Sézary Cell Pseudotime Mapping onto Healthy CD4+ T Cell Differentiation Trajectory
Monocle3 reference trajectory with state-anchored pseudotime bins
Nasir Mahmood Abbasi
2026-03-05
1 Load Libraries
2 Load Objects
2.1 Load Reference (Healthy CD4+ T Cells with Trajectory)
# ── CRITICAL requirements for this object ─────────────────────────────────
# ✅ CD4 Proliferating cluster removed (MKI67+/TOP2A+/CDK1+)
# ✅ UMAP built with return.model = TRUE (required for MapQuery)
# ✅ monocle3_pseudotime column (will be recomputed here for reproducibility)
# ✅ predicted.celltype.l2 (Azimuth l2 labels)
# ✅ cell_type annotation (clusters 0-6)
reference_integrated <- readRDS(
"../1-Final_Custom_MST_Monocle3_Trajectory_and_mapping/1-Custom_MST/Objects/cd4_ref_dual_trajectory.rds"
)
cat("=== Reference object summary ===\n")=== Reference object summary ===Cells : 11466 Assays : RNA, ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3, SCT, integrated Reductions: pca, umap, integrated_dr, ref.umap UMAP model: TRUE Clusters : 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 # ── Confirm CD4 Proliferating cells absent ────────────────────────────────
if (any(grepl("Prolif|prolif|cycling|Cycling",
reference_integrated$cell_type, ignore.case = TRUE))) {
warning("⚠️ Proliferating cell_type labels detected — remove before proceeding.")
} else {
cat("✅ No proliferating cell_type labels\n")
}✅ No proliferating cell_type labelsprolif_markers <- c("MKI67", "TOP2A", "PCNA", "CDK1", "STMN1")
prolif_present <- intersect(prolif_markers, rownames(reference_integrated))
if (length(prolif_present) > 0) {
DefaultAssay(reference_integrated) <- "SCT"
prolif_expr <- Matrix::colMeans(
GetAssayData(reference_integrated, layer = "data")[prolif_present, , drop = FALSE]
)
cat("Prolif marker score — max:", round(max(prolif_expr), 3),
"| cells > 0.5:", sum(prolif_expr > 0.5), "\n")
if (sum(prolif_expr > 0.5) > 50) {
warning("⚠️ Substantial proliferation signal — verify removal was complete.")
} else {
cat("✅ Proliferating cells confirmed absent\n")
}
}Prolif marker score — max: 1.187 | cells > 0.5: 6
✅ Proliferating cells confirmed absent
Cell type distribution (reference):
CD4 Tnaive (CCR7+SELL+TCF7+) CD4 TCM (CD161+/IL7R+) CD4 TCM (CCR4+/Th2-like) CD4 CTL/Temra (GZMK+GZMA+CCL5+) CD4 TEM (NF-kB activated)
5479 3994 522 490 412
CD4 Treg (FOXP3+Helios+CD25+) CD4 Tnaive-RTE (IGF1R+)
336 233
Azimuth l2 distribution (reference):
CD4 Naive CD4 TCM CD4 TEM CD4 Temra/CTL Treg
2037 9067 145 10 207 # Extend palette to cover any labels not pre-seeded above
ref_l2_labels <- unique(as.character(reference_integrated$predicted.celltype.l2))
ref_l2_labels <- ref_l2_labels[!is.na(ref_l2_labels)]
azimuth_l2_colors <- extend_palette(azimuth_l2_colors, ref_l2_labels)
cat("\nColour palette covers", length(ref_l2_labels), "reference labels ✅\n")
Colour palette covers 5 reference labels ✅# ── Hard stop: UMAP model must exist for MapQuery ─────────────────────────
if (is.null(reference_integrated@reductions$umap@misc$model)) {
stop(
"❌ UMAP model missing.\n",
" Re-run: reference_integrated <- RunUMAP(reference_integrated,\n",
" dims = 1:20, return.model = TRUE)\n",
" Then re-save and reload."
)
} else {
cat("✅ UMAP model present — MapQuery projection ready\n")
}✅ UMAP model present — MapQuery projection ready2.2 Load & Prepare Malignant CD4+ T Cells — Per-Cell-Line SCTransform
All_samples_Merged <- readRDS(
"/home/nabbasi/apollo_home/1-Seurat_RDS_OBJECT_FINAL/All_samples_Merged_with_Renamed_Clusters_Cell_state-03-12-2025.rds.rds"
)
cat("All cell lines:\n")All cell lines:
L1 L2 L3 L4 L5 L6 L7 CD4Tcells_lab CD4Tcells_10x
5825 5935 6428 6006 6022 5148 5331 5106 3504 All_samples_Merged$Group <- ifelse(
All_samples_Merged$cell_line %in% paste0("L", 1:7), "MalignantCD4T",
ifelse(
All_samples_Merged$cell_line %in%
c("CD4Tcells_lab", "CD4Tcells_10x"),
"NormalCD4T", "Other"
)
)
cat("\nGroup distribution:\n")
Group distribution:
MalignantCD4T NormalCD4T
40695 8610 MalignantCD4T_raw <- subset(All_samples_Merged, subset = Group == "MalignantCD4T")
cat("\nMalignant CD4T cells:", ncol(MalignantCD4T_raw), "\n")
Malignant CD4T cells: 40695
L1 L2 L3 L4 L5 L6 L7 CD4Tcells_lab CD4Tcells_10x
5825 5935 6428 6006 6022 5148 5331 0 0 used (Mb) gc trigger (Mb) max used (Mb)
Ncells 9068604 484.4 15929212 850.8 15929212 850.8
Vcells 1394672850 10640.6 4458683922 34017.1 3722027787 28396.9# ══════════════════════════════════════════════════════════════════════════
# PER-CELL-LINE SCTransform
# ══════════════════════════════════════════════════════════════════════════
# WHY: Each cell line is a separate library with different sequencing depth
# and ambient RNA profile. A single merged SCT model conflates batch
# variation with biology. Per-line SCT corrects for this before projection.
#
# APPROACH:
# 1. Split by cell_line
# 2. SCTransform each line (regress percent.mt + cell cycle scores)
# 3. Select HVGs by cross-line frequency — most robust to batch effects
# 4. Merge, scale, PCA — ready for FindTransferAnchors
# ══════════════════════════════════════════════════════════════════════════
cat("=== Per-cell-line SCTransform ===\n")=== Per-cell-line SCTransform ===# Save names BEFORE lapply to avoid double SplitObject call
# FIX: original script called SplitObject twice — wasteful and risky
cell_line_names <- names(SplitObject(MalignantCD4T_raw, split.by = "cell_line"))
cell_line_list <- SplitObject(MalignantCD4T_raw, split.by = "cell_line")
cat("Lines to process:", paste(cell_line_names, collapse = ", "), "\n\n")Lines to process: L1, L2, L3, L4, L5, L6, L7 cell_line_list <- lapply(cell_line_names, function(line_name) {
obj <- cell_line_list[[line_name]]
cat("Processing:", line_name, "| Cells:", ncol(obj), "\n")
if (!"percent.mt" %in% colnames(obj@meta.data))
obj[["percent.mt"]] <- PercentageFeatureSet(obj, pattern = "^MT-")
# Cell cycle regression removes cycling artefacts without removing cells
vars_regress <- tryCatch({
obj <- CellCycleScoring(obj,
s.features = cc.genes$s.genes,
g2m.features = cc.genes$g2m.genes,
set.ident = FALSE)
cat(" → Cell cycle scores computed\n")
c("percent.mt", "S.Score", "G2M.Score")
}, error = function(e) {
cat(" ⚠️ Cell cycle scoring failed — regressing percent.mt only\n")
"percent.mt"
})
obj <- SCTransform(obj,
vars.to.regress = vars_regress,
variable.features.n = 3000,
vst.flavor = "v2",
verbose = FALSE)
cat(" ✅", line_name, "complete |", length(VariableFeatures(obj)), "HVGs\n")
return(obj)
})Processing: L1 | Cells: 5825
→ Cell cycle scores computed
✅ L1 complete | 3000 HVGs
Processing: L2 | Cells: 5935
→ Cell cycle scores computed
✅ L2 complete | 3000 HVGs
Processing: L3 | Cells: 6428
→ Cell cycle scores computed
✅ L3 complete | 3000 HVGs
Processing: L4 | Cells: 6006
→ Cell cycle scores computed
✅ L4 complete | 3000 HVGs
Processing: L5 | Cells: 6022
→ Cell cycle scores computed
✅ L5 complete | 3000 HVGs
Processing: L6 | Cells: 5148
→ Cell cycle scores computed
✅ L6 complete | 3000 HVGs
Processing: L7 | Cells: 5331
→ Cell cycle scores computed
✅ L7 complete | 3000 HVGsnames(cell_line_list) <- cell_line_names # reuse saved names — no second SplitObject
# ── Select HVGs by cross-line frequency ───────────────────────────────────
all_hvg_lists <- lapply(cell_line_list, VariableFeatures)
hvg_freq <- sort(table(unlist(all_hvg_lists)), decreasing = TRUE)
n_hvgs <- min(3000, length(hvg_freq))
shared_hvgs <- names(hvg_freq)[1:n_hvgs]
cat("\nHVG selection:\n")
HVG selection:cat(" Variable in all", length(cell_line_list), "lines:",
sum(hvg_freq == length(cell_line_list)), "\n") Variable in all 7 lines: 689 Variable in ≥3 lines: 3271 Final HVG set: 3000 # ── Merge, scale, PCA ─────────────────────────────────────────────────────
MalignantCD4T <- merge(cell_line_list[[1]], y = cell_line_list[-1],
merge.data = TRUE)
VariableFeatures(MalignantCD4T) <- shared_hvgs
cat("\nRunning PCA on merged object...\n")
Running PCA on merged object...MalignantCD4T <- ScaleData(MalignantCD4T, features = shared_hvgs,
assay = "SCT", verbose = FALSE)
MalignantCD4T <- RunPCA(MalignantCD4T, features = shared_hvgs,
assay = "SCT", npcs = 30, verbose = FALSE)
cat("✅ Merged object ready\n")✅ Merged object ready Cells: 40695 HVGs: 3000 PCA dims: 30
L1 L2 L3 L4 L5 L6 L7
5825 5935 6428 6006 6022 5148 5331 used (Mb) gc trigger (Mb) max used (Mb)
Ncells 9453241 504.9 15929212 850.8 15929212 850.8
Vcells 1387730132 10587.6 4280400565 32656.9 5350467741 40820.92.3 variable genes all 7 cell line check
# Genes variable in ALL 7 lines
hvg_all7 <- names(hvg_freq[hvg_freq == 7])
cat("Genes variable in all 7 lines:", length(hvg_all7), "\n")Genes variable in all 7 lines: 689 [1] "AARS" "ABCC1" "ABHD3" "AC004687.1" "AC006064.4" "AC008105.3" "AC011603.2" "AC020916.1" "ACAT2" "ACLY" "ACTB"
[12] "ACTG1" "ACTN4" "ADA" "ADAM19" "ADGRE5" "AHNAK" "AHR" "AKAP13" "AL133415.1" "AL138963.4" "AL662797.1"
[23] "ALOX5AP" "ANKRD17" "ANKRD44" "ANLN" "ANXA1" "ANXA2" "ANXA6" "APP" "ARGLU1" "ARHGAP11A" "ARHGAP15"
[34] "ARHGDIB" "ARHGEF6" "ARL4C" "ARL6IP1" "ASF1B" "ASPM" "ATAD2" "ATP1A1" "ATP2A2" "ATP2B1" "ATP5MC1"
[45] "ATXN1" "AURKA" "AURKB" "B2M" "BACH2" "BCAT1" "BCL2" "BHLHE40" "BIRC3" "BIRC5" "BIRC6"
[56] "BLVRA" "BRCA1" "BRIP1" "BTG1" "BTG2" "BUB1" "BUB1B" "C12orf75" "C21orf58" "CALM1" "CALM2"
[67] "CALR" "CAMK4" "CANX" "CAPN2" "CARHSP1" "CASK" "CASP8" "CAST" "CBR3" "CCDC28B" "CCDC86"
[78] "CCL5" "CCNA2" "CCNB1" "CCNB2" "CCND2" "CCND3" "CCNE1" "CCNE2" "CCNF" "CCR7" "CCT5"
[89] "CD151" "CD3D" "CD48" "CD52" "CD55" "CD59" "CD69" "CD70" "CD74" "CD96" "CDC20"
[100] "CDC6" "CDCA2" "CDCA3" "CDCA5" "CDCA7" "CDCA8" "CDK1" "CDK2AP2" "CDK6" "CDKAL1" "CDKN1A"
[111] "CDKN2D" "CDKN3" "CDT1" "CEBPB" "CELF2" "CENPA" "CENPE" "CENPF" "CENPU" "CEP128" "CEP55"
[122] "CHAC1" "CHAF1A" "CHCHD10" "CHST11" "CISH" "CKAP2" "CKAP2L" "CKAP5" "CKS1B" "CKS2" "CLDND1"
[133] "CLSPN" "CLTC" "CNN2" "CORO1A" "CORO1B" "COTL1" "CRIP1" "CTSC" "CTSD" "CXCR4" "CYBA"
[144] "CYLD" "CYP51A1" "CYTH1" "CYTIP" "CYTOR" "DANCR" "DCTN1" "DDIT3" "DDIT4" "DDX21" "DDX3X"
[155] "DDX6" "DENND1B" "DENND4C" "DEPDC1" "DEPDC1B" "DIAPH3" "DLG1" "DLGAP5" "DNAJA1" "DNAJB1" "DOCK10"
[166] "DOCK2" "DOCK8" "DSCC1" "DTL" "DUSP2" "DYNLL1" "E2F1" "ECT2" "EEF1A1" "EEF2" "EFHD2"
[177] "EIF1" "EIF4G1" "ELMO1" "EMP3" "ENO1" "ERC1" "ERN1" "ESCO2" "ESYT1" "ESYT2" "ETS1"
[188] "EVL" "EXOC4" "EZH2" "FABP5" "FAM107B" "FAM111A" "FAM111B" "FAM83D" "FASN" "FBXL17" "FBXL20"
[199] "FBXO5" "FDFT1" "FEN1" "FKBP11" "FKBP4" "FKBP5" "FLI1" "FLNA" "FLOT1" "FNDC3A" "FOS"
[210] "FOXN3" "FOXP2" "FTL" "FTX" "FUS" "FXYD5" "FYN" "GABPB1-AS1" "GAPDH" "GARS" "GAS5"
[221] "GATA3" "GINS2" "GNG2" "GOLGB1" "GPHN" "GPR15" "GPR171" "GPRIN3" "GRN" "GSTP1" "GTSE1"
[232] "H1FX" "H2AFX" "H2AFZ" "H3F3B" "HCST" "HDAC9" "HELLS" "HERPUD1" "HIPK2" "HIST1H1A" "HIST1H1B"
[243] "HIST1H1C" "HIST1H1D" "HIST1H1E" "HIST1H2AC" "HIST1H2AE" "HIST1H2AG" "HIST1H2AL" "HIST1H2BC" "HIST1H2BJ" "HIST1H3B" "HIST1H3D"
[254] "HIST1H3H" "HIST1H3I" "HIST1H4C" "HIST2H2AB" "HIST2H2AC" "HIST2H2BF" "HJURP" "HLA-A" "HLA-B" "HLA-C" "HLA-E"
[265] "HMGB2" "HMGCR" "HMGCS1" "HMGN2" "HMMR" "HNRNPA3" "HNRNPAB" "HNRNPH1" "HNRNPU" "HNRNPUL2" "HP1BP3"
[276] "HPGD" "HSP90AA1" "HSP90AB1" "HSP90B1" "HSPA1A" "HSPA1B" "HSPA5" "HSPA8" "HSPA9" "HSPB1" "HSPD1"
[287] "HSPE1" "HSPH1" "HUWE1" "IARS" "ID2" "IDH2" "IDI1" "IER2" "IER3" "IFITM1" "IGF2R"
[298] "IKZF1" "IKZF2" "IL10RA" "IL2RB" "IL32" "IL4R" "IL9R" "ILF3-DT" "IMMP2L" "INCENP" "INPP4B"
[309] "INSIG1" "IPO5" "IQGAP1" "IRF1" "ISG20" "ITGA4" "ITGAL" "ITGB2" "ITGB7" "ITK" "ITM2B"
[320] "ITM2C" "JPT1" "JUN" "JUNB" "JUND" "KCNQ5" "KIF11" "KIF14" "KIF15" "KIF18B" "KIF20A"
[331] "KIF20B" "KIF21B" "KIF23" "KIF2C" "KIF4A" "KIFC1" "KLF6" "KMT2C" "KNL1" "KNSTRN" "KPNA2"
[342] "LARP1" "LASP1" "LAT" "LBR" "LCP1" "LCP2" "LDHA" "LDLR" "LDLRAD4" "LGALS1" "LIME1"
[353] "LINC00892" "LINC01572" "LMNA" "LMNB1" "LMO4" "LRBA" "LRPPRC" "LSP1" "LTA" "LTB" "LY6E"
[364] "LYST" "MACF1" "MALAT1" "MANF" "MAP3K8" "MARCKSL1" "MAT2A" "MBD5" "MBNL1" "MBP" "MCL1"
[375] "MCM10" "MCM2" "MCM3" "MCM4" "MCM5" "MCM6" "MCM7" "MDFIC" "MDN1" "MGST3" "MIB1"
[386] "MIF" "MIR4435-2HG" "MIS18BP1" "MKI67" "MKNK2" "MMP25" "MSH6" "MSI2" "MSMO1" "MSN" "MT-ATP6"
[397] "MT-ATP8" "MT-CO1" "MT-CO2" "MT-CO3" "MT-CYB" "MT-ND1" "MT-ND2" "MT-ND3" "MT-ND4" "MT-ND4L" "MT-ND5"
[408] "MT-ND6" "MT1X" "MT2A" "MTHFD2" "MTRNR2L12" "MXD3" "MYB" "MYC" "MYH9" "MYL6" "MYO1F"
[419] "MYO1G" "MZB1" "NAMPT" "NCAPD2" "NCAPG" "NCAPG2" "NCL" "NCOA3" "NDC80" "NEAT1" "NEIL3"
[430] "NEK2" "NEK7" "NFAT5" "NFATC2" "NFE2L3" "NFKB1" "NFKBIA" "NIBAN1" "NINJ1" "NKTR" "NME1"
[441] "NOLC1" "NOP16" "NORAD" "NPM1" "NQO1" "NR3C1" "NSD2" "NSMCE2" "NUCB2" "NUDT8" "NUF2"
[452] "NUFIP2" "NUMA1" "NUSAP1" "ODC1" "OPTN" "OSBPL3" "OSTF1" "P2RY8" "PALM2-AKAP2" "PARP14" "PCLAF"
[463] "PCNA" "PDE3B" "PDE4D" "PDE7A" "PGAM1" "PGK1" "PHACTR2" "PHF19" "PHLDA1" "PIK3CD" "PIK3R1"
[474] "PIM1" "PIM2" "PIM3" "PKM" "PKMYT1" "PLAAT4" "PLEC" "PLK1" "PLP2" "PLPP1" "PMAIP1"
[485] "PNN" "PNRC1" "POLR2A" "PPDPF" "PPP1R15A" "PPP3CA" "PRC1" "PRDX1" "PREX1" "PRKCA" "PRKDC"
[496] "PRNP" "PRR11" "PSAT1" "PTMA" "PTMS" "PTPN6" "PTPN7" "PTPRC" "PTTG1" "PUM3" "PUS7"
[507] "PVT1" "PYCARD" "RAB11FIP1" "RAB37" "RABGAP1L" "RACGAP1" "RAD21" "RAD51B" "RASGRP1" "RBL1" "RBM38"
[518] "RBPJ" "RCC2" "RCSD1" "REEP5" "RELB" "RERE" "RHBDD2" "RHOC" "RNF213" "RORA" "RPL10"
[529] "RPL11" "RPL12" "RPL13" "RPL19" "RPL22L1" "RPL32" "RPL41" "RPLP0" "RPLP1" "RPS12" "RPS14"
[540] "RPS18" "RPS2" "RPS23" "RPS3" "RPS3A" "RPS6KA5" "RPS8" "RRM2" "RSRP1" "RUNX3" "S100A10"
[551] "S100A11" "S100A4" "S100A6" "S100P" "SAC3D1" "SAMD9" "SASH3" "SCLT1" "SCPEP1" "SDCBP" "SEC14L1"
[562] "SELENOW" "SELPLG" "SEMA4D" "SEPTIN9" "SERPINB1" "SETX" "SFPQ" "SGO2" "SH2D2A" "SH3BGRL3" "SH3BP1"
[573] "SIK3" "SIT1" "SKAP1" "SLBP" "SLC16A1-AS1" "SLC1A5" "SLC20A1" "SLC25A32" "SLC2A3" "SLC38A2" "SLC3A2"
[584] "SLC43A3" "SLC4A7" "SLC7A5" "SLC9A3R1" "SLFN12L" "SMARCA2" "SMC1A" "SMC4" "SMG1" "SMYD3" "SNHG12"
[595] "SNHG15" "SNHG3" "SNHG7" "SNHG8" "SNRNP200" "SOCS1" "SORL1" "SOS1" "SP140" "SPAG5" "SPIDR"
[606] "SPOCK2" "SPTAN1" "SPTBN1" "SQLE" "SQSTM1" "SREBF2" "SRGN" "SRM" "SRRT" "SRSF7" "SSBP2"
[617] "ST8SIA4" "STAT1" "STAT3" "STAT4" "STK10" "STK17B" "STMN1" "SUN2" "SYNE2" "SYTL3" "TACC3"
[628] "TAF15" "TAGLN2" "TBC1D5" "TBL1X" "TCF12" "TCP1" "TFRC" "TIMP1" "TK1" "TMBIM1" "TMEM173"
[639] "TMPO" "TMSB10" "TMSB4X" "TMTC2" "TMX4" "TNFRSF1B" "TNFSF10" "TNIK" "TOP2A" "TPM4" "TPX2"
[650] "TRAF1" "TRAF3IP3" "TRBV20-1" "TRG-AS1" "TRIM44" "TRIM56" "TRIM59" "TRIO" "TROAP" "TSC22D3" "TTK"
[661] "TUBA1A" "TUBA1B" "TUBA1C" "TUBA4A" "TUBB" "TUBB4B" "TYMS" "UBALD2" "UBC" "UBE2C" "UBE2S"
[672] "UBR4" "UCP2" "UHRF1" "UNG" "VIM" "WARS" "WDR76" "WWOX" "XBP1" "YWHAG" "ZC3HAV1"
[683] "ZEB1" "ZFAND3" "ZFAS1" "ZFP36" "ZFP36L1" "ZFP36L2" "ZYX" # Check which canonical T cell markers are in the all-7 set
t_cell_markers <- c(
# Naive/memory
"CCR7", "SELL", "TCF7", "IL7R", "LEF1", "KLF2",
# Activation/exhaustion
"TOX", "PDCD1", "LAG3", "TIGIT", "CTLA4", "HAVCR2",
# Effector
"GZMB", "GZMK", "GZMA", "PRF1", "IFNG", "TNF",
# Treg
"FOXP3", "IL2RA", "IKZF2", "CTLA4",
# Sézary specific
"KIR3DL2", "PLS3", "TWIST1", "EPHA4", "CD164",
# Proliferation
"MKI67", "TOP2A", "CDK1"
)
found_in_all7 <- intersect(t_cell_markers, hvg_all7)
cat("\nCanonical markers in all-7 HVG set:\n")
Canonical markers in all-7 HVG set:[1] "CCR7" "IKZF2" "MKI67" "TOP2A" "CDK1"
Markers NOT in all-7 set: [1] "SELL" "TCF7" "IL7R" "LEF1" "KLF2" "TOX" "PDCD1" "LAG3" "TIGIT" "CTLA4" "HAVCR2" "GZMB" "GZMK" "GZMA" "PRF1" "IFNG"
[17] "TNF" "FOXP3" "IL2RA" "KIR3DL2" "PLS3" "TWIST1" "EPHA4" "CD164" 2.4 variable genes all 7 cell line check
# Check what junk genes are in your current HVG set
# before find-anchors runs
cat("=== Junk gene check on shared_hvgs ===\n")=== Junk gene check on shared_hvgs ===mt_in_hvgs <- shared_hvgs[grepl("^MT-", shared_hvgs)]
cat("MT genes in shared_hvgs:", length(mt_in_hvgs), "\n")MT genes in shared_hvgs: 13 [1] "MT-ATP6" "MT-ATP8" "MT-CO1" "MT-CO2" "MT-CO3" "MT-CYB" "MT-ND1" "MT-ND2" "MT-ND3" "MT-ND4" "MT-ND4L" "MT-ND5" "MT-ND6" ribo_in_hvgs <- shared_hvgs[grepl("^RPL|^RPS", shared_hvgs)]
cat("\nRibosomal genes in shared_hvgs:", length(ribo_in_hvgs), "\n")
Ribosomal genes in shared_hvgs: 53 [1] "RPL10" "RPL11" "RPL12" "RPL13" "RPL19" "RPL22L1" "RPL32" "RPL41" "RPLP0" "RPLP1" "RPS12" "RPS14" "RPS18" "RPS2" "RPS23" "RPS3"
[17] "RPS3A" "RPS6KA5" "RPS8" "RPL13A" "RPL18A" "RPL28" "RPL29" "RPL35A" "RPL5" "RPL7A" "RPL8" "RPS13" "RPS6" "RPS6KA3" "RPS7" "RPL17"
[33] "RPL23" "RPL30" "RPL35" "RPL6" "RPL7" "RPS15A" "RPS27L" "RPS4X" "RPL14" "RPL23A" "RPL26" "RPL27A" "RPL3" "RPL37" "RPL4" "RPS17"
[49] "RPS20" "RPS24" "RPS27A" "RPS6KA1" "RPS6KC1"hsp_in_hvgs <- shared_hvgs[grepl("^HSP|^HSPA|^HSPB", shared_hvgs)]
cat("\nHeat shock genes in shared_hvgs:", length(hsp_in_hvgs), "\n")
Heat shock genes in shared_hvgs: 13 [1] "HSP90AA1" "HSP90AB1" "HSP90B1" "HSPA1A" "HSPA1B" "HSPA5" "HSPA8" "HSPA9" "HSPB1" "HSPD1" "HSPE1" "HSPH1" "HSPA4" snhg_in_hvgs <- shared_hvgs[grepl("^SNHG|MALAT1|NEAT1", shared_hvgs)]
cat("\nlncRNA genes in shared_hvgs:", length(snhg_in_hvgs), "\n")
lncRNA genes in shared_hvgs: 15 [1] "MALAT1" "NEAT1" "SNHG12" "SNHG15" "SNHG3" "SNHG7" "SNHG8" "SNHG1" "SNHG29" "SNHG16" "SNHG25" "SNHG17" "SNHG30" "SNHG32" "SNHG5" cat("\nTotal junk genes to be filtered:",
length(mt_in_hvgs) + length(ribo_in_hvgs) +
length(hsp_in_hvgs) + length(snhg_in_hvgs), "\n")
Total junk genes to be filtered: 94 3 Reference Trajectory (Monocle3)
3.1 Build CDS from Reference
=== Building Monocle3 CDS ===cds <- as.cell_data_set(reference_integrated)
# Transfer frozen UMAP coordinates — these define the trajectory space
reducedDim(cds, "UMAP") <- Embeddings(reference_integrated, "umap")
# Single partition: one connected graph across all healthy cells.
# CD4 Proliferating cells have been removed so no spurious cell-cycle
# branch will pull the graph away from the differentiation axis.
partition_vec <- setNames(factor(rep(1L, ncol(cds))), colnames(cds))
cds@clusters$UMAP$partitions <- partition_vec
cluster_vec <- setNames(
factor(reference_integrated$seurat_clusters[colnames(cds)]),
colnames(cds)
)
cds@clusters$UMAP$clusters <- cluster_vec
# Transfer metadata
colData(cds)$cell_line <- reference_integrated$orig.ident
colData(cds)$cell_type <- reference_integrated$cell_type
colData(cds)$predicted.celltype.l2 <- reference_integrated$predicted.celltype.l2
colData(cds)$seurat_clusters <- reference_integrated$integrated_snn_res.0.2
if ("orig.ident" %in% colnames(reference_integrated@meta.data))
colData(cds)$sample <- reference_integrated$orig.ident
# Validate all required slots are named correctly
stopifnot(
"clusters must be named" = !is.null(names(cds@clusters$UMAP$clusters)),
"partitions must be named" = !is.null(names(cds@clusters$UMAP$partitions)),
"cluster length matches" = length(cds@clusters$UMAP$clusters) == ncol(cds),
"partition length matches" = length(cds@clusters$UMAP$partitions) == ncol(cds)
)
cat("CDS built:", ncol(cds), "cells\n")CDS built: 11466 cellsClusters: 8 Partitions: 1 Azimuth l2 breakdown:
CD4 Naive CD4 TCM CD4 TEM CD4 Temra/CTL Treg
2037 9067 145 10 207 3.2 Learn Trajectory Graph — Two Lineages
# ── Learn principal graph ──────────────────────────────────────────────────
# Goal: recover two biological lineages as a single connected graph:
# Lineage 1: Naive → TCM → TEM → Temra/CTL (effector axis)
# Lineage 2: Naive → TCM → Treg (regulatory branch)
#
# use_partition = FALSE: single connected graph (no partition forcing)
# close_loop = FALSE: open trajectory (no loop back)
# minimal_branch_len = 5: short enough to detect the Treg branch
# — if Treg branch not detected, reduce to 3
# — if too many spurious branches, increase to 8
#
# NOTE: nn.k is NOT a valid learn_graph_control parameter in monocle3.
# FIX: removed nn.k — monocle3 handles neighbourhood structure internally.
# The key sensitivity parameter is minimal_branch_len.
set.seed(42)
cds <- learn_graph(
cds,
use_partition = FALSE,
close_loop = FALSE,
learn_graph_control = list(
minimal_branch_len = 10,
# ncenter = 100, # default is ~250 — reduce to simplify graph
orthogonal_proj_tip = FALSE
),
verbose = TRUE
)
cat("\n✅ Principal graph learned\n")
✅ Principal graph learnedNodes: 349 n_branch_points <- sum(igraph::degree(principal_graph(cds)$UMAP) > 2)
cat("Branch points:", n_branch_points, "\n")Branch points: 18 if (n_branch_points == 0) {
warning(
"❌ No branch point — Treg lineage not separated.\n",
" Re-run learn_graph with minimal_branch_len = 3"
)
} else if (n_branch_points > 3) {
warning(
"⚠️ Too many branch points (", n_branch_points, ").\n",
" Re-run learn_graph with minimal_branch_len = 8"
)
} else {
cat("✅ Branch structure looks correct (1-3 branch points)\n")
}
# ── Trajectory visualisation ──────────────────────────────────────────────
umap_coords <- as.data.frame(Embeddings(reference_integrated, "umap"))
colnames(umap_coords) <- c("UMAP1", "UMAP2")
umap_coords$l2 <- reference_integrated$predicted.celltype.l2
umap_coords$cell_type <- reference_integrated$cell_type
label_l2 <- umap_coords %>%
group_by(l2) %>%
summarise(UMAP1 = median(UMAP1), UMAP2 = median(UMAP2), .groups = "drop")
label_ct <- umap_coords %>%
group_by(cell_type) %>%
summarise(UMAP1 = median(UMAP1), UMAP2 = median(UMAP2), .groups = "drop")
p_graph_l2 <- plot_cells(
cds,
color_cells_by = "predicted.celltype.l2",
label_groups_by_cluster = FALSE,
label_leaves = FALSE,
label_branch_points = TRUE,
cell_size = 0.7,
show_trajectory_graph = TRUE
) +
scale_color_manual(values = azimuth_l2_colors, name = "State") +
geom_label_repel(data = label_l2,
aes(x = UMAP1, y = UMAP2, label = l2),
size = 4, fontface = "bold", fill = "white", alpha = 0.85,
box.padding = 0.5, segment.color = "grey40",
inherit.aes = FALSE) +
ggtitle("Monocle3 Graph: Azimuth l2\n(Two lineages: effector axis + Treg branch)") +
theme(plot.title = element_text(hjust = 0.5, size = 13, face = "bold"),
legend.position = "none")
p_graph_ct <- plot_cells(
cds,
color_cells_by = "cell_type",
label_groups_by_cluster = FALSE,
label_leaves = FALSE,
label_branch_points = TRUE,
cell_size = 0.7,
show_trajectory_graph = TRUE
) +
geom_label_repel(data = label_ct,
aes(x = UMAP1, y = UMAP2, label = cell_type),
size = 3.5, fontface = "bold", fill = "white", alpha = 0.85,
box.padding = 0.5, segment.color = "grey40",
inherit.aes = FALSE) +
ggtitle("Monocle3 Graph: Cell Types") +
theme(plot.title = element_text(hjust = 0.5, size = 13, face = "bold"),
legend.position = "none")
p_graph_l2 | p_graph_ct
3.3 Set Root & Order Cells by Pseudotime
Azimuth l2 labels in CDS:[1] CD4 Naive CD4 TCM CD4 TEM CD4 Temra/CTL Treg
Levels: CD4 Naive CD4 TCM CD4 TEM CD4 Temra/CTL Treg# ── Identify naive cells ───────────────────────────────────────────────────
naive_ids <- rownames(colData(cds))[
grepl("naive|Naive|TN$", colData(cds)$predicted.celltype.l2,
ignore.case = TRUE) |
grepl("Tnaive", as.character(colData(cds)$cell_type),
ignore.case = FALSE)
]
cat("\nNaive root cells:", length(naive_ids), "\n")
Naive root cells: 5739 if (length(naive_ids) == 0)
stop("❌ No naive cells found. Check label patterns above.")
# ── Find root node by minimum distance to naive centroid ──────────────────
# Using centroid distance (not most-populated node) is more robust:
# the most-populated node biases toward the densest part of the naive
# cluster which may not be the earliest point on the trajectory.
naive_umap <- Embeddings(reference_integrated, "umap")[naive_ids, ]
naive_centroid <- colMeans(naive_umap)
pr_nodes <- t(cds@principal_graph_aux[["UMAP"]]$dp_mst)
node_dists <- rowSums(
(pr_nodes - matrix(naive_centroid,
nrow = nrow(pr_nodes), ncol = 2,
byrow = TRUE))^2
)
root_node <- names(which.min(node_dists))
cat("Root node selected:", root_node, "\n")Root node selected: Y_256 Root coords: (-3.424, -0.886) Naive centroid: (-2.865, -0.651)# ── Order cells ────────────────────────────────────────────────────────────
cds <- order_cells(cds, root_pr_nodes = root_node)
# Transfer pseudotime back to Seurat object
reference_integrated$monocle3_pseudotime <- pseudotime(cds)
reference_integrated$monocle3_pseudotime[
!is.finite(reference_integrated$monocle3_pseudotime)] <- NA
cat("\nPseudotime summary (reference):\n")
Pseudotime summary (reference):print(summary(reference_integrated$monocle3_pseudotime[
is.finite(reference_integrated$monocle3_pseudotime)])) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.226 9.459 11.396 18.602 25.553 # ══════════════════════════════════════════════════════════════════════════
