8b: B-ALL CellChat — CNS Immunosuppressive Microenvironment

Overview

Part 2 of 2 — CNS-focused immunosuppressive deep dive.

Building on the global CellChat landscape in 12a, this script focuses on the immunosuppressive communication networks that define the CNS leukaemia niche:

1. Galectin-9 / TIM-3 axis — B-ALL and Vγ6Vδ4 as dual immunosuppressive senders
2. TREM2 / APP myeloid programming — Stroma → microglia/macrophage tolerogenic signals
3. Niche retention circuits — CXCL12/CXCR4 (B-ALL), CXCL16/CXCR6 (Vγ6Vδ4)
4. TGFβ network — immunosuppressive signalling from multiple sources
5. Cell-type focused analysis — Vγ6Vδ4, B-ALL, stroma, microglia-like communication
6. Pathway summary tables — CNS-enriched and CNS-only pathways
7. Communication patterns — coordinated signalling programmes

Setup — Load Cached Objects

library(CellChat)
library(Seurat)
library(qs2)
library(dplyr)
library(tidyr)
library(patchwork)
library(ggplot2)
library(ComplexHeatmap)
library(knitr)
library(NMF)

if (!requireNamespace("ggalluvial", quietly = TRUE)) {
  cat("NOTE: ggalluvial not installed — river plots will be skipped\n")
}

base_path <- "/exports/eddie/scratch/aduguid3"
cache_dir <- file.path(base_path, "harmony_clustering")

combined       <- qs_read(file.path(cache_dir, "12_combined_seurat.qs"))
cellchat_bm    <- qs_read(file.path(cache_dir, "12_cellchat_bm_v1.qs"))
cellchat_cns   <- qs_read(file.path(cache_dir, "12_cellchat_cns_v1.qs"))
cellchat_merged <- qs_read(file.path(cache_dir, "12_cellchat_merged_v1.qs"))

cellchat_cns <- netAnalysis_computeCentrality(cellchat_cns)
cellchat_bm  <- netAnalysis_computeCentrality(cellchat_bm)

cns_net <- subsetCommunication(cellchat_cns)
bm_net  <- subsetCommunication(cellchat_bm)

cat("CNS interactions:", nrow(cns_net), "\n")

## CNS interactions: 6600

cat("BM interactions: ", nrow(bm_net), "\n")

## BM interactions:  6162

Step 1 — Galectin-9 / TIM-3 Immunosuppressive Axis

Galectin-9 (LGALS9) emerged as the dominant outgoing signal from B-ALL and is also produced by Vγ6Vδ4 cells. Galectin-9 engages TIM-3 (HAVCR2) on T cells to induce exhaustion/apoptosis, and signals through CD45 and CD44 to broadly suppress immune activation.

Step 1a — All Galectin Interactions in CNS

gal_cns <- cns_net %>%
  filter(pathway_name == "GALECTIN") %>%
  arrange(desc(prob))

cat("Total GALECTIN interactions in CNS:", nrow(gal_cns), "\n\n")

## Total GALECTIN interactions in CNS: 389

# Summarise by sender
gal_cns %>%
  group_by(source) %>%
  summarise(
    n_targets = n_distinct(target),
    n_LR = n(),
    total_prob = round(sum(prob), 3),
    .groups = "drop"
  ) %>%
  arrange(desc(total_prob)) %>%
  kable(caption = "GALECTIN senders in CNS — ranked by total probability")

GALECTIN senders in CNS — ranked by total probability

source	n_targets	n_LR	total_prob
B-ALL	19	47	3.089
Microglia-like	18	34	1.524
Basophils	18	28	0.884
Macrophages	18	26	0.690
CD8_Teff	17	25	0.630
CD8_Tpex	17	25	0.594
Other_gd	17	25	0.572
Neutrophils	16	23	0.487
Stroma	14	19	0.267
DCs	14	19	0.261
CD4_T	12	15	0.145
CD8_Tex	12	15	0.135
NKT-like	12	15	0.132
Pre-B	12	15	0.130
Vg4	12	15	0.121
CD8_Naive	12	15	0.120
Other	12	14	0.103
Vg6Vd4	12	14	0.089

Step 1b — Galectin-9 Specific L-R Pairs

gal_lr <- cns_net %>%
  filter(pathway_name == "GALECTIN") %>%
  group_by(interaction_name) %>%
  summarise(
    n_pairs = n(),
    total_prob = round(sum(prob), 3),
    top_sender = names(sort(table(source), decreasing = TRUE))[1],
    .groups = "drop"
  ) %>%
  arrange(desc(total_prob))

gal_lr %>% kable(caption = "GALECTIN L-R pairs ranked by total probability (CNS)")

