_0.9.0 tools_4.5.1 cachem_1.1.0