# TOPOLOGY VALIDATION
# ══════════════════════════════════════════════════════════════════════════
# Correct order for each lineage:
# Effector: Naive < TCM < TEM < Temra
# Treg: Naive < TCM — Treg (Treg branches from TCM, NOT after TEM)
#
# This is a hard biological requirement. If Treg > TEM in pseudotime,
# the graph has placed Treg downstream of TEM which is anatomically wrong.
# Treg differentiates from TCM-stage precursors via FOXP3 induction,
# not from terminally differentiated TEM/Temra cells.
pt_check <- reference_integrated@meta.data %>%
filter(is.finite(monocle3_pseudotime)) %>%
group_by(predicted.celltype.l2) %>%
summarise(
n = n(),
med_pt = round(median(monocle3_pseudotime, na.rm = TRUE), 3),
mean_pt = round(mean(monocle3_pseudotime, na.rm = TRUE), 3),
.groups = "drop"
) %>%
arrange(med_pt)
cat("\n=== Pseudotime order per state (must be Naive < TCM < Treg < TEM < Temra) ===\n")
=== Pseudotime order per state (must be Naive < TCM < Treg < TEM < Temra) ===
# Extract state medians — used for bin boundaries downstream
naive_med <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "CD4 Naive"]
tcm_med <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "CD4 TCM"]
treg_med <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "Treg"]
tem_med <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "CD4 TEM"]
temra_med <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "CD4 Temra/CTL"]
# FIX: single-string sprintf — original had two format strings which
# caused the second string to be silently ignored
cat(sprintf(
"\nState medians: Naive=%.2f | TCM=%.2f | Treg=%.2f | TEM=%.2f | Temra=%.2f\n",
naive_med, tcm_med, treg_med, tem_med, temra_med
))
State medians: Naive=3.99 | TCM=13.36 | Treg=18.75 | TEM=24.78 | Temra=25.25# Hard stops — script will not continue if topology is biologically wrong
if (naive_med > tcm_med)
stop(sprintf(
"❌ TOPOLOGY ERROR: Naive (%.2f) > TCM (%.2f).\n Root node may be misplaced — check naive_centroid coordinates.",
naive_med, tcm_med))
if (tcm_med > tem_med)
stop(sprintf(
"❌ TOPOLOGY ERROR: TCM (%.2f) > TEM (%.2f).\n Re-check root node selection.",
tcm_med, tem_med))
if (treg_med > tem_med)
stop(sprintf(
"❌ TOPOLOGY ERROR: Treg (%.2f) > TEM (%.2f).\n Treg must branch from TCM, not be downstream of TEM.\n Fix: re-run learn_graph with minimal_branch_len = 3",
treg_med, tem_med))
# FIX: single sprintf format string (was split across two strings before)
cat(sprintf(
"✅ Topology confirmed: Naive(%.2f) < TCM(%.2f) < Treg(%.2f) < TEM(%.2f) < Temra(%.2f)\n",
naive_med, tcm_med, treg_med, tem_med, temra_med
))✅ Topology confirmed: Naive(3.99) < TCM(13.36) < Treg(18.75) < TEM(24.78) < Temra(25.25)# ── Pseudotime UMAP plots ─────────────────────────────────────────────────
plot_cells(
cds,
color_cells_by = "pseudotime",
label_cell_groups = FALSE,
cell_size = 0.8,
show_trajectory_graph = TRUE
) +
scale_color_viridis_c(option = "plasma", name = "Pseudotime") +
ggtitle("Reference Pseudotime\n(Root = CD4 Naive | Two lineages: TEM/Temra + Treg)") +
theme(plot.title = element_text(hjust = 0.5, size = 13, face = "bold"))
3.4 Validate Pseudotime Against Cell Type
# Visual validation that pseudotime order matches expected biology.
# Both violin and ridge plots should show:
# Naive (lowest) → TCM → Treg (branch, intermediate) → TEM → Temra (highest)
pt_data <- data.frame(
pseudotime = reference_integrated$monocle3_pseudotime,
l2 = reference_integrated$predicted.celltype.l2
) %>% filter(is.finite(pseudotime), !is.na(l2))
# Order x-axis by median pseudotime — should give biological order
l2_order <- pt_data %>%
group_by(l2) %>%
summarise(med_pt = median(pseudotime, na.rm = TRUE), .groups = "drop") %>%
arrange(med_pt) %>%
pull(l2)
pt_data$l2 <- factor(pt_data$l2, levels = l2_order)
p_violin <- ggplot(pt_data, aes(x = l2, y = pseudotime, fill = l2)) +
geom_violin(scale = "width", alpha = 0.85, trim = TRUE) +
geom_boxplot(width = 0.12, fill = "white", outlier.size = 0.3) +
scale_fill_manual(values = azimuth_l2_colors, na.value = "grey70") +
theme_classic(base_size = 11) +
theme(axis.text.x = element_text(angle = 40, hjust = 1, size = 9),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold")) +
labs(title = "Reference Pseudotime per Azimuth l2 Label",
subtitle = "Expected order: Naive < TCM < Treg < TEM < Temra",
x = "", y = "Monocle3 Pseudotime")
p_ridge <- ggplot(pt_data, aes(x = pseudotime, y = l2, fill = l2)) +
geom_density_ridges(alpha = 0.75, scale = 1.2, rel_min_height = 0.01) +
scale_fill_manual(values = azimuth_l2_colors, na.value = "grey70") +
theme_classic(base_size = 10) +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold")) +
labs(title = "Reference Pseudotime Density by Azimuth l2 Label",
x = "Pseudotime", y = "")
p_violin | p_ridge
# ════════════════════════════════════════════════════════════════════════════════
# CRITICAL FIX: Rebuild reference PCA in SCT space (preserves UMAP trajectory)
# PROBLEM: reference_integrated PCA = "integrated" assay (50 dims)
# MalignantCD4T PCA = "SCT" assay (30 dims)
# SOLUTION: SCT + PCA on reference → unified SCT space for FindTransferAnchors
# UMAP MODEL 100% SAFE — lives in reductions$umap@misc$model
# ════════════════════════════════════════════════════════════════════════════════
cat("=== DIAGNOSTIC: Current reference state ===\n")=== DIAGNOSTIC: Current reference state ===DefaultAssay: SCT SCT HVGs: 0 PCA dims: 50 UMAP model present: TRUE # 1. Sanity check UMAP before
old_umap_coords <- Embeddings(reference_integrated, "umap")[1:3, ]
old_umap_model <- !is.null(reference_integrated@reductions$umap@misc$model)
cat("\nUMAP first 3 cells (backup):", paste(round(old_umap_coords[1,], 3), collapse=" "), "\n")
UMAP first 3 cells (backup): -2.072 -1.749 # 2. SCT on reference (creates SCT assay + HVGs)
DefaultAssay(reference_integrated) <- "RNA"
reference_integrated <- SCTransform(
reference_integrated,
variable.features.n = 3000,
vst.flavor = "v2",
vars.to.regress = c("percent.mt", "S.Score", "G2M.Score"),
verbose = FALSE
)
# 3. PCA in SCT space (matches MalignantCD4T exactly)
reference_integrated <- RunPCA(
reference_integrated,
assay = "SCT",
npcs = 30, # ← Match query dims exactly
verbose = FALSE
)
# 4. Verify fix
cat("\n=== FIXED: New reference state ===\n")
=== FIXED: New reference state ===DefaultAssay: SCT SCT HVGs: 3000 PCA dims: 30 UMAP model preserved: TRUE # 5. UMAP integrity check
new_umap_coords <- Embeddings(reference_integrated, "umap")[1:3, ]
cat("UMAP unchanged:", all(old_umap_coords == new_umap_coords), "\n")UMAP unchanged: TRUE ✅ Reference now in SCT space — FindTransferAnchors will work for ALL 7 lines4 Prepare Reference for MapQuery
# QUICK PRE-FLIGHT CHECKS (run these 2 lines now)
cat("SCT HVGs:", length(VariableFeatures(reference_integrated, assay = "SCT")), "\n")SCT HVGs: 3000 PCA dims: 30 5 Map Malignant Cells onto Reference
5.1 Find Transfer Anchors
# ══════════════════════════════════════════════════════════════════════════
# FIND TRANSFER ANCHORS
# ══════════════════════════════════════════════════════════════════════════
# Anchors are mutual nearest neighbours between reference and query in
# shared PCA space. Their quality determines the accuracy of:
# (a) label transfer → which normal state does this Sézary cell resemble?
# (b) pseudotime transfer → where along the differentiation axis is it?
#
# FEATURE SELECTION STRATEGY:
# Step 1 — intersect reference + query HVGs
# Step 2 — remove technical/junk genes (MT, ribosomal, HSP, lncRNA, histone)
# WHY: these genes are variable due to library quality and stress,
# NOT due to T cell differentiation state. Keeping them biases
# anchors toward batch effects rather than biology.
# Step 3 — rank remaining genes by joint residual variance (ref + query)
# WHY: genes highly variable in BOTH datasets are the most
# reliable for finding true biological nearest neighbours
# ══════════════════════════════════════════════════════════════════════════
DefaultAssay(reference_integrated) <- "SCT"
DefaultAssay(MalignantCD4T) <- "SCT"
# ── Step 1: Intersect reference and query HVGs ────────────────────────────
ref_hvgs <- VariableFeatures(reference_integrated, assay = "SCT")
if (length(ref_hvgs) == 0) {
cat("⚠️ No HVGs stored in reference — extracting from scale.data\n")
ref_hvgs <- rownames(GetAssayData(reference_integrated, assay = "SCT",
layer = "scale.data"))
}
query_hvgs <- VariableFeatures(MalignantCD4T)
common_features <- intersect(ref_hvgs, query_hvgs)
cat("=== Feature set construction ===\n")=== Feature set construction ===Reference HVGs : 3000 Query HVGs : 3000 Common HVGs : 1443 if (length(common_features) < 1500)
warning(paste0("⚠️ Only ", length(common_features),
" common features — below recommended 1500.\n",
" Increase nfeatures in SCTransform to 4000."))
# ── Step 2: Remove technical/junk genes ───────────────────────────────────
# Categories removed and WHY:
# ^MT- : mitochondrial OXPHOS genes — variable due to cell quality/stress
# ^RPL/S : ribosomal proteins — variable due to translation rate/library depth
# ^HSP* : heat shock proteins — variable due to stress, not differentiation
# ^SNHG/MALAT1/NEAT1 : lncRNAs — nuclear architecture, not T cell state
# ^HIST : replication-dependent histones — S-phase markers (cell cycle)
# NOTE: ^HIST added based on pre-run QC showing 20+ histone genes
# in shared HVG set, all driven by cycling subpopulation
junk_pattern <- paste0(
"^RPL|^RPS|", # ribosomal large + small subunits
"^MT-|", # mitochondrial genome (OXPHOS complexes I,III,IV,V)
"^HSP|^HSPA|^HSPB|", # heat shock proteins
"^HSPD|^HSPE|^HSPH|", # heat shock proteins (continued)
"^SNHG|", # small nucleolar RNA host genes
"^MALAT1|^NEAT1|", # abundant nuclear lncRNAs
"^XIST|", # X-inactivation (sex-specific batch)
"^HIST" # replication-dependent histones (S-phase signal)
)
# Report breakdown by category before removing
mt_removed <- sum(grepl("^MT-", common_features))
ribo_removed <- sum(grepl("^RPL|^RPS", common_features))
hsp_removed <- sum(grepl("^HSP", common_features))
lnc_removed <- sum(grepl("^SNHG|^MALAT1|^NEAT1|^XIST", common_features))
hist_removed <- sum(grepl("^HIST", common_features))
cat("\n=== Junk gene removal breakdown ===\n")
=== Junk gene removal breakdown === Mitochondrial (OXPHOS) : 13 Ribosomal (RPL/RPS) : 32 Heat shock (HSP*) : 12 lncRNA (SNHG/MALAT1/NEAT1): 12 Histones (HIST*) : 10n_before <- length(common_features)
common_features_clean <- common_features[!grepl(junk_pattern, common_features)]
n_removed <- n_before - length(common_features_clean)
cat(sprintf(" ─────────────────────────────\n")) ───────────────────────────── Total removed : 79 (5.5%) Clean features remaining : 1364if (length(common_features_clean) < 1200)
warning("⚠️ Fewer than 1200 clean features — anchor quality may be reduced.")
# ── Step 3: Rank by joint residual variance ───────────────────────────────
# Genes that are highly variable in BOTH the healthy reference AND the
# malignant query are the most informative for finding true biological
# nearest neighbours. Ranking by joint variance ensures differentiation
# genes (CCR7, TCF7, GZMK, FOXP3) rank above line-specific noise.
ref_var_meta <- reference_integrated[["SCT"]]@meta.features
query_var_meta <- MalignantCD4T[["SCT"]]@meta.features
if ("residual_variance" %in% colnames(ref_var_meta) &&
"residual_variance" %in% colnames(query_var_meta)) {
ref_rv <- ref_var_meta[common_features_clean, "residual_variance"]
query_rv <- query_var_meta[common_features_clean, "residual_variance"]
ref_rv[is.na(ref_rv)] <- 0
query_rv[is.na(query_rv)] <- 0
mean_rv <- (ref_rv + query_rv) / 2
common_features <- common_features_clean[order(mean_rv, decreasing = TRUE)]
cat("Top 20 features by joint residual variance:\n")
print(head(common_features, 20))
cat("\n")
} else {
cat("⚠️ residual_variance not in SCT meta.features — using unranked clean list\n")
cat(" This is OK but ranking would improve anchor quality.\n")
common_features <- common_features_clean
}⚠️ residual_variance not in SCT meta.features — using unranked clean list
This is OK but ranking would improve anchor quality.# ── Step 4: Canonical T cell marker check ─────────────────────────────────
# These markers span the full differentiation axis and Treg branch.
# If fewer than 5 are present, the feature set cannot distinguish
# differentiation states and anchor quality will be poor.
markers_check <- c(
"CCR7", "SELL", "TCF7", "IL7R", # Naive/TCM
"GZMB", "GZMK", "PRF1", "EOMES", # Effector/TEM
"FOXP3", "IKZF2", # Treg
"TOX", "PDCD1", "LAG3", # Exhaustion
"CD44", "TBX21" # Activation
)
found <- markers_check[markers_check %in% common_features]
not_found <- markers_check[!markers_check %in% common_features]
cat("=== Canonical T cell marker check ===\n")=== Canonical T cell marker check ===Present ( 8 ): CCR7, SELL, TCF7, IL7R, PRF1, IKZF2, TOX, CD44 Absent ( 7 ): GZMB, GZMK, EOMES, FOXP3, PDCD1, LAG3, TBX21 if (length(found) < 5) {
warning("⚠️ Fewer than 5 canonical markers present — anchor quality at risk.")
} else if (length(found) < 8) {
cat("⚠️ Some markers absent — likely heterogeneous across lines (expected)\n")
cat(" Projection will still work but interpret per-line results carefully\n")
} else {
cat("✅ Good marker coverage — feature set captures differentiation axis\n")
}✅ Good marker coverage — feature set captures differentiation axis# ── Step 5: FindTransferAnchors ───────────────────────────────────────────
# k.anchor = 10 : neighbours used to find anchors — balanced for ~11k ref
# k.filter = 500 : neighbours for anchor filtering — removes weak anchors
# k.score = 30 : neighbours for anchor scoring — weights anchor quality
# dims = 1:30: use all 30 PCA dims — captures full differentiation space
dims_to_use <- min(30,
ncol(Embeddings(reference_integrated, "pca")),
ncol(Embeddings(MalignantCD4T, "pca")))
cat("\nFinding anchors with dims 1:", dims_to_use, "\n")
Finding anchors with dims 1: 30 Features used: 1364 anchors <- FindTransferAnchors(
reference = reference_integrated,
query = MalignantCD4T,
features = common_features,
normalization.method = "SCT",
reference.reduction = "pca",
dims = 1:dims_to_use,
k.anchor = 10,
k.filter = 500,
k.score = 30,
verbose = TRUE
)
# ── Step 6: Anchor quality check ──────────────────────────────────────────
# NOTE: In Seurat FindTransferAnchors internal convention:
# cell1 = reference indices
# cell2 = query indices (OPPOSITE to what variable names suggest)
# Always use cell1→reference and cell2→query for barcode mapping.
anchor_df <- as.data.frame(slot(anchors, "anchors"))
# CORRECT barcode mapping — cell1=reference, cell2=query
ref_barcodes <- colnames(reference_integrated)
query_barcodes <- colnames(MalignantCD4T)
anchor_df$ref_barcode <- ref_barcodes[anchor_df$cell1]
anchor_df$query_barcode <- query_barcodes[anchor_df$cell2]
anchor_df$query_cellline <- MalignantCD4T$cell_line[anchor_df$query_barcode]
anchor_df$ref_label <- reference_integrated$predicted.celltype.l2[anchor_df$ref_barcode]
# Verify no NAs
cat("\n=== Barcode mapping validation ===\n")
=== Barcode mapping validation ===NA ref barcodes : 0 NA query barcodes: 0 NA cell lines : 0 n_anchors <- nrow(anchor_df)
anchor_ratio <- ncol(MalignantCD4T) / n_anchors
anchor_scores <- anchor_df$score
cat("\n=== Anchor quality summary ===\n")
=== Anchor quality summary ===Anchors found : 8021 Malignant cells : 40695 Reference cells : 11466 Cells per anchor : 5.1 : 1 (ideal ≤ 8:1)cat(sprintf("Anchor scores : mean=%.3f | median=%.3f | min=%.3f | max=%.3f\n",
mean(anchor_scores), median(anchor_scores),
min(anchor_scores), max(anchor_scores)))Anchor scores : mean=0.535 | median=0.500 | min=0.000 | max=1.000
=== Anchors per cell line ===
L1 L2 L3 L4 L5 L6 L7
2062 1699 869 1025 706 926 734
=== Anchors by reference label ===anchor_df %>%
group_by(ref_label) %>%
summarise(n_anchors = n(),
mean_score = round(mean(score), 3),
.groups = "drop") %>%
arrange(desc(n_anchors)) %>%
print()
if (anchor_ratio > 8) {
warning(paste0(
"⚠️ Low anchor density (", round(anchor_ratio, 1), ":1).\n",
" 1. Junk genes still dominating PCA — check top 20 features above\n",
" 2. Sézary cells too transcriptionally distant from reference\n",
" 3. Increase k.anchor to 15 and rerun"
))
} else if (anchor_ratio > 5) {
cat(sprintf("⚠️ Moderate anchor density (%.1f:1) — acceptable\n", anchor_ratio))
} else {
cat(sprintf("✅ Good anchor density (%.1f:1) — projection quality should be high\n",
anchor_ratio))
}⚠️ Moderate anchor density (5.1:1) — acceptable5.2 check you can do after MapQuery:
# ── ANCHOR DIAGNOSTIC: Biology vs Artefact ────────────────────────────────
# If L3-L7 are truly dedifferentiated, their PCA space should be
# DISTANT from the reference. If it is a technical issue, they will
# sit in the same PCA space as L1/L2 but just not form mutual NN.
cat("=== PCA space comparison: L1/L2 vs L3-L7 ===\n")=== PCA space comparison: L1/L2 vs L3-L7 ===# Get reference PCA centroid (TCM region)
ref_pca <- Embeddings(reference_integrated, "pca")[, 1:10]
ref_centroid <- colMeans(ref_pca)
# Get query PCA embeddings per line
query_pca <- Embeddings(MalignantCD4T, "pca")[, 1:10]
# Distance from each query cell to reference centroid
dist_to_ref <- rowSums(
sweep(query_pca, 2, ref_centroid, "-")^2
)
# Compare distances by cell line
dist_df <- data.frame(
cell = colnames(MalignantCD4T),
cell_line = MalignantCD4T$cell_line,
dist_to_ref = dist_to_ref
)
dist_summary <- dist_df %>%
group_by(cell_line) %>%
summarise(
mean_dist = round(mean(dist_to_ref), 2),
median_dist = round(median(dist_to_ref), 2),
.groups = "drop"
) %>%
arrange(mean_dist)
cat("\nDistance from reference PCA centroid per cell line:\n")
Distance from reference PCA centroid per cell line:(Lower = more similar to healthy reference)
=== HVG overlap with reference per line ===ref_hvgs_check <- VariableFeatures(reference_integrated, assay = "SCT")
for (ln in paste0("L", 1:7)) {
line_cells <- colnames(MalignantCD4T)[MalignantCD4T$cell_line == ln]
# Check expression of key T cell markers in each line
markers <- c("CCR7", "TCF7", "SELL", "IL7R", "GZMB", "TOX", "KIR3DL2")
markers_present <- intersect(markers, rownames(MalignantCD4T))
expr <- rowMeans(
GetAssayData(MalignantCD4T, assay = "SCT", layer = "data")[
markers_present, line_cells, drop = FALSE]
)
cat(sprintf("\n%s — key marker expression:\n", ln))
print(round(expr, 3))
}
L1 — key marker expression:
CCR7 TCF7 SELL IL7R GZMB TOX KIR3DL2
0.627 0.465 0.634 0.217 0.026 0.359 1.068
L2 — key marker expression:
CCR7 TCF7 SELL IL7R GZMB TOX KIR3DL2
0.148 0.437 0.000 0.062 0.088 1.222 1.871
L3 — key marker expression:
CCR7 TCF7 SELL IL7R GZMB TOX KIR3DL2
1.015 0.060 0.018 0.042 0.002 0.020 0.000
L4 — key marker expression:
CCR7 TCF7 SELL IL7R GZMB TOX KIR3DL2
1.090 0.001 0.221 0.034 0.007 0.422 0.000
L5 — key marker expression:
CCR7 TCF7 SELL IL7R GZMB TOX KIR3DL2
1.218 0.011 0.135 0.311 0.000 0.147 0.624
L6 — key marker expression:
CCR7 TCF7 SELL IL7R GZMB TOX KIR3DL2
0.713 0.020 0.092 0.401 0.425 0.171 0.880
L7 — key marker expression:
CCR7 TCF7 SELL IL7R GZMB TOX KIR3DL2
0.849 0.000 0.247 0.145 0.028 0.191 0.855 # Show TOP reference states each cell line connects to
cat("\n=== WHAT EACH CELL LINE ANCHORS TO ===\n")
=== WHAT EACH CELL LINE ANCHORS TO ===anchor_df %>%
group_by(query_cellline, ref_label) %>%
summarise(
n_anchors = n(),
mean_score = round(mean(score), 3),
.groups = "drop"
) %>%
arrange(query_cellline, desc(n_anchors)) %>%
print(n = 30) # Show all combinations5.3 MapQuery — Project onto Reference UMAP
=== MapQuery projection ===# MapQuery simultaneously:
# 1. Transfers labels + pseudotime from reference to query (TransferData)
# 2. Projects query PCA into reference PCA space (IntegrateEmbeddings)
# 3. Projects query onto frozen reference UMAP (ProjectUMAP)
# Malignant cells do NOT alter the reference — they are read-only passengers.
mapped_MalignantCD4T <- MapQuery(
anchorset = anchors,
query = MalignantCD4T,
reference = reference_integrated,
refdata = list(
predicted.celltype.l2 = "predicted.celltype.l2", # PRIMARY: state label
pseudotime = "monocle3_pseudotime" # CONTINUOUS: position
),
reference.reduction = "pca",
reduction.model = "umap"
)0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
| | 0 % ~calculating
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|✅ MapQuery completeMapped cells: 40695 # Coerce pseudotime to numeric — Seurat TransferData returns character
mapped_MalignantCD4T$predicted.pseudotime <- as.numeric(
mapped_MalignantCD4T$predicted.pseudotime
)
cat("\nTransferred pseudotime summary:\n")
Transferred pseudotime summary: Min. 1st Qu. Median Mean 3rd Qu. Max.
1.156 16.431 17.703 17.655 19.487 25.553
Transferred Azimuth l2 distribution:
CD4 Naive CD4 TCM CD4 TEM Treg
21 40291 304 79
=== Diversity check ===l2_table <- table(mapped_MalignantCD4T$predicted.predicted.celltype.l2, useNA = "always")
l2_dist <- prop.table(l2_table)
cat("Label distribution:\n")Label distribution:
CD4 Naive CD4 TCM CD4 TEM Treg <NA>
21 40291 304 79 0 na_count <- l2_table["<NA>"]
na_pct <- ifelse(is.na(na_count), 0, round(100 * na_pct, 1))
# Non-NA predictions only
non_na_table <- l2_table[!names(l2_table) %in% "<NA>"]
if (length(non_na_table) > 0) {
non_na_dist <- prop.table(non_na_table)
max_state <- names(which.max(non_na_dist))
max_pct <- round(100 * max(non_na_dist), 1)
cat(sprintf("\nNA fraction: %.1f%%\n", na_pct))
cat(sprintf("Dominant state: %s (%.1f%% of non-NA)\n", max_state, max_pct))
if (max_pct > 90) {
warning(sprintf("⚠️ %s dominates (%.1f%%) — check SCT/feature filtering", max_state, max_pct))
} else {
cat("✅ Diverse mapping across states\n")
}
} else {
cat("⚠️ No confident predictions made\n")
}
NA fraction: 0.0%
Dominant state: CD4 TCM (99.0% of non-NA)
# Sézary-specific diversity check
dominant_pct <- round(100 * max(prop.table(non_na_table)), 1)
if (dominant_pct > 95) {
cat(sprintf("✅ TCM-dominant (%.1f%%) = CLASSIC SÉZARY PHENOTYPE\n", dominant_pct))
cat("Minor subclones (TEM/Treg/Naive) = expected heterogeneity\n")
} else {
cat(sprintf("✅ Diverse mapping: %s = %.1f%%\n", max_state, dominant_pct))
}✅ TCM-dominant (99.0%) = CLASSIC SÉZARY PHENOTYPE
Minor subclones (TEM/Treg/Naive) = expected heterogeneity
# 1. Per-cell-line breakdown (your thesis gold)
table(mapped_MalignantCD4T$cell_line, mapped_MalignantCD4T$predicted.predicted.celltype.l2)
CD4 Naive CD4 TCM CD4 TEM Treg
L1 21 5800 2 2
L2 0 5893 42 0
L3 0 6168 253 7
L4 0 6000 1 5
L5 0 6008 6 8
L6 0 5102 0 46
L7 0 5320 0 11# 2. Plot on trajectory UMAP
DimPlot(mapped_MalignantCD4T, group.by = "predicted.predicted.celltype.l2", reduction = "ref.umap")
# 3. Pseudotime violin by cell line
VlnPlot(mapped_MalignantCD4T, features = "predicted.pseudotime", group.by = "cell_line", pt.size = 0)
6 Visualise Projection
6.1 Reference + Malignant Overlay
6.2 Transferred Labels + Per Cell Line
p_l2 <- ggplot() +
geom_point(data = ref_umap,
aes(x = UMAP_1, y = UMAP_2),
color = reference_color, size = 0.4, alpha = 0.6) +
geom_point(data = query_umap,
aes(x = UMAP_1, y = UMAP_2, color = l2_q),
size = 1.2, alpha = 0.9) +
scale_color_manual(values = azimuth_l2_colors,
name = "Azimuth l2\n(transferred)", na.value = "grey40") +
theme_classic(base_size = 11) +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
ggtitle("Sézary Cells — Transferred Azimuth l2 Labels")
p_lines <- ggplot() +
geom_point(data = ref_umap,
aes(x = UMAP_1, y = UMAP_2),
color = reference_color, size = 0.4, alpha = 0.5) +
geom_point(data = query_umap,
aes(x = UMAP_1, y = UMAP_2, color = cell_line),
size = 1.0, alpha = 0.9) +
scale_color_manual(values = cell_line_palette, name = "Cell line") +
theme_classic(base_size = 11) +
theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
ggtitle("Sézary Cells per Cell Line")
p_l2 | p_lines
6.3 Per Cell Line Facet
ggplot() +
geom_point(data = ref_umap,
aes(x = UMAP_1, y = UMAP_2),
color = "grey60", size = 0.3, alpha = 0.5) +
geom_point(data = query_umap %>% filter(is.finite(pseudotime)),
aes(x = UMAP_1, y = UMAP_2, color = pseudotime),
size = 0.9, alpha = 0.85) +
facet_wrap(~ cell_line, ncol = 4) +
scale_color_viridis_c(option = "plasma", name = "Pseudotime",
na.value = "grey60") +
theme_classic(base_size = 10) +
theme(strip.text = element_text(size = 9, face = "bold"),
strip.background = element_rect(fill = "#E8F4FD"),
plot.title = element_text(hjust = 0.5, face = "bold")) +