GALECTIN L-R pairs ranked by total probability (CNS)

interaction_name	n_pairs	total_prob	top_sender
LGALS9_CD44	125	3.139	B-ALL
LGALS9_IGHM	144	2.696	B-ALL
LGALS9_CD45	22	2.460	B-ALL
LGALS9_HAVCR2	90	1.185	B-ALL
LGALS9_P4HB	8	0.495	B-ALL

Step 1c — LGALS9 → HAVCR2 (TIM-3) Interactions

The key immunosuppressive interaction — Galectin-9 engaging TIM-3 on T cells.

tim3_interactions <- cns_net %>%
  filter(grepl("LGALS9.*HAVCR2|HAVCR2.*LGALS9", interaction_name)) %>%
  arrange(desc(prob))

if (nrow(tim3_interactions) > 0) {
  cat("LGALS9 → HAVCR2 (TIM-3) interactions in CNS:", nrow(tim3_interactions), "\n\n")
  tim3_interactions %>%
    select(source, target, interaction_name, prob, pval) %>%
    kable(caption = "Galectin-9 → TIM-3 interactions (CNS)")
} else {
  cat("No direct LGALS9-HAVCR2 interactions — checking all HAVCR2 targets:\n")
  cns_net %>%
    filter(grepl("HAVCR2", interaction_name)) %>%
    select(source, target, interaction_name, pathway_name, prob) %>%
    arrange(desc(prob)) %>%
    kable()
}

## LGALS9 → HAVCR2 (TIM-3) interactions in CNS: 90

Galectin-9 → TIM-3 interactions (CNS)

