Last updated: 2026-07-05
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immgenT-GP-analysis/analysis/
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Produced by script/ExtendedDataTable7_protein_gating.R:
# Extended Data Table 7: protein gating.
#
# One row per GP: its curated positive/negative protein-marker signature, and
# how well a protein gate built from that signature recovers cells with high GP
# loading. For each GP we gate cells by its markers (positive markers above
# their threshold, negative markers at/below), then measure the proportion of
# gated cells that are "positive" for the GP (loading > 0.1; the True Discovery
# Proportion) versus that proportion among all cells, and flag GPs whose gate is
# well aligned with the GP loading.
#
# Reproduces the published data/CITEseq_alignment_scores_manual.csv (Table S3):
# compute_alignment_scores() + format_scores_table() (code/R/gated_protein_helpers.R)
# run on the manually-curated marker set df_markers2 from
# code/R/citeseq_shared_setup.R. Here we keep the presentation subset of columns
# used in Table S3.
library(dplyr)
library(Matrix) # protein matrices are dgCMatrix; must be attached for `[` to dispatch
data_path <- "data/"
output_path <- "figures/generated/"
source("code/R/gated_protein_helpers.R") # compute_alignment_scores(), format_scores_table()
source("code/R/citeseq_shared_setup.R") # df_markers2, L_pm_filtered, protein_mat_normalized_lognorm,
# threshold_results_subset_manual, select_proteins, *_cells
scores_manual <- compute_alignment_scores(
df_m = df_markers2,
protein_mat = protein_mat_normalized_lognorm,
loading_mat = L_pm_filtered,
threshold_df = threshold_results_subset_manual,
selected_proteins = select_proteins,
exclude_cells = c(thymocyte_cells, proliferating_cells, miniverse_cells),
missing_threshold_action = "skip"
)
scores_manual_table <- format_scores_table(scores_manual, df_markers2)
# "Well aligned" in the published Table S3 is the formula-positive set
# (prop_pos_gated >= 0.25 & > prop_pos_all; 35 GPs here, matching
# data/CITEseq_alignment_scores_manual.csv) hand-trimmed to those whose protein
# gate and GP-loading gate also agree visually on the MDE embedding -- the 25
# GPs below. We reproduce that curated flag (intersected with the formula set so
# it stays internally consistent if the underlying numbers ever shift). Note
# this 25-GP table set is deliberately distinct from Figure 6b's separately
# curated 27-GP well_aligned_gps in code/R/citeseq_shared_setup.R.
well_aligned_final <- c(
"GP3", "GP8", "GP10", "GP12", "GP22", "GP25", "GP26", "GP27", "GP29", "GP30",
"GP32", "GP35", "GP41", "GP58", "GP63", "GP68", "GP77", "GP80", "GP107",
"GP117", "GP163", "GP166", "GP170", "GP171", "GP181"
)
scores_manual_table[["Well aligned"]] <-
scores_manual_table[["Well aligned"]] & scores_manual_table$GP %in% well_aligned_final
# Presentation subset used in Table S3, with the two proportion columns given
# their published, more descriptive headers.
protein_gating <- scores_manual_table[, c(
"GP",
"Positive markers",
"Negative markers",
"Prop. positive (gated)",
"Prop. positive (all)",
"Well aligned"
)]
colnames(protein_gating) <- c(
"GP",
"Positive markers",
"Negative markers",
"Prop. positive among gated (True Discovery Proportion)",
"Prop. positive among all cells",
"Well aligned"
)
write.csv(
protein_gating,
file = paste0(output_path, "ExtendedDataTable7_protein_gating.csv"),
row.names = FALSE
)Showing the first 20 rows (of 200), ordered well-aligned GPs first;
download the full table at figures/generated/ExtendedDataTable7_protein_gating.csv.
| GP | Positive markers | Negative markers | Prop. positive among gated (True Discovery Proportion) | Prop. positive among all cells | Well aligned |
|---|---|---|---|---|---|
| GP117 | CD73.5NTD, LY49A, CD24 | THY1.2, CD44 | 1.000 | 0.003 | TRUE |
| GP10 | KLRG1, CD8B, CD8A, ITAX.CD11C, ICAM1, ITA4.CD49D, CD29 | CD4, CD62L, CD5 | 0.988 | 0.042 | TRUE |
| GP26 | CD8B, CD38, CD44, CD86, CD8A, CD29, CD39, CD11A | CD45RB, CD62L | 0.985 | 0.306 | TRUE |
| GP68 | IL2RA.CD25, FR4, GITR.CD357, NEUROPILIN1.CD304 | 0.982 | 0.057 | TRUE | |
| GP171 | CD62L | CD44 | 0.971 | 0.868 | TRUE |
| GP58 | CD8B, CD8A | CD4 | 0.949 | 0.448 | TRUE |
| GP8 | TCRVG3 | 0.925 | 0.006 | TRUE | |
| GP30 | CD11A, CD49B, CD38 | CD8B, CD8A, CD4 | 0.880 | 0.128 | TRUE |
| GP27 | KLRG1, ICOS.CD278, GITR.CD357 | 0.864 | 0.035 | TRUE | |
| GP170 | ITB7, CD103, CD4, CD38 | GITR.CD357, CD62L | 0.823 | 0.214 | TRUE |
| GP80 | CD29, CD44, ITA4.CD49D | ITB7, CD103 | 0.795 | 0.398 | TRUE |
| GP35 | THY1.2, CD73.5NTD, CD11A, CD2, CD69, CD5, ITAM.CD11B | CD62L | 0.793 | 0.313 | TRUE |
| GP12 | FR4, CD73.5NTD | CD62L, CD45RB | 0.752 | 0.102 | TRUE |
| GP3 | THY1.2, CD5, CD2, CD11A, CD44, SCA1 | 0.746 | 0.112 | TRUE | |
| GP29 | CD8A | CD4, CD8B | 0.725 | 0.210 | TRUE |
| GP77 | CD55.DAF, CD38, CD24, LY49A, B220, ITB7, CD45RB | 0.722 | 0.003 | TRUE | |
| GP166 | CD44, THY1.2, KLRG1, ITB7, ITAM.CD11B | 0.708 | 0.406 | TRUE | |
| GP22 | LY49A | CD4, CD8B, TCRGD | 0.703 | 0.050 | TRUE |
| GP32 | CD45RB, CD27, CD62L | CD103, CD24, CD38, ITB7, CD4, CD73.5NTD | 0.696 | 0.548 | TRUE |
| GP25 | CD62L, CD45RB, CD55.DAF | 0.686 | 0.334 | TRUE |
Extended Data Table 7. One row per GP: its curated positive / negative protein marker signature, and how well a protein gate built from that signature recovers cells with high GP loading. For each GP, cells are gated by its markers (positive markers above their threshold, negative at/below); we report the proportion of gated cells positive for the GP (loading > 0.1; the True Discovery Proportion) versus that proportion among all cells, and a Well aligned flag. The flag is the formula-positive set (TDP >= 0.25 and greater than the all-cell proportion) hand-trimmed to those whose protein and loading gates also agree visually on the MDE embedding – reproducing the published table’s curated 25-GP set.
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