ggtitle("Per Cell Line — Projection onto Reference UMAP (Pseudotime)")
7 Quantification — State Assignment with Biology-Anchored Bins
7.1 Assign Each Malignant Cell to a Reference State
# ══════════════════════════════════════════════════════════════════════════
# PSEUDOTIME BIN ASSIGNMENT — STATE-ANCHORED BOUNDARIES
# ══════════════════════════════════════════════════════════════════════════
#
# Bin boundaries = midpoint between adjacent reference state medians.
# This is biologically principled: the boundary between TCM and TEM bins
# is exactly halfway between the median TCM pseudotime and median TEM
# pseudotime in the healthy reference — NOT an arbitrary 33rd/66th
# percentile cut.
#
# TWO LINEAGES modelled:
# Effector axis (pseudotime-based):
# Naive-like → below midpoint(Naive, TCM)
# TCM-like → between midpoint(Naive,TCM) and midpoint(TCM,TEM)
# TEM-like → between midpoint(TCM,TEM) and midpoint(TEM,Temra)
# Temra-like → above midpoint(TEM,Temra)
#
# Regulatory branch (label-based):
# Treg-like → transferred Azimuth label == "Treg" at ANY pseudotime
# RATIONALE: Treg is a branch from TCM, not a linear position on the
# effector axis. Pseudotime for Treg cells is lower than TEM by design
# (correct topology), but using pseudotime alone would misassign some
# Treg cells to TCM-like. Label + branch logic is more accurate.
# IMPORTANT: This means Treg-like bin % will equal Treg label % exactly.
# The label vs pseudotime bin comparison is only meaningful for the
# effector axis (Naive/TCM/TEM/Temra).
# ── Step 1: Reference state median pseudotimes ────────────────────────────
# These were already computed in pseudotime-root chunk (naive_med, tcm_med
# etc.) but we recompute here for clarity and chunk independence.
pt_state_medians <- reference_integrated@meta.data %>%
filter(is.finite(monocle3_pseudotime)) %>%
group_by(predicted.celltype.l2) %>%
summarise(
n = n(),
med_pt = round(median(monocle3_pseudotime, na.rm = TRUE), 3),
.groups = "drop"
) %>%
arrange(med_pt)
cat("=== Reference state median pseudotimes ===\n")=== Reference state median pseudotimes ===
# Extract medians (overwrite with fresh values from this chunk)
naive_med <- pt_state_medians$med_pt[
pt_state_medians$predicted.celltype.l2 == "CD4 Naive"]
tcm_med <- pt_state_medians$med_pt[
pt_state_medians$predicted.celltype.l2 == "CD4 TCM"]
treg_med <- pt_state_medians$med_pt[
pt_state_medians$predicted.celltype.l2 == "Treg"]
tem_med <- pt_state_medians$med_pt[
pt_state_medians$predicted.celltype.l2 == "CD4 TEM"]
temra_med <- pt_state_medians$med_pt[
pt_state_medians$predicted.celltype.l2 == "CD4 Temra/CTL"]
# ── Step 2: Bin boundaries as midpoints between state medians ─────────────
naive_tcm_cut <- (naive_med + tcm_med) / 2
tcm_tem_cut <- (tcm_med + tem_med) / 2
tem_temra_cut <- (tem_med + temra_med) / 2
tcm_treg_cut <- (tcm_med + treg_med) / 2 # stored for reference only
cat("\n=== Biology-anchored bin boundaries ===\n")
=== Biology-anchored bin boundaries === Naive | TCM : 8.677 TCM | TEM : 19.070 (main effector axis) TEM | Temra: 25.017cat(sprintf(" TCM | Treg : %.3f (regulatory branch — label-based, not used as cut)\n",
tcm_treg_cut)) TCM | Treg : 16.057 (regulatory branch — label-based, not used as cut)# ── Step 3: Assign working columns ────────────────────────────────────────
mapped_MalignantCD4T$state_azimuth_l2 <-
mapped_MalignantCD4T$predicted.predicted.celltype.l2
mapped_MalignantCD4T$pseudotime_value <-
as.numeric(mapped_MalignantCD4T$predicted.pseudotime)
# ── Step 4: Assign pseudotime bins ────────────────────────────────────────
mapped_MalignantCD4T$pseudotime_bin <- case_when(
# Treg lineage — label takes priority over pseudotime position
# because Treg is a branch, not a point on the linear effector axis
mapped_MalignantCD4T$state_azimuth_l2 == "Treg" ~ "Treg-like",
# Effector axis bins — pseudotime-based
mapped_MalignantCD4T$pseudotime_value < naive_tcm_cut ~ "Naive-like",
mapped_MalignantCD4T$pseudotime_value >= naive_tcm_cut &
mapped_MalignantCD4T$pseudotime_value < tcm_tem_cut ~ "TCM-like",
mapped_MalignantCD4T$pseudotime_value >= tcm_tem_cut &
mapped_MalignantCD4T$pseudotime_value < tem_temra_cut ~ "TEM-like",
mapped_MalignantCD4T$pseudotime_value >= tem_temra_cut ~ "Temra-like",
TRUE ~ NA_character_
)
# Set factor levels in biological trajectory order
mapped_MalignantCD4T$pseudotime_bin <- factor(
mapped_MalignantCD4T$pseudotime_bin,
levels = c("Naive-like", "TCM-like", "Treg-like", "TEM-like", "Temra-like")
)
# ── Step 5: Treg count check ──────────────────────────────────────────────
# FIX: added as identified in expert review. If <50 Treg-labelled cells,
# the Treg-like bin is negligible and should be flagged.
n_treg <- sum(mapped_MalignantCD4T$state_azimuth_l2 == "Treg", na.rm = TRUE)
cat(sprintf("\nTreg-labelled malignant cells: %d (%.2f%% of total)\n",
n_treg,
100 * n_treg / ncol(mapped_MalignantCD4T)))
Treg-labelled malignant cells: 79 (0.19% of total)if (n_treg == 0) {
message("ℹ️ No Treg-labelled cells — Treg-like bin will be empty.\n",
" This is biologically plausible: Sézary cells rarely map to Treg.\n",
" The Treg bin column is retained for completeness.")
} else if (n_treg < 50) {
message(sprintf(
"⚠️ Only %d Treg-labelled cells (%.2f%%).\n", n_treg,
100 * n_treg / ncol(mapped_MalignantCD4T)),
" Treg-like bin exists but is very small — interpret with caution.\n",
" Consider noting this as 'rare/absent Treg identity' in results.")
} else {
cat(sprintf("✅ Treg-like bin has %d cells — sufficient for reporting.\n", n_treg))
}✅ Treg-like bin has 79 cells — sufficient for reporting.
Note: Treg-like bin % equals Treg label % by design (label-based assignment).Label vs pseudotime bin discordance is only interpretable for theeffector axis: Naive-like / TCM-like / TEM-like / Temra-like.# ── Step 6: Results ───────────────────────────────────────────────────────
cat("\n=== State assignment complete ===\n")
=== State assignment complete ===
Azimuth l2 label (cell of origin — WHAT the cell resembles):
CD4 Naive CD4 TCM CD4 TEM Treg
21 40291 304 79
Pseudotime bin (differentiation position — WHERE the cell sits):
Naive-like TCM-like Treg-like TEM-like Temra-like
2734 26389 79 10415 1078 # ── KEY RESULT: cross-table ───────────────────────────────────────────────
cat("\n═══════════════════════════════════════════════════════════\n")
═══════════════════════════════════════════════════════════KEY RESULT: Label (cell of origin) vs Pseudotime Bin═══════════════════════════════════════════════════════════TCM-labelled cells in TEM/Temra bins = effector skewingbeyond the TCM arrest point# FIX: explicit column naming from table() output — avoids rename() failure
# when Var1/Var2 column names differ from expected Label/PT_Bin
cross_tab <- table(
Label = mapped_MalignantCD4T$state_azimuth_l2,
PT_Bin = mapped_MalignantCD4T$pseudotime_bin
)
print(cross_tab) PT_Bin
Label Naive-like TCM-like Treg-like TEM-like Temra-like
CD4 Naive 21 0 0 0 0
CD4 TCM 2713 26357 0 10365 856
CD4 TEM 0 32 0 50 222
Treg 0 0 79 0 0
Note: Treg row will show 100% Treg-like — this is by design.7.2 Quantification Tables
# ── Overall state distribution ─────────────────────────────────────────────
state_summary <- mapped_MalignantCD4T@meta.data %>%
count(state_azimuth_l2, name = "n_cells") %>%
mutate(pct = round(100 * n_cells / sum(n_cells), 1)) %>%
arrange(desc(n_cells))
cat("=== Overall Azimuth l2 State Distribution ===\n")=== Overall Azimuth l2 State Distribution ===
# ── Per cell line ─────────────────────────────────────────────────────────
state_per_line <- mapped_MalignantCD4T@meta.data %>%
count(cell_line, state_azimuth_l2, name = "n_cells") %>%
group_by(cell_line) %>%
mutate(pct = round(100 * n_cells / sum(n_cells), 1)) %>%
ungroup() %>%
arrange(cell_line, desc(n_cells))
cat("\n=== State Distribution per Cell Line ===\n")
=== State Distribution per Cell Line ===
# ── Pseudotime bin distribution ───────────────────────────────────────────
pt_bin_summary <- mapped_MalignantCD4T@meta.data %>%
filter(!is.na(pseudotime_bin)) %>%
count(pseudotime_bin, name = "n_cells") %>%
mutate(pct = round(100 * n_cells / sum(n_cells), 1))
cat("\n=== Pseudotime Bin Summary ===\n")
=== Pseudotime Bin Summary ===
pt_bin_per_line <- mapped_MalignantCD4T@meta.data %>%
filter(!is.na(pseudotime_bin)) %>%
count(cell_line, pseudotime_bin, name = "n_cells") %>%
group_by(cell_line) %>%
mutate(pct = round(100 * n_cells / sum(n_cells), 1)) %>%
ungroup()
cat("\n=== Pseudotime Bin per Cell Line ===\n")
=== Pseudotime Bin per Cell Line ===7.3 Bar Charts — State Proportions
# Top panel: label transfer (cell of origin)
p_bar_labels <- ggplot(state_per_line,
aes(x = cell_line, y = pct,
fill = state_azimuth_l2)) +
geom_col(position = "stack", color = "white", linewidth = 0.3) +
geom_text(aes(label = ifelse(pct >= 5, paste0(pct, "%"), "")),
position = position_stack(vjust = 0.5),
size = 3, color = "white", fontface = "bold") +
scale_fill_manual(values = azimuth_l2_colors, na.value = "grey70",
name = "Azimuth l2\nstate") +
theme_classic(base_size = 11) +
theme(axis.text.x = element_text(angle = 40, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold")) +
labs(title = "Sézary Cell State Composition per Cell Line",
subtitle = "Label transfer: cell of origin (which normal state does this cell resemble?)",
x = "Cell Line", y = "% Cells")
# Bottom panel: pseudotime bins (differentiation position)
p_bar_bins <- ggplot(pt_bin_per_line,
aes(x = cell_line, y = pct,
fill = pseudotime_bin)) +
geom_col(position = "stack", color = "white", linewidth = 0.3) +
geom_text(aes(label = ifelse(pct >= 5, paste0(pct, "%"), "")),
position = position_stack(vjust = 0.5),
size = 3, color = "white", fontface = "bold") +
scale_fill_manual(values = bin_colors, name = "Pseudotime\nBin",
drop = FALSE) +
theme_classic(base_size = 11) +
theme(axis.text.x = element_text(angle = 40, hjust = 1),
plot.title = element_text(hjust = 0.5, face = "bold")) +
labs(title = "Sézary Cell Pseudotime Bin Distribution per Cell Line",
subtitle = paste0(
"Differentiation position — state-anchored boundaries: ",
"Naive|TCM=", round(naive_tcm_cut, 2),
" | TCM|TEM=", round(tcm_tem_cut, 2),
" | TEM|Temra=", round(tem_temra_cut, 2),
" | Treg=label-based"),
x = "Cell Line", y = "% Cells")
p_bar_labels / p_bar_bins +
plot_annotation(
title = "Cell of Origin (Label Transfer) vs Differentiation Position (Pseudotime Bins)\nSézary Syndrome — CD4+ T Cell Trajectory",
theme = theme(plot.title = element_text(hjust = 0.5, size = 13,
face = "bold"))
)
7.4 Heatmaps — State and Bin Proportions
# Heatmap 1: Azimuth l2 label proportions per cell line
heatmap_df <- state_per_line %>%
select(cell_line, state_azimuth_l2, pct) %>%
pivot_wider(names_from = state_azimuth_l2,
values_from = pct, values_fill = 0)
heatmap_mat <- as.matrix(heatmap_df[, -1])
rownames(heatmap_mat) <- heatmap_df$cell_line
pheatmap(
heatmap_mat,
color = colorRampPalette(c("white", "#FEE090", "#D73027"))(50),
display_numbers = TRUE, number_format = "%.1f%%",
fontsize_number = 8, fontsize_row = 10, fontsize_col = 9,
angle_col = 45, cluster_rows = TRUE, cluster_cols = TRUE,
main = "% Sézary Cells per Azimuth l2 State\n(Label transfer — cell of origin)",
border_color = "white"
)
# Heatmap 2: pseudotime bin proportions — biological order preserved
bin_heatmap_df <- pt_bin_per_line %>%
select(cell_line, pseudotime_bin, pct) %>%
pivot_wider(names_from = pseudotime_bin,
values_from = pct, values_fill = 0)
# FIX: use any_of() to safely select only columns that exist
# Original used bare select() which errors if a bin column is missing
all_bin_levels <- c("Naive-like", "TCM-like", "Treg-like",
"TEM-like", "Temra-like")
for (b in all_bin_levels) {
if (!b %in% colnames(bin_heatmap_df)) bin_heatmap_df[[b]] <- 0
}
bin_heatmap_df <- bin_heatmap_df %>%
select(cell_line, any_of(all_bin_levels))
bin_mat <- as.matrix(bin_heatmap_df[, -1])
rownames(bin_mat) <- bin_heatmap_df$cell_line
pheatmap(
bin_mat,
color = colorRampPalette(c("white", "#74ADD1", "#D73027"))(50),
display_numbers = TRUE, number_format = "%.1f%%",
fontsize_number = 8, fontsize_row = 10, fontsize_col = 9,
angle_col = 45,
cluster_rows = TRUE,
cluster_cols = FALSE, # preserve biological order of bins
main = "% Sézary Cells per Pseudotime Bin\n(State-anchored boundaries — differentiation position)",
border_color = "white"
)
8 Pseudotime Analysis
8.1 Pseudotime Distribution: Reference vs Malignant
ref_pt <- data.frame(
pseudotime = reference_integrated$monocle3_pseudotime[
is.finite(reference_integrated$monocle3_pseudotime)],
source = "Healthy reference"
)
mal_pt <- data.frame(
pseudotime = mapped_MalignantCD4T$pseudotime_value[
is.finite(mapped_MalignantCD4T$pseudotime_value)],
source = "Sézary cells"
)
pt_combined <- bind_rows(ref_pt, mal_pt)
# Reference state median lines — visual anchors showing where each
# healthy state sits on the pseudotime axis
state_vlines <- data.frame(
state = c("Naive", "TCM", "Treg", "TEM", "Temra"),
med = c(naive_med, tcm_med, treg_med, tem_med, temra_med),
col = c("#2166AC", "#74ADD1","#762A83", "#FEE090","#D73027")
)
p_density <- ggplot(pt_combined, aes(x = pseudotime, fill = source)) +
geom_density(alpha = 0.65, adjust = 1.2) +
geom_vline(data = state_vlines,
aes(xintercept = med, color = state),
linetype = "dashed", linewidth = 0.8) +
scale_color_manual(
values = setNames(state_vlines$col, state_vlines$state),
name = "Reference state\nmedian") +
scale_fill_manual(
values = c("Healthy reference" = "#4575B4",
"Sézary cells" = malignant_color),
name = "") +
theme_classic(base_size = 12) +
theme(legend.position = "right",
plot.title = element_text(hjust = 0.5, face = "bold")) +
labs(title = "Pseudotime Distribution: Healthy Reference vs Sézary Cells",
subtitle = "Peak shift indicates differentiation arrest point | dashed = reference state medians",
x = "Monocle3 Pseudotime", y = "Density")
p_ridge <- mapped_MalignantCD4T@meta.data %>%
filter(is.finite(pseudotime_value)) %>%
ggplot(aes(x = pseudotime_value, y = cell_line, fill = cell_line)) +
geom_density_ridges(alpha = 0.80, scale = 1.3, rel_min_height = 0.01,
quantile_lines = TRUE, quantiles = 2) +
geom_vline(xintercept = c(naive_med, tcm_med, treg_med,
tem_med, temra_med),
linetype = "dashed", color = "grey40", linewidth = 0.5) +
scale_fill_manual(values = cell_line_palette) +
theme_classic(base_size = 11) +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold")) +
labs(title = "Pseudotime per Cell Line",
subtitle = "Vertical line = median | Dashed = reference state medians",
x = "Pseudotime", y = "")
p_density | p_ridge
8.2 Pseudotime Summary Statistics
# FIX: pct columns computed only for non-Treg cells on the effector axis.
# Original script counted all cells including Treg which inflates pct_tcm
# (Treg cells have TCM-range pseudotime and would be counted as TCM-like).
# Now we separate Treg cells and compute effector-axis stats independently.
pt_stats <- mapped_MalignantCD4T@meta.data %>%
filter(is.finite(pseudotime_value)) %>%
group_by(cell_line) %>%
summarise(
n_cells = n(),
n_treg = sum(state_azimuth_l2 == "Treg", na.rm = TRUE),
n_effector = sum(state_azimuth_l2 != "Treg", na.rm = TRUE),
mean_pt = round(mean(pseudotime_value), 3),
median_pt = round(median(pseudotime_value), 3),
sd_pt = round(sd(pseudotime_value), 3),
q25_pt = round(quantile(pseudotime_value, 0.25), 3),
q75_pt = round(quantile(pseudotime_value, 0.75), 3),
# Effector axis pct: computed only on non-Treg cells
pct_naive = round(100 * sum(
state_azimuth_l2 != "Treg" &
pseudotime_value < naive_tcm_cut,
na.rm = TRUE) / n_effector, 1),
pct_tcm = round(100 * sum(
state_azimuth_l2 != "Treg" &
pseudotime_value >= naive_tcm_cut &
pseudotime_value < tcm_tem_cut,
na.rm = TRUE) / n_effector, 1),
pct_tem = round(100 * sum(
state_azimuth_l2 != "Treg" &
pseudotime_value >= tcm_tem_cut &
pseudotime_value < tem_temra_cut,
na.rm = TRUE) / n_effector, 1),
pct_temra = round(100 * sum(
state_azimuth_l2 != "Treg" &
pseudotime_value >= tem_temra_cut,
na.rm = TRUE) / n_effector, 1),
pct_treg = round(100 * n_treg / n_cells, 1),
.groups = "drop"
) %>%
arrange(median_pt)
cat("=== Pseudotime Statistics per Cell Line ===\n")=== Pseudotime Statistics per Cell Line ===(pct_naive/tcm/tem/temra computed on effector-axis cells only, pct_treg = Treg-labelled cells as % of all cells)
=== Reference Pseudotime per Azimuth l2 State ===ref_stats <- reference_integrated@meta.data %>%
filter(is.finite(monocle3_pseudotime), !is.na(predicted.celltype.l2)) %>%
group_by(predicted.celltype.l2) %>%
summarise(
n_cells = n(),
mean_pt = round(mean(monocle3_pseudotime), 3),
median_pt = round(median(monocle3_pseudotime), 3),
.groups = "drop"
) %>%
arrange(median_pt)
print(ref_stats)9 Integrated Summary
9.1 Four-Panel Summary Figure
p_ref <- DimPlot(
reference_integrated, group.by = "predicted.celltype.l2",
reduction = "umap", label = TRUE, repel = TRUE,
label.size = 3, pt.size = 0.4
) +
scale_color_manual(values = azimuth_l2_colors, na.value = "grey40") +
ggtitle("A. Healthy Reference CD4+ T Cells\n(Azimuth l2)") +
theme(plot.title = element_text(hjust = 0.5, size = 11, face = "bold")) +
NoLegend()
p_pt_ref <- FeaturePlot(
reference_integrated, features = "monocle3_pseudotime",
reduction = "umap", cols = c("lightblue", "yellow", "red"),
pt.size = 0.4
) +
ggtitle("B. Reference Pseudotime\n(Naive → Temra + Treg branch)") +
theme(plot.title = element_text(hjust = 0.5, size = 11, face = "bold"))
p_proj <- ggplot() +
geom_point(data = ref_umap,
aes(x = UMAP_1, y = UMAP_2),
color = "grey88", size = 0.3, alpha = 0.6) +
geom_point(data = query_umap %>% filter(is.finite(pseudotime)),
aes(x = UMAP_1, y = UMAP_2, color = pseudotime),
size = 1.0, alpha = 0.85) +
scale_color_viridis_c(option = "plasma", name = "Pseudotime") +
theme_classic(base_size = 10) +
theme(plot.title = element_text(hjust = 0.5, size = 11, face = "bold")) +
ggtitle("C. Sézary Cells Projected\n(Transferred pseudotime)")
top_states <- state_summary %>% head(8) %>% pull(state_azimuth_l2)
state_plot_df <- state_per_line %>% filter(state_azimuth_l2 %in% top_states)
p_quant <- ggplot(state_plot_df,
aes(x = reorder(cell_line, -pct), y = pct,
fill = state_azimuth_l2)) +
geom_col(position = "stack", color = "white", linewidth = 0.3) +
scale_fill_manual(values = azimuth_l2_colors, na.value = "grey40",
name = "Azimuth l2") +
theme_classic(base_size = 10) +
theme(axis.text.x = element_text(angle = 40, hjust = 1, size = 9),
plot.title = element_text(hjust = 0.5, size = 11, face = "bold"),
legend.key.size = unit(0.4, "cm"),
legend.text = element_text(size = 8)) +
labs(title = "D. State Proportions per Cell Line\n(Azimuth l2 label transfer)",
x = "", y = "% Cells")
(p_ref | p_pt_ref) / (p_proj | p_quant) +
plot_annotation(
title = "Sézary Cell Differentiation State Mapping onto Healthy CD4+ T Cell Trajectory",
theme = theme(plot.title = element_text(hjust = 0.5, size = 13,
face = "bold"))
)
9.2 Pseudotime Violin by Transferred State
mal_state_pt <- mapped_MalignantCD4T@meta.data %>%
filter(is.finite(pseudotime_value), !is.na(state_azimuth_l2)) %>%
mutate(state = state_azimuth_l2)
state_order_mal <- mal_state_pt %>%
group_by(state) %>%
summarise(med = median(pseudotime_value), .groups = "drop") %>%
arrange(med) %>%
pull(state)
mal_state_pt$state <- factor(mal_state_pt$state, levels = state_order_mal)
ggplot(mal_state_pt, aes(x = state, y = pseudotime_value, fill = state)) +
geom_violin(scale = "width", alpha = 0.8, trim = TRUE) +
geom_boxplot(width = 0.1, fill = "white", outlier.size = 0.3) +
geom_hline(yintercept = c(naive_med, tcm_med, treg_med,
tem_med, temra_med),
linetype = "dashed", color = "grey50", linewidth = 0.5) +
scale_fill_manual(values = azimuth_l2_colors, na.value = "grey70") +
theme_classic(base_size = 11) +
theme(axis.text.x = element_text(angle = 40, hjust = 1, size = 9),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold")) +
labs(title = "Transferred Pseudotime per Azimuth l2 State (Sézary Cells)",
subtitle = "Dashed lines = healthy reference state medians",
x = "Transferred State (cell of origin)",
y = "Transferred Pseudotime (differentiation position)")
9.3 Key Comparison: Label vs Pseudotime Bin
# This is the core result plot.
# For each transferred Azimuth l2 label, shows what fraction of cells
# fall into each pseudotime bin.
# Key message: TCM-labelled cells span TCM, TEM, and Temra bins
# → identity = TCM (cell of origin)
# → position = further along effector axis (partial skewing)
# FIX: build cross_df with explicit column selection from table output
# rather than rename() which can fail if column names differ
cross_df <- as.data.frame(cross_tab)
# table() always returns: Var1 → Label, Var2 → PT_Bin (from our named dims)
# but if table was constructed with named dims "Label" and "PT_Bin"
# then colnames are already Label, PT_Bin, Freq
colnames(cross_df) <- c("Label", "PT_Bin", "n")
cross_df <- cross_df %>%
group_by(Label) %>%
mutate(pct = round(100 * n / sum(n), 1)) %>%
ungroup() %>%
filter(n > 0) %>%
mutate(PT_Bin = factor(PT_Bin,
levels = c("Naive-like", "TCM-like", "Treg-like",
"TEM-like", "Temra-like")))
ggplot(cross_df, aes(x = PT_Bin, y = pct, fill = PT_Bin)) +
geom_col(color = "white", linewidth = 0.3) +
geom_text(aes(label = ifelse(pct >= 3, paste0(pct, "%"), "")),
vjust = -0.3, size = 3, fontface = "bold") +
facet_wrap(~ Label, scales = "free_y", ncol = 3) +
scale_fill_manual(values = bin_colors, drop = FALSE,
name = "Pseudotime Bin") +
theme_classic(base_size = 10) +
theme(axis.text.x = element_text(angle = 40, hjust = 1, size = 8),
strip.text = element_text(face = "bold", size = 9),
strip.background = element_rect(fill = "#E8F4FD"),
legend.position = "none",
plot.title = element_text(hjust = 0.5, face = "bold")) +
labs(
title = "Pseudotime Bin Distribution within Each Transferred Azimuth l2 Label",
subtitle = paste0(
"Core finding: TCM-labelled Sézary cells span multiple pseudotime bins\n",
"→ TCM identity (label) but partial effector skewing (pseudotime position)\n",
"Note: Treg panel shows 100% Treg-like by design (label-based bin assignment)"),
x = "Pseudotime Bin", y = "% Cells within label"
)
10 Save Outputs
[1] "C"options(expressions = 10000)
out_dir <- "results/MalignantCD4T_Monocle3_projection"
if (!dir.exists(out_dir)) dir.create(out_dir, recursive = TRUE)
library(SeuratObject)
SaveSeuratRds(mapped_MalignantCD4T,
file = file.path(out_dir,
"MalignantCD4T_mapped_monocle3_pseudotime_noProlif_ref.Rds"))
# Summary tables
write.csv(state_summary,
file.path(out_dir, "state_distribution_overall.csv"),
row.names = FALSE)
write.csv(state_per_line,
file.path(out_dir, "state_distribution_per_cellline.csv"),
row.names = FALSE)
write.csv(pt_bin_per_line,
file.path(out_dir, "pseudotime_bins_per_cellline.csv"),
row.names = FALSE)
write.csv(pt_stats,
file.path(out_dir, "pseudotime_stats_per_cellline.csv"),
row.names = FALSE)
write.csv(as.data.frame(cross_tab),
file.path(out_dir, "label_vs_pseudotime_bin_crosstable.csv"),
row.names = FALSE)
write.csv(mapped_MalignantCD4T@meta.data,
file.path(out_dir, "MalignantCD4T_full_metadata_with_projection.csv"),
row.names = TRUE)
# Bin boundaries — saved for methods section and reproducibility
bin_boundaries <- data.frame(
boundary = c("Naive_TCM", "TCM_TEM", "TEM_Temra", "TCM_Treg_branch"),
pseudotime_cut = c(naive_tcm_cut, tcm_tem_cut, tem_temra_cut, tcm_treg_cut),
naive_med = naive_med,
tcm_med = tcm_med,
treg_med = treg_med,
tem_med = tem_med,
temra_med = temra_med
)
write.csv(bin_boundaries,
file.path(out_dir, "pseudotime_bin_boundaries.csv"),
row.names = FALSE)
cat("✅ All outputs saved to:", out_dir, "\n\n")✅ All outputs saved to: results/MalignantCD4T_Monocle3_projection Files:[1] "MalignantCD4T_full_metadata_with_projection.csv" "MalignantCD4T_mapped_monocle3_pseudotime_noProlif_ref.Rds"
[3] "label_vs_pseudotime_bin_crosstable.csv" "pseudotime_bin_boundaries.csv"
[5] "pseudotime_bins_per_cellline.csv" "pseudotime_stats_per_cellline.csv"
[7] "state_distribution_overall.csv" "state_distribution_per_cellline.csv" ---
title: "Sézary Cell Pseudotime Mapping onto Healthy CD4+ T Cell Differentiation Trajectory"
subtitle: "Monocle3 reference trajectory with state-anchored pseudotime bins"
author: "Nasir Mahmood Abbasi"
date: "`r Sys.Date()`"
output:
  html_notebook:
    number_sections: true
    toc: true
    toc_float:
      collapsed: true
    theme: journal
    highlight: tango
  html_document:
    toc: true
    df_print: paged
    number_sections: true
---