source	target	interaction_name	prob	pval
B-ALL	Microglia-like	LGALS9_HAVCR2	0.0536680	0
B-ALL	Monocytes	LGALS9_HAVCR2	0.0414412	0
B-ALL	CD8_Tpex	LGALS9_HAVCR2	0.0389816	0
Microglia-like	Microglia-like	LGALS9_HAVCR2	0.0387106	0
Basophils	Microglia-like	LGALS9_HAVCR2	0.0364071	0
Macrophages	Microglia-like	LGALS9_HAVCR2	0.0325265	0
CD8_Teff	Microglia-like	LGALS9_HAVCR2	0.0320830	0
CD8_Tpex	Microglia-like	LGALS9_HAVCR2	0.0302065	0
Microglia-like	Monocytes	LGALS9_HAVCR2	0.0297842	0
Other_gd	Microglia-like	LGALS9_HAVCR2	0.0290987	0
Basophils	Monocytes	LGALS9_HAVCR2	0.0279964	0
Microglia-like	CD8_Tpex	LGALS9_HAVCR2	0.0279962	0
Basophils	CD8_Tpex	LGALS9_HAVCR2	0.0263128	0
Neutrophils	Microglia-like	LGALS9_HAVCR2	0.0262552	0
Macrophages	Monocytes	LGALS9_HAVCR2	0.0249890	0
CD8_Teff	Monocytes	LGALS9_HAVCR2	0.0246456	0
Macrophages	CD8_Tpex	LGALS9_HAVCR2	0.0234820	0
CD8_Tpex	Monocytes	LGALS9_HAVCR2	0.0231937	0
CD8_Teff	CD8_Tpex	LGALS9_HAVCR2	0.0231588	0
Other_gd	Monocytes	LGALS9_HAVCR2	0.0223372	0
CD8_Tpex	CD8_Tpex	LGALS9_HAVCR2	0.0217925	0
Other_gd	CD8_Tpex	LGALS9_HAVCR2	0.0209867	0
Neutrophils	Monocytes	LGALS9_HAVCR2	0.0201407	0
Neutrophils	CD8_Tpex	LGALS9_HAVCR2	0.0189204	0
B-ALL	CD8_Tex	LGALS9_HAVCR2	0.0186428	0
Stroma	Microglia-like	LGALS9_HAVCR2	0.0169414	0
DCs	Microglia-like	LGALS9_HAVCR2	0.0165711	0
Microglia-like	CD8_Tex	LGALS9_HAVCR2	0.0133097	0
Stroma	Monocytes	LGALS9_HAVCR2	0.0129671	0
DCs	Monocytes	LGALS9_HAVCR2	0.0126826	0
Basophils	CD8_Tex	LGALS9_HAVCR2	0.0124981	0
Stroma	CD8_Tpex	LGALS9_HAVCR2	0.0121760	0
DCs	CD8_Tpex	LGALS9_HAVCR2	0.0119086	0
CD4_T	Microglia-like	LGALS9_HAVCR2	0.0117292	0
Macrophages	CD8_Tex	LGALS9_HAVCR2	0.0111365	0
CD8_Teff	CD8_Tex	LGALS9_HAVCR2	0.0109813	0
CD8_Tex	Microglia-like	LGALS9_HAVCR2	0.0109398	0
NKT-like	Microglia-like	LGALS9_HAVCR2	0.0106975	0
Pre-B	Microglia-like	LGALS9_HAVCR2	0.0104786	0
B-ALL	CD8_Teff	LGALS9_HAVCR2	0.0103425	0
CD8_Tpex	CD8_Tex	LGALS9_HAVCR2	0.0103258	0
Other_gd	CD8_Tex	LGALS9_HAVCR2	0.0099397	0
Vg4	Microglia-like	LGALS9_HAVCR2	0.0097979	0
CD8_Naive	Microglia-like	LGALS9_HAVCR2	0.0096620	0
CD4_T	Monocytes	LGALS9_HAVCR2	0.0089665	0
Neutrophils	CD8_Tex	LGALS9_HAVCR2	0.0089511	0
Other	Microglia-like	LGALS9_HAVCR2	0.0088531	0
CD4_T	CD8_Tpex	LGALS9_HAVCR2	0.0084174	0
CD8_Tex	Monocytes	LGALS9_HAVCR2	0.0083614	0
NKT-like	Monocytes	LGALS9_HAVCR2	0.0081758	0
Pre-B	Monocytes	LGALS9_HAVCR2	0.0080080	0
CD8_Tex	CD8_Tpex	LGALS9_HAVCR2	0.0078491	0
Vg6Vd4	Microglia-like	LGALS9_HAVCR2	0.0076801	0
NKT-like	CD8_Tpex	LGALS9_HAVCR2	0.0076747	0
Pre-B	CD8_Tpex	LGALS9_HAVCR2	0.0075172	0
Vg4	Monocytes	LGALS9_HAVCR2	0.0074866	0
CD8_Naive	Monocytes	LGALS9_HAVCR2	0.0073825	0
Microglia-like	CD8_Teff	LGALS9_HAVCR2	0.0073660	0
Vg4	CD8_Tpex	LGALS9_HAVCR2	0.0070275	0
CD8_Naive	CD8_Tpex	LGALS9_HAVCR2	0.0069298	0
Basophils	CD8_Teff	LGALS9_HAVCR2	0.0069143	0
Other	Monocytes	LGALS9_HAVCR2	0.0067632	0
Other	CD8_Tpex	LGALS9_HAVCR2	0.0063481	0
Macrophages	CD8_Teff	LGALS9_HAVCR2	0.0061572	0
CD8_Teff	CD8_Teff	LGALS9_HAVCR2	0.0060710	0
Vg6Vd4	Monocytes	LGALS9_HAVCR2	0.0058655	0
Stroma	CD8_Tex	LGALS9_HAVCR2	0.0057396	0
CD8_Tpex	CD8_Teff	LGALS9_HAVCR2	0.0057069	0
DCs	CD8_Tex	LGALS9_HAVCR2	0.0056128	0
Vg6Vd4	CD8_Tpex	LGALS9_HAVCR2	0.0055052	0
Other_gd	CD8_Teff	LGALS9_HAVCR2	0.0054926	0
Neutrophils	CD8_Teff	LGALS9_HAVCR2	0.0049441	0
CD4_T	CD8_Tex	LGALS9_HAVCR2	0.0039599	0
CD8_Tex	CD8_Tex	LGALS9_HAVCR2	0.0036914	0
NKT-like	CD8_Tex	LGALS9_HAVCR2	0.0036091	0
Pre-B	CD8_Tex	LGALS9_HAVCR2	0.0035347	0
Vg4	CD8_Tex	LGALS9_HAVCR2	0.0033036	0
CD8_Naive	CD8_Tex	LGALS9_HAVCR2	0.0032575	0
Stroma	CD8_Teff	LGALS9_HAVCR2	0.0031657	0
DCs	CD8_Teff	LGALS9_HAVCR2	0.0030955	0
Other	CD8_Tex	LGALS9_HAVCR2	0.0029831	0
Vg6Vd4	CD8_Tex	LGALS9_HAVCR2	0.0025859	0
CD4_T	CD8_Teff	LGALS9_HAVCR2	0.0021823	0
CD8_Tex	CD8_Teff	LGALS9_HAVCR2	0.0020341	0
NKT-like	CD8_Teff	LGALS9_HAVCR2	0.0019886	0
Pre-B	CD8_Teff	LGALS9_HAVCR2	0.0019476	0
Vg4	CD8_Teff	LGALS9_HAVCR2	0.0018201	0
CD8_Naive	CD8_Teff	LGALS9_HAVCR2	0.0017946	0
Other	CD8_Teff	LGALS9_HAVCR2	0.0016433	0
Vg6Vd4	CD8_Teff	LGALS9_HAVCR2	0.0014242	0

# Compare with BM
tim3_bm <- bm_net %>%
  filter(grepl("LGALS9.*HAVCR2|HAVCR2.*LGALS9", interaction_name))
cat("\nSame interactions in BM:", nrow(tim3_bm), "\n")

## 
## Same interactions in BM: 72

if (nrow(tim3_bm) > 0) {
  tim3_bm %>%
    select(source, target, interaction_name, prob) %>%
    arrange(desc(prob)) %>%
    kable(caption = "Galectin-9 → TIM-3 interactions (BM)")
}

Galectin-9 → TIM-3 interactions (BM)

source	target	interaction_name	prob
B-ALL	CD8_Tex	LGALS9_HAVCR2	0.0629273
Basophils	CD8_Tex	LGALS9_HAVCR2	0.0573296
Neutrophils	CD8_Tex	LGALS9_HAVCR2	0.0398187
B-ALL	Monocytes	LGALS9_HAVCR2	0.0387143
Stroma	CD8_Tex	LGALS9_HAVCR2	0.0386844
Progenitors	CD8_Tex	LGALS9_HAVCR2	0.0361347
Basophils	Monocytes	LGALS9_HAVCR2	0.0351896
Other_gd	CD8_Tex	LGALS9_HAVCR2	0.0316515
CD8_Tpex	CD8_Tex	LGALS9_HAVCR2	0.0266629
Neutrophils	Monocytes	LGALS9_HAVCR2	0.0242671
Stroma	Monocytes	LGALS9_HAVCR2	0.0235649
Progenitors	Monocytes	LGALS9_HAVCR2	0.0219890
Other_gd	Monocytes	LGALS9_HAVCR2	0.0192258
DCs	CD8_Tex	LGALS9_HAVCR2	0.0187730
Microglia-like	CD8_Tex	LGALS9_HAVCR2	0.0181322
CD8_Tpex	Monocytes	LGALS9_HAVCR2	0.0161630
B-ALL	CD8_Teff	LGALS9_HAVCR2	0.0157444
Macrophages	CD8_Tex	LGALS9_HAVCR2	0.0157131
CD8_Teff	CD8_Tex	LGALS9_HAVCR2	0.0146490
Basophils	CD8_Teff	LGALS9_HAVCR2	0.0142799
CD4_T	CD8_Tex	LGALS9_HAVCR2	0.0142431
B-ALL	CD8_Tpex	LGALS9_HAVCR2	0.0141021
Pre-B	CD8_Tex	LGALS9_HAVCR2	0.0140108
CD8_Naive	CD8_Tex	LGALS9_HAVCR2	0.0136071
Other	CD8_Tex	LGALS9_HAVCR2	0.0134957
NKT-like	CD8_Tex	LGALS9_HAVCR2	0.0132489
Basophils	CD8_Tpex	LGALS9_HAVCR2	0.0127884
CD8_Tex	CD8_Tex	LGALS9_HAVCR2	0.0114925
DCs	Monocytes	LGALS9_HAVCR2	0.0113439
Vg4	CD8_Tex	LGALS9_HAVCR2	0.0111247
Microglia-like	Monocytes	LGALS9_HAVCR2	0.0109539
Neutrophils	CD8_Teff	LGALS9_HAVCR2	0.0097818
Stroma	CD8_Teff	LGALS9_HAVCR2	0.0094947
Macrophages	Monocytes	LGALS9_HAVCR2	0.0094832
Progenitors	CD8_Teff	LGALS9_HAVCR2	0.0088512
CD8_Teff	Monocytes	LGALS9_HAVCR2	0.0088372
Neutrophils	CD8_Tpex	LGALS9_HAVCR2	0.0087559
CD4_T	Monocytes	LGALS9_HAVCR2	0.0085909
Stroma	CD8_Tpex	LGALS9_HAVCR2	0.0084987
Pre-B	Monocytes	LGALS9_HAVCR2	0.0084501
CD8_Naive	Monocytes	LGALS9_HAVCR2	0.0082052
Other	Monocytes	LGALS9_HAVCR2	0.0081377
NKT-like	Monocytes	LGALS9_HAVCR2	0.0079881
Progenitors	CD8_Tpex	LGALS9_HAVCR2	0.0079221
Other_gd	CD8_Teff	LGALS9_HAVCR2	0.0077259
CD8_Tex	Monocytes	LGALS9_HAVCR2	0.0069242
Other_gd	CD8_Tpex	LGALS9_HAVCR2	0.0069141
Vg4	Monocytes	LGALS9_HAVCR2	0.0067016
CD8_Tpex	CD8_Teff	LGALS9_HAVCR2	0.0064830
CD8_Tpex	CD8_Tpex	LGALS9_HAVCR2	0.0058010
DCs	CD8_Teff	LGALS9_HAVCR2	0.0045367
Microglia-like	CD8_Teff	LGALS9_HAVCR2	0.0043797
DCs	CD8_Tpex	LGALS9_HAVCR2	0.0040587
Microglia-like	CD8_Tpex	LGALS9_HAVCR2	0.0039181
Macrophages	CD8_Teff	LGALS9_HAVCR2	0.0037883
CD8_Teff	CD8_Teff	LGALS9_HAVCR2	0.0035289
CD4_T	CD8_Teff	LGALS9_HAVCR2	0.0034300
Macrophages	CD8_Tpex	LGALS9_HAVCR2	0.0033889
Pre-B	CD8_Teff	LGALS9_HAVCR2	0.0033735
CD8_Naive	CD8_Teff	LGALS9_HAVCR2	0.0032752
Other	CD8_Teff	LGALS9_HAVCR2	0.0032482
NKT-like	CD8_Teff	LGALS9_HAVCR2	0.0031881
CD8_Teff	CD8_Tpex	LGALS9_HAVCR2	0.0031567
CD4_T	CD8_Tpex	LGALS9_HAVCR2	0.0030682
Pre-B	CD8_Tpex	LGALS9_HAVCR2	0.0030176
CD8_Naive	CD8_Tpex	LGALS9_HAVCR2	0.0029297
Other	CD8_Tpex	LGALS9_HAVCR2	0.0029055
NKT-like	CD8_Tpex	LGALS9_HAVCR2	0.0028518
CD8_Tex	CD8_Teff	LGALS9_HAVCR2	0.0027618
Vg4	CD8_Teff	LGALS9_HAVCR2	0.0026726
CD8_Tex	CD8_Tpex	LGALS9_HAVCR2	0.0024703
Vg4	CD8_Tpex	LGALS9_HAVCR2	0.0023905