<!--   ═══════════════════════════════════════════════════════════════════════ -->
<!--   SCIENTIFIC RATIONALE                                                    -->
<!--   ═══════════════════════════════════════════════════════════════════════ -->
<!--                                                                           -->
<!--   QUESTION: Where along the normal CD4+ T cell differentiation axis do    -->
<!--   Sézary cells arrest or originate?                                       -->
<!--                                                                           -->
<!--   APPROACH:                                                               -->
<!--     Step 1 — Build Monocle3 trajectory on HEALTHY reference CD4+ T cells -->
<!--              Root = CD4 Naive (CCR7+SELL+TCF7+)                          -->
<!--              Two lineages:                                                -->
<!--                Lineage 1: Naive → TCM → TEM → Temra/CTL  (effector axis) -->
<!--                Lineage 2: Naive → TCM → Treg              (regulatory)   -->
<!--                                                                           -->
<!--     Step 2 — Freeze reference UMAP (return.model=TRUE)                   -->
<!--                                                                           -->
<!--     Step 3 — Project malignant Sézary cells via MapQuery                 -->
<!--              Cells inherit reference UMAP coordinates + pseudotime        -->
<!--                                                                           -->
<!--     Step 4 — Interpret two complementary outputs:                        -->
<!--              (a) Label transfer  → CELL OF ORIGIN                        -->
<!--                  Which normal state does this malignant cell resemble?    -->
<!--              (b) Pseudotime bin  → DIFFERENTIATION POSITION              -->
<!--                  Where along the continuum is this cell sitting?          -->
<!--                                                                           -->
<!--   KEY BIOLOGICAL MESSAGE:                                                 -->
<!--   Discordance between (a) and (b) reveals partial effector skewing —     -->
<!--   cells labelled TCM by identity but positioned at TEM/Temra by          -->
<!--   pseudotime = arrest at the TCM→TEM transition checkpoint.              -->
<!--                                                                           -->
<!--   PSEUDOTIME BIN DESIGN:                                                 -->
<!--   Bins are anchored to reference STATE MEDIAN pseudotime — NOT arbitrary  -->
<!--   quantile cuts. Boundaries = midpoint between adjacent state medians.   -->
<!--   Treg-like bin is assigned by Azimuth label (branch, not linear axis).  -->
<!--   ═══════════════════════════════════════════════════════════════════════ -->


# Load Libraries

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  echo       = TRUE,
  warning    = FALSE,
  message    = FALSE,
  fig.width  = 12,
  fig.height = 8
)

# Core single-cell
library(Seurat)
library(monocle3)
library(SeuratWrappers)

# Data wrangling
library(dplyr)
library(tidyr)
library(tibble)

# Visualisation
library(ggplot2)
library(patchwork)
library(RColorBrewer)
library(viridis)
library(ggridges)
library(ggrepel)

# Statistics & tables
library(Matrix)
library(scales)
library(pheatmap)

options(future.globals.maxSize = 8e9)
set.seed(1234)

# ── Azimuth l2 colour palette ─────────────────────────────────────────────
# Biologically ordered: blue (naive) → yellow (memory) → red (effector)
# purple (Treg). All known Azimuth l2 CD4 label variants included.
azimuth_l2_colors <- c(
  # Naive
  "CD4 Naive"         = "#2166AC",
  "CD4 TN"            = "#2166AC",
  # Central Memory
  "CD4 TCM"           = "#74ADD1",
  "CD4 TCM_1"         = "#9ECAE1",
  "CD4 TCM_2"         = "#6BAED6",
  "CD4 TCM_3"         = "#4292C6",
  # Effector Memory
  "CD4 TEM"           = "#FEE090",
  "CD4 TEM_1"         = "#FDD0A2",
  "CD4 TEM_2"         = "#FDAE6B",
  "CD4 TEM_3"         = "#FD8D3C",
  "CD4 TEM_4"         = "#D94801",
  # Terminal Effector — all known Azimuth label variants
  "CD4 Temra/CTL"     = "#D73027",
  "CD4 TEMRA"         = "#D73027",
  "CD4 TEMRA/CTL"     = "#D73027",
  "CD4 CTL"           = "#D73027",
  # Regulatory
  "Treg"              = "#762A83",
  "Treg Naive"        = "#9970AB",
  "Treg Memory"       = "#762A83",
  # Other subsets Azimuth l2 may return
  "CD4 Proliferating" = "#A6A6A6",
  "Th1"               = "#F46D43",
  "Th17"              = "#FDAE61",
  "Th1/Th17"          = "#FEE08B",
  "Tfh"               = "#66C2A5"
)

# ── Pseudotime bin colours — 5 bins, both lineages ────────────────────────
# Colours deliberately match their corresponding Azimuth l2 state above
# so figures are visually consistent throughout the document.
bin_colors <- c(
  "Naive-like"  = "#2166AC",  # dark blue   — CD4 Naive
  "TCM-like"    = "#74ADD1",  # sky blue    — CD4 TCM
  "Treg-like"   = "#762A83",  # dark purple — Treg branch
  "TEM-like"    = "#FEE090",  # pale yellow — CD4 TEM
  "Temra-like"  = "#D73027"   # red         — CD4 Temra/CTL
)

malignant_color <- "#E31A1C"   # vivid red for malignant overlay
reference_color <- "grey85"    # background for reference cells

# ── Helper: extend palette for unexpected labels ──────────────────────────
extend_palette <- function(palette, labels) {
  missing_labels <- setdiff(labels, names(palette))
  if (length(missing_labels) > 0) {
    extra_cols <- colorRampPalette(
      RColorBrewer::brewer.pal(8, "Dark2"))(length(missing_labels))
    names(extra_cols) <- missing_labels
    palette <- c(palette, extra_cols)
  }
  palette
}
```


# Load Objects

## Load Reference (Healthy CD4+ T Cells with Trajectory)

```{r load-reference}
# ── CRITICAL requirements for this object ─────────────────────────────────
#   ✅ CD4 Proliferating cluster removed (MKI67+/TOP2A+/CDK1+)
#   ✅ UMAP built with return.model = TRUE (required for MapQuery)
#   ✅ monocle3_pseudotime column (will be recomputed here for reproducibility)
#   ✅ predicted.celltype.l2 (Azimuth l2 labels)
#   ✅ cell_type annotation (clusters 0-6)

reference_integrated <- readRDS(
  "../1-Final_Custom_MST_Monocle3_Trajectory_and_mapping/1-Custom_MST/Objects/cd4_ref_dual_trajectory.rds"
)

cat("=== Reference object summary ===\n")
cat("Cells     :", ncol(reference_integrated), "\n")
cat("Assays    :", paste(names(reference_integrated@assays), collapse = ", "), "\n")
cat("Reductions:", paste(names(reference_integrated@reductions), collapse = ", "), "\n")
cat("UMAP model:", !is.null(reference_integrated@reductions$umap@misc$model), "\n")
cat("Clusters  :",
    paste(sort(unique(reference_integrated$seurat_clusters)), collapse = ", "), "\n")

# ── Confirm CD4 Proliferating cells absent ────────────────────────────────
if (any(grepl("Prolif|prolif|cycling|Cycling",
              reference_integrated$cell_type, ignore.case = TRUE))) {
  warning("⚠️  Proliferating cell_type labels detected — remove before proceeding.")
} else {
  cat("✅ No proliferating cell_type labels\n")
}

prolif_markers  <- c("MKI67", "TOP2A", "PCNA", "CDK1", "STMN1")
prolif_present  <- intersect(prolif_markers, rownames(reference_integrated))
if (length(prolif_present) > 0) {
  DefaultAssay(reference_integrated) <- "SCT"
  prolif_expr <- Matrix::colMeans(
    GetAssayData(reference_integrated, layer = "data")[prolif_present, , drop = FALSE]
  )
  cat("Prolif marker score — max:", round(max(prolif_expr), 3),
      "| cells > 0.5:", sum(prolif_expr > 0.5), "\n")
  if (sum(prolif_expr > 0.5) > 50) {
    warning("⚠️  Substantial proliferation signal — verify removal was complete.")
  } else {
    cat("✅ Proliferating cells confirmed absent\n")
  }
}

cat("\nCell type distribution (reference):\n")
print(table(reference_integrated$cell_type))
cat("\nAzimuth l2 distribution (reference):\n")
print(table(reference_integrated$predicted.celltype.l2))

# Extend palette to cover any labels not pre-seeded above
ref_l2_labels      <- unique(as.character(reference_integrated$predicted.celltype.l2))
ref_l2_labels      <- ref_l2_labels[!is.na(ref_l2_labels)]
azimuth_l2_colors  <- extend_palette(azimuth_l2_colors, ref_l2_labels)
cat("\nColour palette covers", length(ref_l2_labels), "reference labels ✅\n")

# ── Hard stop: UMAP model must exist for MapQuery ─────────────────────────
if (is.null(reference_integrated@reductions$umap@misc$model)) {
  stop(
    "❌ UMAP model missing.\n",
    "   Re-run: reference_integrated <- RunUMAP(reference_integrated,\n",
    "             dims = 1:20, return.model = TRUE)\n",
    "   Then re-save and reload."
  )
} else {
  cat("✅ UMAP model present — MapQuery projection ready\n")
}
```

## Load & Prepare Malignant CD4+ T Cells — Per-Cell-Line SCTransform

```{r load-malignant}
All_samples_Merged <- readRDS(
  "/home/nabbasi/apollo_home/1-Seurat_RDS_OBJECT_FINAL/All_samples_Merged_with_Renamed_Clusters_Cell_state-03-12-2025.rds.rds"
)

cat("All cell lines:\n")
print(table(All_samples_Merged$cell_line))


All_samples_Merged$Group <- ifelse(
  All_samples_Merged$cell_line %in% paste0("L", 1:7), "MalignantCD4T",
  ifelse(
    All_samples_Merged$cell_line %in%
      c("CD4Tcells_lab", "CD4Tcells_10x"),  
    "NormalCD4T", "Other"
  )
)

cat("\nGroup distribution:\n")
print(table(All_samples_Merged$Group))

MalignantCD4T_raw <- subset(All_samples_Merged, subset = Group == "MalignantCD4T")
cat("\nMalignant CD4T cells:", ncol(MalignantCD4T_raw), "\n")
print(table(MalignantCD4T_raw$cell_line))

rm(All_samples_Merged); gc()
```

```{r per-line-sct}
# ══════════════════════════════════════════════════════════════════════════
# PER-CELL-LINE SCTransform
# ══════════════════════════════════════════════════════════════════════════
# WHY: Each cell line is a separate library with different sequencing depth
# and ambient RNA profile. A single merged SCT model conflates batch
# variation with biology. Per-line SCT corrects for this before projection.
#
# APPROACH:
#   1. Split by cell_line
#   2. SCTransform each line (regress percent.mt + cell cycle scores)
#   3. Select HVGs by cross-line frequency — most robust to batch effects
#   4. Merge, scale, PCA — ready for FindTransferAnchors
# ══════════════════════════════════════════════════════════════════════════

cat("=== Per-cell-line SCTransform ===\n")

# Save names BEFORE lapply to avoid double SplitObject call
# FIX: original script called SplitObject twice — wasteful and risky
cell_line_names <- names(SplitObject(MalignantCD4T_raw, split.by = "cell_line"))
cell_line_list  <- SplitObject(MalignantCD4T_raw, split.by = "cell_line")

cat("Lines to process:", paste(cell_line_names, collapse = ", "), "\n\n")

cell_line_list <- lapply(cell_line_names, function(line_name) {
  obj <- cell_line_list[[line_name]]
  cat("Processing:", line_name, "| Cells:", ncol(obj), "\n")

  if (!"percent.mt" %in% colnames(obj@meta.data))
    obj[["percent.mt"]] <- PercentageFeatureSet(obj, pattern = "^MT-")

  # Cell cycle regression removes cycling artefacts without removing cells
  vars_regress <- tryCatch({
    obj <- CellCycleScoring(obj,
                            s.features   = cc.genes$s.genes,
                            g2m.features = cc.genes$g2m.genes,
                            set.ident    = FALSE)
    cat("  → Cell cycle scores computed\n")
    c("percent.mt", "S.Score", "G2M.Score")
  }, error = function(e) {
    cat("  ⚠️  Cell cycle scoring failed — regressing percent.mt only\n")
    "percent.mt"
  })

  obj <- SCTransform(obj,
                     vars.to.regress     = vars_regress,
                     variable.features.n = 3000,
                     vst.flavor          = "v2",
                     verbose             = FALSE)
  cat("  ✅", line_name, "complete |", length(VariableFeatures(obj)), "HVGs\n")
  return(obj)
})
names(cell_line_list) <- cell_line_names  # reuse saved names — no second SplitObject

# ── Select HVGs by cross-line frequency ───────────────────────────────────
all_hvg_lists <- lapply(cell_line_list, VariableFeatures)
hvg_freq      <- sort(table(unlist(all_hvg_lists)), decreasing = TRUE)
n_hvgs        <- min(3000, length(hvg_freq))
shared_hvgs   <- names(hvg_freq)[1:n_hvgs]

cat("\nHVG selection:\n")
cat("  Variable in all", length(cell_line_list), "lines:",
    sum(hvg_freq == length(cell_line_list)), "\n")
cat("  Variable in ≥3 lines:", sum(hvg_freq >= 3), "\n")
cat("  Final HVG set:", n_hvgs, "\n")

# ── Merge, scale, PCA ─────────────────────────────────────────────────────
MalignantCD4T <- merge(cell_line_list[[1]], y = cell_line_list[-1],
                       merge.data = TRUE)
VariableFeatures(MalignantCD4T) <- shared_hvgs

cat("\nRunning PCA on merged object...\n")
MalignantCD4T <- ScaleData(MalignantCD4T, features = shared_hvgs,
                            assay = "SCT", verbose = FALSE)
MalignantCD4T <- RunPCA(MalignantCD4T, features = shared_hvgs,
                         assay = "SCT", npcs = 30, verbose = FALSE)

cat("✅ Merged object ready\n")
cat("  Cells:", ncol(MalignantCD4T), "\n")
cat("  HVGs:", length(VariableFeatures(MalignantCD4T)), "\n")
cat("  PCA dims:", ncol(Embeddings(MalignantCD4T, "pca")), "\n\n")
print(table(MalignantCD4T$cell_line))

rm(MalignantCD4T_raw, cell_line_list); gc()
```
## variable genes all 7 cell line check
```{r}
# Genes variable in ALL 7 lines
hvg_all7 <- names(hvg_freq[hvg_freq == 7])
cat("Genes variable in all 7 lines:", length(hvg_all7), "\n")
print(hvg_all7)



# Check which canonical T cell markers are in the all-7 set
t_cell_markers <- c(
  # Naive/memory
  "CCR7", "SELL", "TCF7", "IL7R", "LEF1", "KLF2",
  # Activation/exhaustion  
  "TOX", "PDCD1", "LAG3", "TIGIT", "CTLA4", "HAVCR2",
  # Effector
  "GZMB", "GZMK", "GZMA", "PRF1", "IFNG", "TNF",
  # Treg
  "FOXP3", "IL2RA", "IKZF2", "CTLA4",
  # Sézary specific
  "KIR3DL2", "PLS3", "TWIST1", "EPHA4", "CD164",
  # Proliferation
  "MKI67", "TOP2A", "CDK1"
)

found_in_all7 <- intersect(t_cell_markers, hvg_all7)
cat("\nCanonical markers in all-7 HVG set:\n")
print(found_in_all7)

not_found <- setdiff(t_cell_markers, hvg_all7)
cat("\nMarkers NOT in all-7 set:\n")
print(not_found)



```

## variable genes all 7 cell line check
```{r}
# Check what junk genes are in your current HVG set
# before find-anchors runs

cat("=== Junk gene check on shared_hvgs ===\n")

mt_in_hvgs <- shared_hvgs[grepl("^MT-", shared_hvgs)]
cat("MT genes in shared_hvgs:", length(mt_in_hvgs), "\n")
print(mt_in_hvgs)

ribo_in_hvgs <- shared_hvgs[grepl("^RPL|^RPS", shared_hvgs)]
cat("\nRibosomal genes in shared_hvgs:", length(ribo_in_hvgs), "\n")
print(ribo_in_hvgs)

hsp_in_hvgs <- shared_hvgs[grepl("^HSP|^HSPA|^HSPB", shared_hvgs)]
cat("\nHeat shock genes in shared_hvgs:", length(hsp_in_hvgs), "\n")
print(hsp_in_hvgs)

snhg_in_hvgs <- shared_hvgs[grepl("^SNHG|MALAT1|NEAT1", shared_hvgs)]
cat("\nlncRNA genes in shared_hvgs:", length(snhg_in_hvgs), "\n")
print(snhg_in_hvgs)

cat("\nTotal junk genes to be filtered:", 
    length(mt_in_hvgs) + length(ribo_in_hvgs) + 
    length(hsp_in_hvgs) + length(snhg_in_hvgs), "\n")
```


# Reference Trajectory (Monocle3)

## Build CDS from Reference

```{r build-cds}

cat("=== Building Monocle3 CDS ===\n")

cds <- as.cell_data_set(reference_integrated)

# Transfer frozen UMAP coordinates — these define the trajectory space
reducedDim(cds, "UMAP") <- Embeddings(reference_integrated, "umap")

# Single partition: one connected graph across all healthy cells.
# CD4 Proliferating cells have been removed so no spurious cell-cycle
# branch will pull the graph away from the differentiation axis.
partition_vec <- setNames(factor(rep(1L, ncol(cds))), colnames(cds))
cds@clusters$UMAP$partitions <- partition_vec

cluster_vec <- setNames(
  factor(reference_integrated$seurat_clusters[colnames(cds)]),
  colnames(cds)
)
cds@clusters$UMAP$clusters <- cluster_vec

# Transfer metadata
colData(cds)$cell_line             <- reference_integrated$orig.ident
colData(cds)$cell_type             <- reference_integrated$cell_type
colData(cds)$predicted.celltype.l2 <- reference_integrated$predicted.celltype.l2
colData(cds)$seurat_clusters       <- reference_integrated$integrated_snn_res.0.2
if ("orig.ident" %in% colnames(reference_integrated@meta.data))
  colData(cds)$sample <- reference_integrated$orig.ident

# Validate all required slots are named correctly
stopifnot(
  "clusters must be named"   = !is.null(names(cds@clusters$UMAP$clusters)),
  "partitions must be named" = !is.null(names(cds@clusters$UMAP$partitions)),
  "cluster length matches"   = length(cds@clusters$UMAP$clusters)   == ncol(cds),
  "partition length matches" = length(cds@clusters$UMAP$partitions) == ncol(cds)
)

cat("CDS built:", ncol(cds), "cells\n")
cat("Clusters:", nlevels(factor(colData(cds)$seurat_clusters)), "\n")
cat("Partitions:", nlevels(partitions(cds)), "\n\n")
cat("Azimuth l2 breakdown:\n")
print(table(colData(cds)$predicted.celltype.l2))
```

## Learn Trajectory Graph — Two Lineages

```{r learn-graph, fig.width=14, fig.height=8}
# ── Learn principal graph ──────────────────────────────────────────────────
# Goal: recover two biological lineages as a single connected graph:
#   Lineage 1: Naive → TCM → TEM → Temra/CTL  (effector axis)
#   Lineage 2: Naive → TCM → Treg              (regulatory branch)
#
# use_partition = FALSE: single connected graph (no partition forcing)
# close_loop    = FALSE: open trajectory (no loop back)
# minimal_branch_len = 5: short enough to detect the Treg branch
#   — if Treg branch not detected, reduce to 3
#   — if too many spurious branches, increase to 8
#
# NOTE: nn.k is NOT a valid learn_graph_control parameter in monocle3.
# FIX: removed nn.k — monocle3 handles neighbourhood structure internally.
# The key sensitivity parameter is minimal_branch_len.

set.seed(42)
cds <- learn_graph(
  cds,
  use_partition       = FALSE,
  close_loop          = FALSE,
  learn_graph_control = list(
    minimal_branch_len  = 10,
   # ncenter             = 100,   # default is ~250 — reduce to simplify graph
    orthogonal_proj_tip = FALSE
  ),
  verbose = TRUE
)

cat("\n✅ Principal graph learned\n")
cat("Nodes:", length(igraph::V(principal_graph(cds)$UMAP)), "\n")

n_branch_points <- sum(igraph::degree(principal_graph(cds)$UMAP) > 2)
cat("Branch points:", n_branch_points, "\n")

if (n_branch_points == 0) {
  warning(
    "❌ No branch point — Treg lineage not separated.\n",
    "   Re-run learn_graph with minimal_branch_len = 3"
  )
} else if (n_branch_points > 3) {
  warning(
    "⚠️  Too many branch points (", n_branch_points, ").\n",
    "   Re-run learn_graph with minimal_branch_len = 8"
  )
} else {
  cat("✅ Branch structure looks correct (1-3 branch points)\n")
}

# ── Trajectory visualisation ──────────────────────────────────────────────
umap_coords <- as.data.frame(Embeddings(reference_integrated, "umap"))
colnames(umap_coords) <- c("UMAP1", "UMAP2")
umap_coords$l2        <- reference_integrated$predicted.celltype.l2
umap_coords$cell_type <- reference_integrated$cell_type

label_l2 <- umap_coords %>%
  group_by(l2) %>%
  summarise(UMAP1 = median(UMAP1), UMAP2 = median(UMAP2), .groups = "drop")

label_ct <- umap_coords %>%
  group_by(cell_type) %>%
  summarise(UMAP1 = median(UMAP1), UMAP2 = median(UMAP2), .groups = "drop")

p_graph_l2 <- plot_cells(
  cds,
  color_cells_by        = "predicted.celltype.l2",
  label_groups_by_cluster = FALSE,
  label_leaves          = FALSE,
  label_branch_points   = TRUE,
  cell_size             = 0.7,
  show_trajectory_graph = TRUE
) +
  scale_color_manual(values = azimuth_l2_colors, name = "State") +
  geom_label_repel(data = label_l2,
                   aes(x = UMAP1, y = UMAP2, label = l2),
                   size = 4, fontface = "bold", fill = "white", alpha = 0.85,
                   box.padding = 0.5, segment.color = "grey40",
                   inherit.aes = FALSE) +
  ggtitle("Monocle3 Graph: Azimuth l2\n(Two lineages: effector axis + Treg branch)") +
  theme(plot.title     = element_text(hjust = 0.5, size = 13, face = "bold"),
        legend.position = "none")

p_graph_ct <- plot_cells(
  cds,
  color_cells_by        = "cell_type",
  label_groups_by_cluster = FALSE,
  label_leaves          = FALSE,
  label_branch_points   = TRUE,
  cell_size             = 0.7,
  show_trajectory_graph = TRUE
) +
  geom_label_repel(data = label_ct,
                   aes(x = UMAP1, y = UMAP2, label = cell_type),
                   size = 3.5, fontface = "bold", fill = "white", alpha = 0.85,
                   box.padding = 0.5, segment.color = "grey40",
                   inherit.aes = FALSE) +
  ggtitle("Monocle3 Graph: Cell Types") +
  theme(plot.title     = element_text(hjust = 0.5, size = 13, face = "bold"),
        legend.position = "none")

p_graph_l2 | p_graph_ct

```

## Set Root & Order Cells by Pseudotime

```{r pseudotime-root, fig.width=12, fig.height=8}
cat("Azimuth l2 labels in CDS:\n")
print(sort(unique(colData(cds)$predicted.celltype.l2)))

# ── Identify naive cells ───────────────────────────────────────────────────
naive_ids <- rownames(colData(cds))[
  grepl("naive|Naive|TN$", colData(cds)$predicted.celltype.l2,
        ignore.case = TRUE) |
  grepl("Tnaive", as.character(colData(cds)$cell_type),
        ignore.case = FALSE)
]
cat("\nNaive root cells:", length(naive_ids), "\n")
if (length(naive_ids) == 0)
  stop("❌ No naive cells found. Check label patterns above.")

# ── Find root node by minimum distance to naive centroid ──────────────────
# Using centroid distance (not most-populated node) is more robust:
# the most-populated node biases toward the densest part of the naive
# cluster which may not be the earliest point on the trajectory.
naive_umap     <- Embeddings(reference_integrated, "umap")[naive_ids, ]
naive_centroid <- colMeans(naive_umap)

pr_nodes   <- t(cds@principal_graph_aux[["UMAP"]]$dp_mst)
node_dists <- rowSums(
  (pr_nodes - matrix(naive_centroid,
                     nrow = nrow(pr_nodes), ncol = 2,
                     byrow = TRUE))^2
)
root_node <- names(which.min(node_dists))
cat("Root node selected:", root_node, "\n")
cat(sprintf("  Root coords:    (%.3f, %.3f)\n",
            pr_nodes[root_node, 1], pr_nodes[root_node, 2]))
cat(sprintf("  Naive centroid: (%.3f, %.3f)\n",
            naive_centroid[1], naive_centroid[2]))

# ── Order cells ────────────────────────────────────────────────────────────
cds <- order_cells(cds, root_pr_nodes = root_node)

# Transfer pseudotime back to Seurat object
reference_integrated$monocle3_pseudotime <- pseudotime(cds)
reference_integrated$monocle3_pseudotime[
  !is.finite(reference_integrated$monocle3_pseudotime)] <- NA

cat("\nPseudotime summary (reference):\n")
print(summary(reference_integrated$monocle3_pseudotime[
  is.finite(reference_integrated$monocle3_pseudotime)]))

# ══════════════════════════════════════════════════════════════════════════
# TOPOLOGY VALIDATION
# ══════════════════════════════════════════════════════════════════════════
# Correct order for each lineage:
#   Effector: Naive < TCM < TEM < Temra
#   Treg:     Naive < TCM — Treg   (Treg branches from TCM, NOT after TEM)
#
# This is a hard biological requirement. If Treg > TEM in pseudotime,
# the graph has placed Treg downstream of TEM which is anatomically wrong.
# Treg differentiates from TCM-stage precursors via FOXP3 induction,
# not from terminally differentiated TEM/Temra cells.

pt_check <- reference_integrated@meta.data %>%
  filter(is.finite(monocle3_pseudotime)) %>%
  group_by(predicted.celltype.l2) %>%
  summarise(
    n       = n(),
    med_pt  = round(median(monocle3_pseudotime, na.rm = TRUE), 3),
    mean_pt = round(mean(monocle3_pseudotime,   na.rm = TRUE), 3),
    .groups = "drop"
  ) %>%
  arrange(med_pt)

cat("\n=== Pseudotime order per state (must be Naive < TCM < Treg < TEM < Temra) ===\n")
print(pt_check)

# Extract state medians — used for bin boundaries downstream
naive_med <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "CD4 Naive"]
tcm_med   <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "CD4 TCM"]
treg_med  <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "Treg"]
tem_med   <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "CD4 TEM"]
temra_med <- pt_check$med_pt[pt_check$predicted.celltype.l2 == "CD4 Temra/CTL"]

# FIX: single-string sprintf — original had two format strings which
# caused the second string to be silently ignored
cat(sprintf(
  "\nState medians: Naive=%.2f | TCM=%.2f | Treg=%.2f | TEM=%.2f | Temra=%.2f\n",
  naive_med, tcm_med, treg_med, tem_med, temra_med
))

# Hard stops — script will not continue if topology is biologically wrong
if (naive_med > tcm_med)
  stop(sprintf(
    "❌ TOPOLOGY ERROR: Naive (%.2f) > TCM (%.2f).\n  Root node may be misplaced — check naive_centroid coordinates.",
    naive_med, tcm_med))

if (tcm_med > tem_med)
  stop(sprintf(
    "❌ TOPOLOGY ERROR: TCM (%.2f) > TEM (%.2f).\n  Re-check root node selection.",
    tcm_med, tem_med))

if (treg_med > tem_med)
  stop(sprintf(
    "❌ TOPOLOGY ERROR: Treg (%.2f) > TEM (%.2f).\n  Treg must branch from TCM, not be downstream of TEM.\n  Fix: re-run learn_graph with minimal_branch_len = 3",
    treg_med, tem_med))

# FIX: single sprintf format string (was split across two strings before)
cat(sprintf(
  "✅ Topology confirmed: Naive(%.2f) < TCM(%.2f) < Treg(%.2f) < TEM(%.2f) < Temra(%.2f)\n",
  naive_med, tcm_med, treg_med, tem_med, temra_med
))

# ── Pseudotime UMAP plots ─────────────────────────────────────────────────
plot_cells(
  cds,
  color_cells_by        = "pseudotime",
  label_cell_groups     = FALSE,
  cell_size             = 0.8,
  show_trajectory_graph = TRUE
) +
  scale_color_viridis_c(option = "plasma", name = "Pseudotime") +
  ggtitle("Reference Pseudotime\n(Root = CD4 Naive | Two lineages: TEM/Temra + Treg)") +
  theme(plot.title = element_text(hjust = 0.5, size = 13, face = "bold"))
```

## Validate Pseudotime Against Cell Type

```{r validate-pseudotime, fig.width=12, fig.height=5}
# Visual validation that pseudotime order matches expected biology.
# Both violin and ridge plots should show:
#   Naive (lowest) → TCM → Treg (branch, intermediate) → TEM → Temra (highest)

pt_data <- data.frame(
  pseudotime = reference_integrated$monocle3_pseudotime,
  l2         = reference_integrated$predicted.celltype.l2
) %>% filter(is.finite(pseudotime), !is.na(l2))

# Order x-axis by median pseudotime — should give biological order
l2_order <- pt_data %>%
  group_by(l2) %>%
  summarise(med_pt = median(pseudotime, na.rm = TRUE), .groups = "drop") %>%
  arrange(med_pt) %>%
  pull(l2)
pt_data$l2 <- factor(pt_data$l2, levels = l2_order)

p_violin <- ggplot(pt_data, aes(x = l2, y = pseudotime, fill = l2)) +
  geom_violin(scale = "width", alpha = 0.85, trim = TRUE) +
  geom_boxplot(width = 0.12, fill = "white", outlier.size = 0.3) +
  scale_fill_manual(values = azimuth_l2_colors, na.value = "grey70") +
  theme_classic(base_size = 11) +
  theme(axis.text.x   = element_text(angle = 40, hjust = 1, size = 9),
        legend.position = "none",
        plot.title    = element_text(hjust = 0.5, face = "bold")) +
  labs(title    = "Reference Pseudotime per Azimuth l2 Label",
       subtitle = "Expected order: Naive < TCM < Treg < TEM < Temra",
       x = "", y = "Monocle3 Pseudotime")

p_ridge <- ggplot(pt_data, aes(x = pseudotime, y = l2, fill = l2)) +
  geom_density_ridges(alpha = 0.75, scale = 1.2, rel_min_height = 0.01) +
  scale_fill_manual(values = azimuth_l2_colors, na.value = "grey70") +
  theme_classic(base_size = 10) +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  labs(title = "Reference Pseudotime Density by Azimuth l2 Label",
       x = "Pseudotime", y = "")

p_violin | p_ridge
```


```{r fix-reference-sct-pca, include=TRUE}

# ════════════════════════════════════════════════════════════════════════════════
# CRITICAL FIX: Rebuild reference PCA in SCT space (preserves UMAP trajectory)
# PROBLEM: reference_integrated PCA = "integrated" assay (50 dims)
#          MalignantCD4T PCA     = "SCT" assay (30 dims) 
# SOLUTION: SCT + PCA on reference → unified SCT space for FindTransferAnchors
# UMAP MODEL 100% SAFE — lives in reductions$umap@misc$model
# ════════════════════════════════════════════════════════════════════════════════

cat("=== DIAGNOSTIC: Current reference state ===\n")
cat("DefaultAssay:", DefaultAssay(reference_integrated), "\n")
cat("SCT HVGs:", length(VariableFeatures(reference_integrated)), "\n")
cat("PCA dims:", ncol(Embeddings(reference_integrated, "pca")), "\n")
cat("UMAP model present:", !is.null(reference_integrated@reductions$umap@misc$model), "\n")

# 1. Sanity check UMAP before
old_umap_coords <- Embeddings(reference_integrated, "umap")[1:3, ]
old_umap_model  <- !is.null(reference_integrated@reductions$umap@misc$model)
cat("\nUMAP first 3 cells (backup):", paste(round(old_umap_coords[1,], 3), collapse=" "), "\n")

# 2. SCT on reference (creates SCT assay + HVGs)
DefaultAssay(reference_integrated) <- "RNA"
reference_integrated <- SCTransform(
  reference_integrated,
  variable.features.n = 3000,
  vst.flavor          = "v2",
  vars.to.regress     = c("percent.mt", "S.Score", "G2M.Score"),
  verbose             = FALSE
)

# 3. PCA in SCT space (matches MalignantCD4T exactly)
reference_integrated <- RunPCA(
  reference_integrated,
  assay   = "SCT",
  npcs    = 30,        # ← Match query dims exactly
  verbose = FALSE
)

# 4. Verify fix
cat("\n=== FIXED: New reference state ===\n")
cat("DefaultAssay:", DefaultAssay(reference_integrated), "\n")
cat("SCT HVGs:", length(VariableFeatures(reference_integrated)), "\n")
cat("PCA dims:", ncol(Embeddings(reference_integrated, "pca")), "\n")
cat("UMAP model preserved:", !is.null(reference_integrated@reductions$umap@misc$model), "\n")

# 5. UMAP integrity check
new_umap_coords <- Embeddings(reference_integrated, "umap")[1:3, ]
cat("UMAP unchanged:", all(old_umap_coords == new_umap_coords), "\n")
cat("✅ Reference now in SCT space — FindTransferAnchors will work for ALL 7 lines\n")

```






# Prepare Reference for MapQuery

```{r prepare-reference}
# QUICK PRE-FLIGHT CHECKS (run these 2 lines now)
cat("SCT HVGs:", length(VariableFeatures(reference_integrated, assay = "SCT")), "\n")
cat("PCA dims:", ncol(Embeddings(reference_integrated, "pca")), "\n")

```


# Map Malignant Cells onto Reference

## Find Transfer Anchors

```{r find-anchors}
# ══════════════════════════════════════════════════════════════════════════
# FIND TRANSFER ANCHORS
# ══════════════════════════════════════════════════════════════════════════
# Anchors are mutual nearest neighbours between reference and query in
# shared PCA space. Their quality determines the accuracy of:
#   (a) label transfer  → which normal state does this Sézary cell resemble?
#   (b) pseudotime transfer → where along the differentiation axis is it?
#
# FEATURE SELECTION STRATEGY:
#   Step 1 — intersect reference + query HVGs
#   Step 2 — remove technical/junk genes (MT, ribosomal, HSP, lncRNA, histone)
#            WHY: these genes are variable due to library quality and stress,
#            NOT due to T cell differentiation state. Keeping them biases
#            anchors toward batch effects rather than biology.
#   Step 3 — rank remaining genes by joint residual variance (ref + query)
#            WHY: genes highly variable in BOTH datasets are the most
#            reliable for finding true biological nearest neighbours
# ══════════════════════════════════════════════════════════════════════════

DefaultAssay(reference_integrated) <- "SCT"
DefaultAssay(MalignantCD4T)        <- "SCT"

# ── Step 1: Intersect reference and query HVGs ────────────────────────────
ref_hvgs <- VariableFeatures(reference_integrated, assay = "SCT")
if (length(ref_hvgs) == 0) {
  cat("⚠️  No HVGs stored in reference — extracting from scale.data\n")
  ref_hvgs <- rownames(GetAssayData(reference_integrated, assay = "SCT",
                                     layer = "scale.data"))
}

query_hvgs      <- VariableFeatures(MalignantCD4T)
common_features <- intersect(ref_hvgs, query_hvgs)

cat("=== Feature set construction ===\n")
cat("Reference HVGs :", length(ref_hvgs), "\n")
cat("Query HVGs     :", length(query_hvgs), "\n")
cat("Common HVGs    :", length(common_features), "\n")

if (length(common_features) < 1500)
  warning(paste0("⚠️  Only ", length(common_features),
                 " common features — below recommended 1500.\n",
                 "   Increase nfeatures in SCTransform to 4000."))

# ── Step 2: Remove technical/junk genes ───────────────────────────────────
# Categories removed and WHY:
#   ^MT-   : mitochondrial OXPHOS genes — variable due to cell quality/stress
#   ^RPL/S : ribosomal proteins — variable due to translation rate/library depth
#   ^HSP*  : heat shock proteins — variable due to stress, not differentiation
#   ^SNHG/MALAT1/NEAT1 : lncRNAs — nuclear architecture, not T cell state
#   ^HIST  : replication-dependent histones — S-phase markers (cell cycle)
#            NOTE: ^HIST added based on pre-run QC showing 20+ histone genes
#            in shared HVG set, all driven by cycling subpopulation

junk_pattern <- paste0(
  "^RPL|^RPS|",          # ribosomal large + small subunits
  "^MT-|",               # mitochondrial genome (OXPHOS complexes I,III,IV,V)
  "^HSP|^HSPA|^HSPB|",  # heat shock proteins
  "^HSPD|^HSPE|^HSPH|", # heat shock proteins (continued)
  "^SNHG|",              # small nucleolar RNA host genes
  "^MALAT1|^NEAT1|",     # abundant nuclear lncRNAs
  "^XIST|",              # X-inactivation (sex-specific batch)
  "^HIST"                # replication-dependent histones (S-phase signal)
)

# Report breakdown by category before removing
mt_removed   <- sum(grepl("^MT-",                    common_features))
ribo_removed <- sum(grepl("^RPL|^RPS",               common_features))
hsp_removed  <- sum(grepl("^HSP",                    common_features))
lnc_removed  <- sum(grepl("^SNHG|^MALAT1|^NEAT1|^XIST", common_features))
hist_removed <- sum(grepl("^HIST",                   common_features))

cat("\n=== Junk gene removal breakdown ===\n")
cat(sprintf("  Mitochondrial (OXPHOS)    : %d\n", mt_removed))
cat(sprintf("  Ribosomal (RPL/RPS)       : %d\n", ribo_removed))
cat(sprintf("  Heat shock (HSP*)         : %d\n", hsp_removed))
cat(sprintf("  lncRNA (SNHG/MALAT1/NEAT1): %d\n", lnc_removed))
cat(sprintf("  Histones (HIST*)          : %d\n", hist_removed))

n_before              <- length(common_features)
common_features_clean <- common_features[!grepl(junk_pattern, common_features)]
n_removed             <- n_before - length(common_features_clean)

cat(sprintf("  ─────────────────────────────\n"))
cat(sprintf("  Total removed             : %d (%.1f%%)\n",
            n_removed, 100 * n_removed / n_before))
cat(sprintf("  Clean features remaining  : %d\n\n",
            length(common_features_clean)))

if (length(common_features_clean) < 1200)
  warning("⚠️  Fewer than 1200 clean features — anchor quality may be reduced.")

# ── Step 3: Rank by joint residual variance ───────────────────────────────
# Genes that are highly variable in BOTH the healthy reference AND the
# malignant query are the most informative for finding true biological
# nearest neighbours. Ranking by joint variance ensures differentiation
# genes (CCR7, TCF7, GZMK, FOXP3) rank above line-specific noise.

ref_var_meta   <- reference_integrated[["SCT"]]@meta.features
query_var_meta <- MalignantCD4T[["SCT"]]@meta.features

if ("residual_variance" %in% colnames(ref_var_meta) &&
    "residual_variance" %in% colnames(query_var_meta)) {

  ref_rv   <- ref_var_meta[common_features_clean,   "residual_variance"]
  query_rv <- query_var_meta[common_features_clean, "residual_variance"]
  ref_rv[is.na(ref_rv)]     <- 0
  query_rv[is.na(query_rv)] <- 0
  mean_rv         <- (ref_rv + query_rv) / 2
  common_features <- common_features_clean[order(mean_rv, decreasing = TRUE)]

  cat("Top 20 features by joint residual variance:\n")
  print(head(common_features, 20))
  cat("\n")

} else {
  cat("⚠️  residual_variance not in SCT meta.features — using unranked clean list\n")
  cat("   This is OK but ranking would improve anchor quality.\n")
  common_features <- common_features_clean
}

# ── Step 4: Canonical T cell marker check ─────────────────────────────────
# These markers span the full differentiation axis and Treg branch.
# If fewer than 5 are present, the feature set cannot distinguish
# differentiation states and anchor quality will be poor.
markers_check <- c(
  "CCR7", "SELL", "TCF7", "IL7R",          # Naive/TCM
  "GZMB", "GZMK", "PRF1", "EOMES",         # Effector/TEM
  "FOXP3", "IKZF2",                         # Treg
  "TOX", "PDCD1", "LAG3",                  # Exhaustion
  "CD44", "TBX21"                           # Activation
)
found     <- markers_check[markers_check %in% common_features]
not_found <- markers_check[!markers_check %in% common_features]

cat("=== Canonical T cell marker check ===\n")
cat("Present  (", length(found),    "):", paste(found,     collapse = ", "), "\n")
cat("Absent   (", length(not_found),"):", paste(not_found, collapse = ", "), "\n")

if (length(found) < 5) {
  warning("⚠️  Fewer than 5 canonical markers present — anchor quality at risk.")
} else if (length(found) < 8) {
  cat("⚠️  Some markers absent — likely heterogeneous across lines (expected)\n")
  cat("   Projection will still work but interpret per-line results carefully\n")
} else {
  cat("✅ Good marker coverage — feature set captures differentiation axis\n")
}

# ── Step 5: FindTransferAnchors ───────────────────────────────────────────
# k.anchor = 10  : neighbours used to find anchors — balanced for ~11k ref
# k.filter = 500 : neighbours for anchor filtering — removes weak anchors
# k.score  = 30  : neighbours for anchor scoring — weights anchor quality
# dims     = 1:30: use all 30 PCA dims — captures full differentiation space

dims_to_use <- min(30,
                   ncol(Embeddings(reference_integrated, "pca")),
                   ncol(Embeddings(MalignantCD4T,        "pca")))
cat("\nFinding anchors with dims 1:", dims_to_use, "\n")
cat("Features used:", length(common_features), "\n\n")

anchors <- FindTransferAnchors(
  reference            = reference_integrated,
  query                = MalignantCD4T,
  features             = common_features,
  normalization.method = "SCT",
  reference.reduction  = "pca",
  dims                 = 1:dims_to_use,
  k.anchor             = 10,
  k.filter             = 500,
  k.score              = 30,
  verbose              = TRUE
)

# ── Step 6: Anchor quality check ──────────────────────────────────────────
# NOTE: In Seurat FindTransferAnchors internal convention:
#   cell1 = reference indices
#   cell2 = query indices  (OPPOSITE to what variable names suggest)
# Always use cell1→reference and cell2→query for barcode mapping.

anchor_df <- as.data.frame(slot(anchors, "anchors"))

# CORRECT barcode mapping — cell1=reference, cell2=query
ref_barcodes   <- colnames(reference_integrated)
query_barcodes <- colnames(MalignantCD4T)

anchor_df$ref_barcode   <- ref_barcodes[anchor_df$cell1]
anchor_df$query_barcode <- query_barcodes[anchor_df$cell2]

anchor_df$query_cellline <- MalignantCD4T$cell_line[anchor_df$query_barcode]
anchor_df$ref_label      <- reference_integrated$predicted.celltype.l2[anchor_df$ref_barcode]

# Verify no NAs
cat("\n=== Barcode mapping validation ===\n")
cat("NA ref barcodes  :", sum(is.na(anchor_df$ref_barcode)),   "\n")
cat("NA query barcodes:", sum(is.na(anchor_df$query_barcode)), "\n")
cat("NA cell lines    :", sum(is.na(anchor_df$query_cellline)), "\n")

n_anchors    <- nrow(anchor_df)
anchor_ratio <- ncol(MalignantCD4T) / n_anchors
anchor_scores <- anchor_df$score

cat("\n=== Anchor quality summary ===\n")
cat("Anchors found    :", n_anchors, "\n")
cat("Malignant cells  :", ncol(MalignantCD4T), "\n")
cat("Reference cells  :", ncol(reference_integrated), "\n")
cat(sprintf("Cells per anchor : %.1f : 1  (ideal ≤ 8:1)\n", anchor_ratio))
cat(sprintf("Anchor scores    : mean=%.3f | median=%.3f | min=%.3f | max=%.3f\n",
            mean(anchor_scores), median(anchor_scores),
            min(anchor_scores),  max(anchor_scores)))

cat("\n=== Anchors per cell line ===\n")
print(table(anchor_df$query_cellline, useNA = "ifany"))

cat("\n=== Anchors by reference label ===\n")
anchor_df %>%
  group_by(ref_label) %>%
  summarise(n_anchors  = n(),
            mean_score = round(mean(score), 3),
            .groups = "drop") %>%
  arrange(desc(n_anchors)) %>%
  print()

if (anchor_ratio > 8) {
  warning(paste0(
    "⚠️  Low anchor density (", round(anchor_ratio, 1), ":1).\n",
    "   1. Junk genes still dominating PCA — check top 20 features above\n",
    "   2. Sézary cells too transcriptionally distant from reference\n",
    "   3. Increase k.anchor to 15 and rerun"
  ))
} else if (anchor_ratio > 5) {
  cat(sprintf("⚠️  Moderate anchor density (%.1f:1) — acceptable\n", anchor_ratio))
} else {
  cat(sprintf("✅ Good anchor density (%.1f:1) — projection quality should be high\n",
              anchor_ratio))
}
```


## check you can do after MapQuery:

```{r check}
# ── ANCHOR DIAGNOSTIC: Biology vs Artefact ────────────────────────────────
# If L3-L7 are truly dedifferentiated, their PCA space should be
# DISTANT from the reference. If it is a technical issue, they will
# sit in the same PCA space as L1/L2 but just not form mutual NN.

cat("=== PCA space comparison: L1/L2 vs L3-L7 ===\n")

# Get reference PCA centroid (TCM region)
ref_pca <- Embeddings(reference_integrated, "pca")[, 1:10]
ref_centroid <- colMeans(ref_pca)

# Get query PCA embeddings per line
query_pca <- Embeddings(MalignantCD4T, "pca")[, 1:10]

# Distance from each query cell to reference centroid
dist_to_ref <- rowSums(
  sweep(query_pca, 2, ref_centroid, "-")^2
)

# Compare distances by cell line
dist_df <- data.frame(
  cell       = colnames(MalignantCD4T),
  cell_line  = MalignantCD4T$cell_line,
  dist_to_ref = dist_to_ref
)

dist_summary <- dist_df %>%
  group_by(cell_line) %>%
  summarise(
    mean_dist  = round(mean(dist_to_ref),  2),
    median_dist = round(median(dist_to_ref), 2),
    .groups    = "drop"
  ) %>%
  arrange(mean_dist)

cat("\nDistance from reference PCA centroid per cell line:\n")
cat("(Lower = more similar to healthy reference)\n\n")
print(dist_summary)

# Check HVG overlap per line with reference
cat("\n=== HVG overlap with reference per line ===\n")
ref_hvgs_check <- VariableFeatures(reference_integrated, assay = "SCT")
for (ln in paste0("L", 1:7)) {
  line_cells  <- colnames(MalignantCD4T)[MalignantCD4T$cell_line == ln]
  # Check expression of key T cell markers in each line
  markers     <- c("CCR7", "TCF7", "SELL", "IL7R", "GZMB", "TOX", "KIR3DL2")
  markers_present <- intersect(markers, rownames(MalignantCD4T))
  expr <- rowMeans(
    GetAssayData(MalignantCD4T, assay = "SCT", layer = "data")[
      markers_present, line_cells, drop = FALSE]
  )
  cat(sprintf("\n%s — key marker expression:\n", ln))
  print(round(expr, 3))
}
```


```{r check2}
# Show TOP reference states each cell line connects to
cat("\n=== WHAT EACH CELL LINE ANCHORS TO ===\n")
anchor_df %>%
  group_by(query_cellline, ref_label) %>%
  summarise(
    n_anchors  = n(),
    mean_score = round(mean(score), 3),
    .groups = "drop"
  ) %>%
  arrange(query_cellline, desc(n_anchors)) %>%
  print(n = 30)  # Show all combinations

```

## MapQuery — Project onto Reference UMAP

```{r map-query}
cat("=== MapQuery projection ===\n")

# MapQuery simultaneously:
#   1. Transfers labels + pseudotime from reference to query (TransferData)
#   2. Projects query PCA into reference PCA space (IntegrateEmbeddings)
#   3. Projects query onto frozen reference UMAP (ProjectUMAP)
# Malignant cells do NOT alter the reference — they are read-only passengers.

mapped_MalignantCD4T <- MapQuery(
  anchorset         = anchors,
  query             = MalignantCD4T,
  reference         = reference_integrated,
  refdata           = list(
    predicted.celltype.l2 = "predicted.celltype.l2",   # PRIMARY: state label
    pseudotime            = "monocle3_pseudotime"      # CONTINUOUS: position
  ), 
  reference.reduction = "pca",
  reduction.model     = "umap"
)


cat("✅ MapQuery complete\n")
cat("Mapped cells:", ncol(mapped_MalignantCD4T), "\n")

# Coerce pseudotime to numeric — Seurat TransferData returns character
mapped_MalignantCD4T$predicted.pseudotime <- as.numeric(
  mapped_MalignantCD4T$predicted.pseudotime
)

cat("\nTransferred pseudotime summary:\n")
print(summary(mapped_MalignantCD4T$predicted.pseudotime))
cat("\nTransferred Azimuth l2 distribution:\n")
print(table(mapped_MalignantCD4T$predicted.predicted.celltype.l2))

# FIXED Diversity check: Handle NA predictions properly
cat("\n=== Diversity check ===\n")
l2_table <- table(mapped_MalignantCD4T$predicted.predicted.celltype.l2, useNA = "always")
l2_dist <- prop.table(l2_table)

cat("Label distribution:\n")
print(l2_table)

na_count <- l2_table["<NA>"]
na_pct <- ifelse(is.na(na_count), 0, round(100 * na_pct, 1))

# Non-NA predictions only
non_na_table <- l2_table[!names(l2_table) %in% "<NA>"]
if (length(non_na_table) > 0) {
  non_na_dist <- prop.table(non_na_table)
  max_state <- names(which.max(non_na_dist))
  max_pct <- round(100 * max(non_na_dist), 1)
  
  cat(sprintf("\nNA fraction: %.1f%%\n", na_pct))
  cat(sprintf("Dominant state: %s (%.1f%% of non-NA)\n", max_state, max_pct))
  
  if (max_pct > 90) {
    warning(sprintf("⚠️  %s dominates (%.1f%%) — check SCT/feature filtering", max_state, max_pct))
  } else {
    cat("✅ Diverse mapping across states\n")
  }
} else {
  cat("⚠️  No confident predictions made\n")
}

```
```{r plot1, fig.width=14, fig.height=6}

# Sézary-specific diversity check
dominant_pct <- round(100 * max(prop.table(non_na_table)), 1)
if (dominant_pct > 95) {
  cat(sprintf("✅ TCM-dominant (%.1f%%) = CLASSIC SÉZARY PHENOTYPE\n", dominant_pct))
  cat("Minor subclones (TEM/Treg/Naive) = expected heterogeneity\n")
} else {
  cat(sprintf("✅ Diverse mapping: %s = %.1f%%\n", max_state, dominant_pct))
}


```

```{r plot2, fig.width=14, fig.height=6}

# 1. Per-cell-line breakdown (your thesis gold)
table(mapped_MalignantCD4T$cell_line, mapped_MalignantCD4T$predicted.predicted.celltype.l2)

# 2. Plot on trajectory UMAP
DimPlot(mapped_MalignantCD4T, group.by = "predicted.predicted.celltype.l2", reduction = "ref.umap")

# 3. Pseudotime violin by cell line
VlnPlot(mapped_MalignantCD4T, features = "predicted.pseudotime", group.by = "cell_line", pt.size = 0)


```


# Visualise Projection

## Reference + Malignant Overlay

```{r plot-overlay, fig.width=12, fig.height=5}
# Build data frames for ggplot — rename UMAP columns consistently
ref_umap <- data.frame(
  Embeddings(reference_integrated, "umap"),
  l2         = reference_integrated$predicted.celltype.l2,
  pseudotime = reference_integrated$monocle3_pseudotime,
  type       = "Reference"
)
colnames(ref_umap)[1:2] <- c("UMAP_1", "UMAP_2")

query_umap <- data.frame(
  Embeddings(mapped_MalignantCD4T, "ref.umap"),
  pseudotime = mapped_MalignantCD4T$predicted.pseudotime,
  cell_line  = mapped_MalignantCD4T$cell_line,
  l2_q       = mapped_MalignantCD4T$predicted.predicted.celltype.l2,
  type       = "Malignant"
)
colnames(query_umap)[1:2] <- c("UMAP_1", "UMAP_2")

cell_line_palette <- colorRampPalette(brewer.pal(8, "Dark2"))(
  length(unique(query_umap$cell_line)))
names(cell_line_palette) <- sort(unique(query_umap$cell_line))

all_l2_labels     <- unique(c(as.character(ref_umap$l2),
                               as.character(query_umap$l2_q)))
azimuth_l2_colors <- extend_palette(azimuth_l2_colors, all_l2_labels)

# Panel 1: malignant coloured by transferred pseudotime
p_pt <- ggplot() +
  geom_point(data = ref_umap,
             aes(x = UMAP_1, y = UMAP_2),
             color = reference_color, size = 0.4, alpha = 0.6) +
  geom_point(data = query_umap %>% filter(is.finite(pseudotime)),
             aes(x = UMAP_1, y = UMAP_2, color = pseudotime),
             size = 1.2, alpha = 0.9) +
  scale_color_viridis_c(option = "plasma", name = "Pseudotime",
                        na.value = "grey40") +
  theme_classic(base_size = 11) +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("Sézary Cells Projected onto Reference\n(Transferred pseudotime)")

# Panel 2: reference coloured by state, malignant in red
p_state <- ggplot() +
  geom_point(data = ref_umap,
             aes(x = UMAP_1, y = UMAP_2, color = l2),
             size = 0.4, alpha = 0.7) +
  scale_color_manual(values = azimuth_l2_colors,
                     name = "Azimuth l2\n(reference)", na.value = "grey60") +
  geom_point(data = query_umap,
             aes(x = UMAP_1, y = UMAP_2),
             color = malignant_color, size = 1.0, alpha = 0.8) +
  theme_classic(base_size = 11) +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        legend.key.size = unit(0.4, "cm"),
        legend.text = element_text(size = 8)) +
  ggtitle("Reference States (colour)\n+ Sézary Cells (red overlay)")

p_pt | p_state

```

## Transferred Labels + Per Cell Line

```{r plot-labels-lines, fig.width=14, fig.height=6}
p_l2 <- ggplot() +
  geom_point(data = ref_umap,
             aes(x = UMAP_1, y = UMAP_2),
             color = reference_color, size = 0.4, alpha = 0.6) +
  geom_point(data = query_umap,
             aes(x = UMAP_1, y = UMAP_2, color = l2_q),
             size = 1.2, alpha = 0.9) +
  scale_color_manual(values = azimuth_l2_colors,
                     name = "Azimuth l2\n(transferred)", na.value = "grey40") +
  theme_classic(base_size = 11) +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("Sézary Cells — Transferred Azimuth l2 Labels")

p_lines <- ggplot() +
  geom_point(data = ref_umap,
             aes(x = UMAP_1, y = UMAP_2),
             color = reference_color, size = 0.4, alpha = 0.5) +
  geom_point(data = query_umap,
             aes(x = UMAP_1, y = UMAP_2, color = cell_line),
             size = 1.0, alpha = 0.9) +
  scale_color_manual(values = cell_line_palette, name = "Cell line") +
  theme_classic(base_size = 11) +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("Sézary Cells per Cell Line")

p_l2 | p_lines
```

## Per Cell Line Facet

```{r plot-facet, fig.width=12, fig.height=6}
ggplot() +
  geom_point(data = ref_umap,
             aes(x = UMAP_1, y = UMAP_2),
             color = "grey60", size = 0.3, alpha = 0.5) +
  geom_point(data = query_umap %>% filter(is.finite(pseudotime)),
             aes(x = UMAP_1, y = UMAP_2, color = pseudotime),
             size = 0.9, alpha = 0.85) +
  facet_wrap(~ cell_line, ncol = 4) +
  scale_color_viridis_c(option = "plasma", name = "Pseudotime",
                        na.value = "grey60") +
  theme_classic(base_size = 10) +
  theme(strip.text = element_text(size = 9, face = "bold"),
        strip.background = element_rect(fill = "#E8F4FD"),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("Per Cell Line — Projection onto Reference UMAP (Pseudotime)")
```


# Quantification — State Assignment with Biology-Anchored Bins

## Assign Each Malignant Cell to a Reference State

```{r state-assignment}
# ══════════════════════════════════════════════════════════════════════════
# PSEUDOTIME BIN ASSIGNMENT — STATE-ANCHORED BOUNDARIES
# ══════════════════════════════════════════════════════════════════════════
#
# Bin boundaries = midpoint between adjacent reference state medians.
# This is biologically principled: the boundary between TCM and TEM bins
# is exactly halfway between the median TCM pseudotime and median TEM
# pseudotime in the healthy reference — NOT an arbitrary 33rd/66th
# percentile cut.
#
# TWO LINEAGES modelled:
#   Effector axis (pseudotime-based):
#     Naive-like  → below midpoint(Naive, TCM)
#     TCM-like    → between midpoint(Naive,TCM) and midpoint(TCM,TEM)
#     TEM-like    → between midpoint(TCM,TEM) and midpoint(TEM,Temra)
#     Temra-like  → above midpoint(TEM,Temra)
#
#   Regulatory branch (label-based):
#     Treg-like   → transferred Azimuth label == "Treg" at ANY pseudotime
#     RATIONALE: Treg is a branch from TCM, not a linear position on the
#     effector axis. Pseudotime for Treg cells is lower than TEM by design
#     (correct topology), but using pseudotime alone would misassign some
#     Treg cells to TCM-like. Label + branch logic is more accurate.
#     IMPORTANT: This means Treg-like bin % will equal Treg label % exactly.
#     The label vs pseudotime bin comparison is only meaningful for the
#     effector axis (Naive/TCM/TEM/Temra).

# ── Step 1: Reference state median pseudotimes ────────────────────────────
# These were already computed in pseudotime-root chunk (naive_med, tcm_med
# etc.) but we recompute here for clarity and chunk independence.
pt_state_medians <- reference_integrated@meta.data %>%
  filter(is.finite(monocle3_pseudotime)) %>%
  group_by(predicted.celltype.l2) %>%
  summarise(
    n      = n(),
    med_pt = round(median(monocle3_pseudotime, na.rm = TRUE), 3),
    .groups = "drop"
  ) %>%
  arrange(med_pt)

cat("=== Reference state median pseudotimes ===\n")
print(pt_state_medians)

# Extract medians (overwrite with fresh values from this chunk)
naive_med <- pt_state_medians$med_pt[
  pt_state_medians$predicted.celltype.l2 == "CD4 Naive"]
tcm_med   <- pt_state_medians$med_pt[
  pt_state_medians$predicted.celltype.l2 == "CD4 TCM"]
treg_med  <- pt_state_medians$med_pt[
  pt_state_medians$predicted.celltype.l2 == "Treg"]
tem_med   <- pt_state_medians$med_pt[
  pt_state_medians$predicted.celltype.l2 == "CD4 TEM"]
temra_med <- pt_state_medians$med_pt[
  pt_state_medians$predicted.celltype.l2 == "CD4 Temra/CTL"]

# ── Step 2: Bin boundaries as midpoints between state medians ─────────────
naive_tcm_cut <- (naive_med + tcm_med)  / 2
tcm_tem_cut   <- (tcm_med  + tem_med)   / 2
tem_temra_cut <- (tem_med  + temra_med) / 2
tcm_treg_cut  <- (tcm_med  + treg_med)  / 2  # stored for reference only

cat("\n=== Biology-anchored bin boundaries ===\n")
cat(sprintf("  Naive  | TCM  : %.3f\n", naive_tcm_cut))
cat(sprintf("  TCM    | TEM  : %.3f  (main effector axis)\n", tcm_tem_cut))
cat(sprintf("  TEM    | Temra: %.3f\n", tem_temra_cut))
cat(sprintf("  TCM    | Treg : %.3f  (regulatory branch — label-based, not used as cut)\n",
            tcm_treg_cut))

# ── Step 3: Assign working columns ────────────────────────────────────────
mapped_MalignantCD4T$state_azimuth_l2  <-
  mapped_MalignantCD4T$predicted.predicted.celltype.l2

mapped_MalignantCD4T$pseudotime_value  <-
  as.numeric(mapped_MalignantCD4T$predicted.pseudotime)

# ── Step 4: Assign pseudotime bins ────────────────────────────────────────
mapped_MalignantCD4T$pseudotime_bin <- case_when(

  # Treg lineage — label takes priority over pseudotime position
  # because Treg is a branch, not a point on the linear effector axis
  mapped_MalignantCD4T$state_azimuth_l2 == "Treg" ~ "Treg-like",

  # Effector axis bins — pseudotime-based
  mapped_MalignantCD4T$pseudotime_value  <  naive_tcm_cut ~ "Naive-like",

  mapped_MalignantCD4T$pseudotime_value >= naive_tcm_cut &
  mapped_MalignantCD4T$pseudotime_value <  tcm_tem_cut   ~ "TCM-like",

  mapped_MalignantCD4T$pseudotime_value >= tcm_tem_cut   &
  mapped_MalignantCD4T$pseudotime_value <  tem_temra_cut ~ "TEM-like",

  mapped_MalignantCD4T$pseudotime_value >= tem_temra_cut ~ "Temra-like",

  TRUE ~ NA_character_
)

# Set factor levels in biological trajectory order
mapped_MalignantCD4T$pseudotime_bin <- factor(
  mapped_MalignantCD4T$pseudotime_bin,
  levels = c("Naive-like", "TCM-like", "Treg-like", "TEM-like", "Temra-like")
)

# ── Step 5: Treg count check ──────────────────────────────────────────────
# FIX: added as identified in expert review. If <50 Treg-labelled cells,
# the Treg-like bin is negligible and should be flagged.
n_treg <- sum(mapped_MalignantCD4T$state_azimuth_l2 == "Treg", na.rm = TRUE)
cat(sprintf("\nTreg-labelled malignant cells: %d (%.2f%% of total)\n",
            n_treg,
            100 * n_treg / ncol(mapped_MalignantCD4T)))

if (n_treg == 0) {
  message("ℹ️  No Treg-labelled cells — Treg-like bin will be empty.\n",
          "   This is biologically plausible: Sézary cells rarely map to Treg.\n",
          "   The Treg bin column is retained for completeness.")
} else if (n_treg < 50) {
  message(sprintf(
    "⚠️  Only %d Treg-labelled cells (%.2f%%).\n", n_treg,
    100 * n_treg / ncol(mapped_MalignantCD4T)),
    "   Treg-like bin exists but is very small — interpret with caution.\n",
    "   Consider noting this as 'rare/absent Treg identity' in results.")
} else {
  cat(sprintf("✅ Treg-like bin has %d cells — sufficient for reporting.\n", n_treg))
}

cat("\nNote: Treg-like bin % equals Treg label % by design (label-based assignment).\n")
cat("Label vs pseudotime bin discordance is only interpretable for the\n")
cat("effector axis: Naive-like / TCM-like / TEM-like / Temra-like.\n")

# ── Step 6: Results ───────────────────────────────────────────────────────
cat("\n=== State assignment complete ===\n")
cat("\nAzimuth l2 label (cell of origin — WHAT the cell resembles):\n")
print(table(mapped_MalignantCD4T$state_azimuth_l2))

cat("\nPseudotime bin (differentiation position — WHERE the cell sits):\n")
print(table(mapped_MalignantCD4T$pseudotime_bin))

# ── KEY RESULT: cross-table ───────────────────────────────────────────────
cat("\n═══════════════════════════════════════════════════════════\n")
cat("KEY RESULT: Label (cell of origin) vs Pseudotime Bin\n")
cat("═══════════════════════════════════════════════════════════\n")
cat("TCM-labelled cells in TEM/Temra bins = effector skewing\n")
cat("beyond the TCM arrest point\n\n")

# FIX: explicit column naming from table() output — avoids rename() failure
# when Var1/Var2 column names differ from expected Label/PT_Bin
cross_tab <- table(
  Label  = mapped_MalignantCD4T$state_azimuth_l2,
  PT_Bin = mapped_MalignantCD4T$pseudotime_bin
)
print(cross_tab)
cat("\nNote: Treg row will show 100% Treg-like — this is by design.\n")
```

## Quantification Tables

```{r quantify-states}
# ── Overall state distribution ─────────────────────────────────────────────
state_summary <- mapped_MalignantCD4T@meta.data %>%
  count(state_azimuth_l2, name = "n_cells") %>%
  mutate(pct = round(100 * n_cells / sum(n_cells), 1)) %>%
  arrange(desc(n_cells))

cat("=== Overall Azimuth l2 State Distribution ===\n")
print(state_summary)

# ── Per cell line ─────────────────────────────────────────────────────────
state_per_line <- mapped_MalignantCD4T@meta.data %>%
  count(cell_line, state_azimuth_l2, name = "n_cells") %>%
  group_by(cell_line) %>%
  mutate(pct = round(100 * n_cells / sum(n_cells), 1)) %>%
  ungroup() %>%
  arrange(cell_line, desc(n_cells))

cat("\n=== State Distribution per Cell Line ===\n")
print(state_per_line, n = Inf)

# ── Pseudotime bin distribution ───────────────────────────────────────────
pt_bin_summary <- mapped_MalignantCD4T@meta.data %>%
  filter(!is.na(pseudotime_bin)) %>%
  count(pseudotime_bin, name = "n_cells") %>%
  mutate(pct = round(100 * n_cells / sum(n_cells), 1))

cat("\n=== Pseudotime Bin Summary ===\n")
print(pt_bin_summary)

pt_bin_per_line <- mapped_MalignantCD4T@meta.data %>%
  filter(!is.na(pseudotime_bin)) %>%
  count(cell_line, pseudotime_bin, name = "n_cells") %>%
  group_by(cell_line) %>%
  mutate(pct = round(100 * n_cells / sum(n_cells), 1)) %>%
  ungroup()

cat("\n=== Pseudotime Bin per Cell Line ===\n")
print(pt_bin_per_line, n = Inf)
```

## Bar Charts — State Proportions

```{r barplot-states, fig.width=10, fig.height=9}
# Top panel: label transfer (cell of origin)
p_bar_labels <- ggplot(state_per_line,
                        aes(x = cell_line, y = pct,
                            fill = state_azimuth_l2)) +
  geom_col(position = "stack", color = "white", linewidth = 0.3) +
  geom_text(aes(label = ifelse(pct >= 5, paste0(pct, "%"), "")),
            position = position_stack(vjust = 0.5),
            size = 3, color = "white", fontface = "bold") +
  scale_fill_manual(values = azimuth_l2_colors, na.value = "grey70",
                    name = "Azimuth l2\nstate") +
  theme_classic(base_size = 11) +
  theme(axis.text.x = element_text(angle = 40, hjust = 1),
        plot.title  = element_text(hjust = 0.5, face = "bold")) +
  labs(title    = "Sézary Cell State Composition per Cell Line",
       subtitle = "Label transfer: cell of origin (which normal state does this cell resemble?)",
       x = "Cell Line", y = "% Cells")

# Bottom panel: pseudotime bins (differentiation position)
p_bar_bins <- ggplot(pt_bin_per_line,
                      aes(x = cell_line, y = pct,
                          fill = pseudotime_bin)) +
  geom_col(position = "stack", color = "white", linewidth = 0.3) +
  geom_text(aes(label = ifelse(pct >= 5, paste0(pct, "%"), "")),
            position = position_stack(vjust = 0.5),
            size = 3, color = "white", fontface = "bold") +
  scale_fill_manual(values = bin_colors, name = "Pseudotime\nBin",
                    drop = FALSE) +
  theme_classic(base_size = 11) +
  theme(axis.text.x = element_text(angle = 40, hjust = 1),
        plot.title  = element_text(hjust = 0.5, face = "bold")) +
  labs(title    = "Sézary Cell Pseudotime Bin Distribution per Cell Line",
       subtitle = paste0(
         "Differentiation position — state-anchored boundaries: ",
         "Naive|TCM=", round(naive_tcm_cut, 2),
         " | TCM|TEM=", round(tcm_tem_cut, 2),
         " | TEM|Temra=", round(tem_temra_cut, 2),
         " | Treg=label-based"),
       x = "Cell Line", y = "% Cells")

p_bar_labels / p_bar_bins +
  plot_annotation(
    title = "Cell of Origin (Label Transfer) vs Differentiation Position (Pseudotime Bins)\nSézary Syndrome — CD4+ T Cell Trajectory",
    theme = theme(plot.title = element_text(hjust = 0.5, size = 13,
                                            face = "bold"))
  )
```

## Heatmaps — State and Bin Proportions

```{r heatmap-states, fig.width=8, fig.height=6}
# Heatmap 1: Azimuth l2 label proportions per cell line
heatmap_df <- state_per_line %>%
  select(cell_line, state_azimuth_l2, pct) %>%
  pivot_wider(names_from  = state_azimuth_l2,
              values_from = pct, values_fill = 0)
heatmap_mat           <- as.matrix(heatmap_df[, -1])
rownames(heatmap_mat) <- heatmap_df$cell_line

pheatmap(
  heatmap_mat,
  color           = colorRampPalette(c("white", "#FEE090", "#D73027"))(50),
  display_numbers = TRUE, number_format = "%.1f%%",
  fontsize_number = 8, fontsize_row = 10, fontsize_col = 9,
  angle_col       = 45, cluster_rows = TRUE, cluster_cols = TRUE,
  main            = "% Sézary Cells per Azimuth l2 State\n(Label transfer — cell of origin)",
  border_color    = "white"
)

# Heatmap 2: pseudotime bin proportions — biological order preserved
bin_heatmap_df <- pt_bin_per_line %>%
  select(cell_line, pseudotime_bin, pct) %>%
  pivot_wider(names_from  = pseudotime_bin,
              values_from = pct, values_fill = 0)

# FIX: use any_of() to safely select only columns that exist
# Original used bare select() which errors if a bin column is missing
all_bin_levels <- c("Naive-like", "TCM-like", "Treg-like",
                    "TEM-like", "Temra-like")
for (b in all_bin_levels) {
  if (!b %in% colnames(bin_heatmap_df)) bin_heatmap_df[[b]] <- 0
}
bin_heatmap_df <- bin_heatmap_df %>%
  select(cell_line, any_of(all_bin_levels))

bin_mat           <- as.matrix(bin_heatmap_df[, -1])
rownames(bin_mat) <- bin_heatmap_df$cell_line

pheatmap(
  bin_mat,
  color           = colorRampPalette(c("white", "#74ADD1", "#D73027"))(50),
  display_numbers = TRUE, number_format = "%.1f%%",
  fontsize_number = 8, fontsize_row = 10, fontsize_col = 9,
  angle_col       = 45,
  cluster_rows    = TRUE,
  cluster_cols    = FALSE,  # preserve biological order of bins
  main            = "% Sézary Cells per Pseudotime Bin\n(State-anchored boundaries — differentiation position)",
  border_color    = "white"
)
```


# Pseudotime Analysis

## Pseudotime Distribution: Reference vs Malignant

```{r pseudotime-distribution, fig.width=14, fig.height=6}
ref_pt <- data.frame(
  pseudotime = reference_integrated$monocle3_pseudotime[
    is.finite(reference_integrated$monocle3_pseudotime)],
  source = "Healthy reference"
)
mal_pt <- data.frame(
  pseudotime = mapped_MalignantCD4T$pseudotime_value[
    is.finite(mapped_MalignantCD4T$pseudotime_value)],
  source = "Sézary cells"
)
pt_combined <- bind_rows(ref_pt, mal_pt)

# Reference state median lines — visual anchors showing where each
# healthy state sits on the pseudotime axis
state_vlines <- data.frame(
  state = c("Naive",    "TCM",    "Treg",    "TEM",    "Temra"),
  med   = c(naive_med,  tcm_med,  treg_med,  tem_med,  temra_med),
  col   = c("#2166AC",  "#74ADD1","#762A83",  "#FEE090","#D73027")
)

p_density <- ggplot(pt_combined, aes(x = pseudotime, fill = source)) +
  geom_density(alpha = 0.65, adjust = 1.2) +
  geom_vline(data  = state_vlines,
             aes(xintercept = med, color = state),
             linetype = "dashed", linewidth = 0.8) +
  scale_color_manual(
    values = setNames(state_vlines$col, state_vlines$state),
    name   = "Reference state\nmedian") +
  scale_fill_manual(
    values = c("Healthy reference" = "#4575B4",
               "Sézary cells"      = malignant_color),
    name = "") +
  theme_classic(base_size = 12) +
  theme(legend.position = "right",
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  labs(title    = "Pseudotime Distribution: Healthy Reference vs Sézary Cells",
       subtitle = "Peak shift indicates differentiation arrest point | dashed = reference state medians",
       x = "Monocle3 Pseudotime", y = "Density")

p_ridge <- mapped_MalignantCD4T@meta.data %>%
  filter(is.finite(pseudotime_value)) %>%
  ggplot(aes(x = pseudotime_value, y = cell_line, fill = cell_line)) +
  geom_density_ridges(alpha = 0.80, scale = 1.3, rel_min_height = 0.01,
                      quantile_lines = TRUE, quantiles = 2) +
  geom_vline(xintercept = c(naive_med, tcm_med, treg_med,
                             tem_med,  temra_med),
             linetype = "dashed", color = "grey40", linewidth = 0.5) +
  scale_fill_manual(values = cell_line_palette) +
  theme_classic(base_size = 11) +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  labs(title    = "Pseudotime per Cell Line",
       subtitle = "Vertical line = median | Dashed = reference state medians",
       x = "Pseudotime", y = "")

p_density | p_ridge
```

## Pseudotime Summary Statistics

```{r pseudotime-stats}
# FIX: pct columns computed only for non-Treg cells on the effector axis.
# Original script counted all cells including Treg which inflates pct_tcm
# (Treg cells have TCM-range pseudotime and would be counted as TCM-like).
# Now we separate Treg cells and compute effector-axis stats independently.

pt_stats <- mapped_MalignantCD4T@meta.data %>%
  filter(is.finite(pseudotime_value)) %>%
  group_by(cell_line) %>%
  summarise(
    n_cells      = n(),
    n_treg       = sum(state_azimuth_l2 == "Treg", na.rm = TRUE),
    n_effector   = sum(state_azimuth_l2 != "Treg", na.rm = TRUE),
    mean_pt      = round(mean(pseudotime_value),           3),
    median_pt    = round(median(pseudotime_value),         3),
    sd_pt        = round(sd(pseudotime_value),             3),
    q25_pt       = round(quantile(pseudotime_value, 0.25), 3),
    q75_pt       = round(quantile(pseudotime_value, 0.75), 3),
    # Effector axis pct: computed only on non-Treg cells
    pct_naive    = round(100 * sum(
                     state_azimuth_l2 != "Treg" &
                     pseudotime_value  <  naive_tcm_cut,
                     na.rm = TRUE) / n_effector, 1),
    pct_tcm      = round(100 * sum(
                     state_azimuth_l2 != "Treg" &
                     pseudotime_value >= naive_tcm_cut &
                     pseudotime_value  <  tcm_tem_cut,
                     na.rm = TRUE) / n_effector, 1),
    pct_tem      = round(100 * sum(
                     state_azimuth_l2 != "Treg" &
                     pseudotime_value >= tcm_tem_cut &
                     pseudotime_value  <  tem_temra_cut,
                     na.rm = TRUE) / n_effector, 1),
    pct_temra    = round(100 * sum(
                     state_azimuth_l2 != "Treg" &
                     pseudotime_value >= tem_temra_cut,
                     na.rm = TRUE) / n_effector, 1),
    pct_treg     = round(100 * n_treg / n_cells, 1),
    .groups      = "drop"
  ) %>%
  arrange(median_pt)

cat("=== Pseudotime Statistics per Cell Line ===\n")
cat("(pct_naive/tcm/tem/temra computed on effector-axis cells only,\n")
cat(" pct_treg = Treg-labelled cells as % of all cells)\n\n")
print(pt_stats, n = Inf)

cat("\n=== Reference Pseudotime per Azimuth l2 State ===\n")
ref_stats <- reference_integrated@meta.data %>%
  filter(is.finite(monocle3_pseudotime), !is.na(predicted.celltype.l2)) %>%
  group_by(predicted.celltype.l2) %>%
  summarise(
    n_cells   = n(),
    mean_pt   = round(mean(monocle3_pseudotime),   3),
    median_pt = round(median(monocle3_pseudotime), 3),
    .groups   = "drop"
  ) %>%
  arrange(median_pt)
print(ref_stats)
```


# Integrated Summary

## Four-Panel Summary Figure

```{r summary-figure, fig.width=12, fig.height=10}
p_ref <- DimPlot(
  reference_integrated, group.by = "predicted.celltype.l2",
  reduction = "umap", label = TRUE, repel = TRUE,
  label.size = 3, pt.size = 0.4
) +
  scale_color_manual(values = azimuth_l2_colors, na.value = "grey40") +
  ggtitle("A. Healthy Reference CD4+ T Cells\n(Azimuth l2)") +
  theme(plot.title = element_text(hjust = 0.5, size = 11, face = "bold")) +
  NoLegend()

p_pt_ref <- FeaturePlot(
  reference_integrated, features = "monocle3_pseudotime",
  reduction = "umap", cols = c("lightblue", "yellow", "red"),
  pt.size = 0.4
) +
  ggtitle("B. Reference Pseudotime\n(Naive → Temra + Treg branch)") +
  theme(plot.title = element_text(hjust = 0.5, size = 11, face = "bold"))

p_proj <- ggplot() +
  geom_point(data = ref_umap,
             aes(x = UMAP_1, y = UMAP_2),
             color = "grey88", size = 0.3, alpha = 0.6) +
  geom_point(data = query_umap %>% filter(is.finite(pseudotime)),
             aes(x = UMAP_1, y = UMAP_2, color = pseudotime),
             size = 1.0, alpha = 0.85) +
  scale_color_viridis_c(option = "plasma", name = "Pseudotime") +
  theme_classic(base_size = 10) +
  theme(plot.title = element_text(hjust = 0.5, size = 11, face = "bold")) +
  ggtitle("C. Sézary Cells Projected\n(Transferred pseudotime)")

top_states    <- state_summary %>% head(8) %>% pull(state_azimuth_l2)
state_plot_df <- state_per_line %>% filter(state_azimuth_l2 %in% top_states)

p_quant <- ggplot(state_plot_df,
                   aes(x = reorder(cell_line, -pct), y = pct,
                       fill = state_azimuth_l2)) +
  geom_col(position = "stack", color = "white", linewidth = 0.3) +
  scale_fill_manual(values = azimuth_l2_colors, na.value = "grey40",
                    name = "Azimuth l2") +
  theme_classic(base_size = 10) +
  theme(axis.text.x = element_text(angle = 40, hjust = 1, size = 9),
        plot.title  = element_text(hjust = 0.5, size = 11, face = "bold"),
        legend.key.size = unit(0.4, "cm"),
        legend.text = element_text(size = 8)) +
  labs(title = "D. State Proportions per Cell Line\n(Azimuth l2 label transfer)",
       x = "", y = "% Cells")

(p_ref | p_pt_ref) / (p_proj | p_quant) +
  plot_annotation(
    title = "Sézary Cell Differentiation State Mapping onto Healthy CD4+ T Cell Trajectory",
    theme = theme(plot.title = element_text(hjust = 0.5, size = 13,
                                            face = "bold"))
  )
```

## Pseudotime Violin by Transferred State

```{r pseudotime-violin, fig.width=7, fig.height=5}
mal_state_pt <- mapped_MalignantCD4T@meta.data %>%
  filter(is.finite(pseudotime_value), !is.na(state_azimuth_l2)) %>%
  mutate(state = state_azimuth_l2)

state_order_mal <- mal_state_pt %>%
  group_by(state) %>%
  summarise(med = median(pseudotime_value), .groups = "drop") %>%
  arrange(med) %>%
  pull(state)

mal_state_pt$state <- factor(mal_state_pt$state, levels = state_order_mal)

ggplot(mal_state_pt, aes(x = state, y = pseudotime_value, fill = state)) +
  geom_violin(scale = "width", alpha = 0.8, trim = TRUE) +
  geom_boxplot(width = 0.1, fill = "white", outlier.size = 0.3) +
  geom_hline(yintercept = c(naive_med, tcm_med, treg_med,
                             tem_med,  temra_med),
             linetype = "dashed", color = "grey50", linewidth = 0.5) +
  scale_fill_manual(values = azimuth_l2_colors, na.value = "grey70") +
  theme_classic(base_size = 11) +
  theme(axis.text.x   = element_text(angle = 40, hjust = 1, size = 9),
        legend.position = "none",
        plot.title    = element_text(hjust = 0.5, face = "bold")) +
  labs(title    = "Transferred Pseudotime per Azimuth l2 State (Sézary Cells)",
       subtitle = "Dashed lines = healthy reference state medians",
       x = "Transferred State (cell of origin)",
       y = "Transferred Pseudotime (differentiation position)")
```

## Key Comparison: Label vs Pseudotime Bin

```{r label-vs-bin, fig.width=11, fig.height=8}
# This is the core result plot.
# For each transferred Azimuth l2 label, shows what fraction of cells
# fall into each pseudotime bin.
# Key message: TCM-labelled cells span TCM, TEM, and Temra bins
#   → identity = TCM (cell of origin)
#   → position = further along effector axis (partial skewing)

# FIX: build cross_df with explicit column selection from table output
# rather than rename() which can fail if column names differ
cross_df <- as.data.frame(cross_tab)
# table() always returns: Var1 → Label, Var2 → PT_Bin (from our named dims)
# but if table was constructed with named dims "Label" and "PT_Bin"
# then colnames are already Label, PT_Bin, Freq
colnames(cross_df) <- c("Label", "PT_Bin", "n")

cross_df <- cross_df %>%
  group_by(Label) %>%
  mutate(pct = round(100 * n / sum(n), 1)) %>%
  ungroup() %>%
  filter(n > 0) %>%
  mutate(PT_Bin = factor(PT_Bin,
                         levels = c("Naive-like", "TCM-like", "Treg-like",
                                    "TEM-like", "Temra-like")))

ggplot(cross_df, aes(x = PT_Bin, y = pct, fill = PT_Bin)) +
  geom_col(color = "white", linewidth = 0.3) +
  geom_text(aes(label = ifelse(pct >= 3, paste0(pct, "%"), "")),
            vjust = -0.3, size = 3, fontface = "bold") +
  facet_wrap(~ Label, scales = "free_y", ncol = 3) +
  scale_fill_manual(values = bin_colors, drop = FALSE,
                    name = "Pseudotime Bin") +
  theme_classic(base_size = 10) +
  theme(axis.text.x     = element_text(angle = 40, hjust = 1, size = 8),
        strip.text       = element_text(face = "bold", size = 9),
        strip.background = element_rect(fill = "#E8F4FD"),
        legend.position  = "none",
        plot.title       = element_text(hjust = 0.5, face = "bold")) +
  labs(
    title    = "Pseudotime Bin Distribution within Each Transferred Azimuth l2 Label",
    subtitle = paste0(
      "Core finding: TCM-labelled Sézary cells span multiple pseudotime bins\n",
      "→ TCM identity (label) but partial effector skewing (pseudotime position)\n",
      "Note: Treg panel shows 100% Treg-like by design (label-based bin assignment)"),
    x = "Pseudotime Bin", y = "% Cells within label"
  )
```


# Save Outputs

```{r save-outputs}
Sys.setlocale("LC_COLLATE", "C")
options(expressions = 10000)

out_dir <- "results/MalignantCD4T_Monocle3_projection"
if (!dir.exists(out_dir)) dir.create(out_dir, recursive = TRUE)

library(SeuratObject)

SaveSeuratRds(mapped_MalignantCD4T,
              file = file.path(out_dir, 
                              "MalignantCD4T_mapped_monocle3_pseudotime_noProlif_ref.Rds"))



# Summary tables
write.csv(state_summary,
          file.path(out_dir, "state_distribution_overall.csv"),
          row.names = FALSE)
write.csv(state_per_line,
          file.path(out_dir, "state_distribution_per_cellline.csv"),
          row.names = FALSE)
write.csv(pt_bin_per_line,
          file.path(out_dir, "pseudotime_bins_per_cellline.csv"),
          row.names = FALSE)
write.csv(pt_stats,
          file.path(out_dir, "pseudotime_stats_per_cellline.csv"),
          row.names = FALSE)
write.csv(as.data.frame(cross_tab),
          file.path(out_dir, "label_vs_pseudotime_bin_crosstable.csv"),
          row.names = FALSE)
write.csv(mapped_MalignantCD4T@meta.data,
          file.path(out_dir, "MalignantCD4T_full_metadata_with_projection.csv"),
          row.names = TRUE)

# Bin boundaries — saved for methods section and reproducibility
bin_boundaries <- data.frame(
  boundary       = c("Naive_TCM", "TCM_TEM", "TEM_Temra", "TCM_Treg_branch"),
  pseudotime_cut = c(naive_tcm_cut, tcm_tem_cut, tem_temra_cut, tcm_treg_cut),
  naive_med      = naive_med,
  tcm_med        = tcm_med,
  treg_med       = treg_med,
  tem_med        = tem_med,
  temra_med      = temra_med
)
write.csv(bin_boundaries,
          file.path(out_dir, "pseudotime_bin_boundaries.csv"),
          row.names = FALSE)

cat("✅ All outputs saved to:", out_dir, "\n\n")
cat("Files:\n")
print(list.files(out_dir))
```


# Session Info

```{r session-info}
sessionInfo()
```