Step 1d — B-ALL Galectin Output

ball_gal <- cns_net %>%
  filter(source == "B-ALL", pathway_name == "GALECTIN") %>%
  arrange(desc(prob))

cat("B-ALL GALECTIN outgoing in CNS:", nrow(ball_gal), "\n\n")

## B-ALL GALECTIN outgoing in CNS: 47

ball_gal %>%
  select(source, target, interaction_name, prob, pval) %>%
  head(25) %>%
  kable(caption = "Top 25 B-ALL → GALECTIN interactions (CNS)")

Top 25 B-ALL → GALECTIN interactions (CNS)

source	target	interaction_name	prob	pval
B-ALL	Monocytes	LGALS9_CD45	0.1424436	0
B-ALL	CD8_Tpex	LGALS9_CD45	0.1370330	0
B-ALL	CD8_Teff	LGALS9_CD45	0.1357315	0
B-ALL	CD8_Tex	LGALS9_CD45	0.1353338	0
B-ALL	CD4_T	LGALS9_CD45	0.1281408	0
B-ALL	Other	LGALS9_CD45	0.1278967	0
B-ALL	CD8_Naive	LGALS9_CD45	0.1278645	0
B-ALL	Pre-B	LGALS9_IGHM	0.1256823	0
B-ALL	NKT-like	LGALS9_CD45	0.1192309	0
B-ALL	DCs	LGALS9_CD45	0.1158341	0
B-ALL	Vg4	LGALS9_CD45	0.1048894	0
B-ALL	Other_gd	LGALS9_CD45	0.1021184	0
B-ALL	Pre-B	LGALS9_CD45	0.1016737	0
B-ALL	Vg6Vd4	LGALS9_CD45	0.1004244	0
B-ALL	B-ALL	LGALS9_CD45	0.0917183	0
B-ALL	Monocytes	LGALS9_CD44	0.0871337	0
B-ALL	Stroma	LGALS9_P4HB	0.0835998	0
B-ALL	Basophils	LGALS9_P4HB	0.0748385	0
B-ALL	Neutrophils	LGALS9_P4HB	0.0683865	0
B-ALL	Other	LGALS9_IGHM	0.0597214	0
B-ALL	DCs	LGALS9_IGHM	0.0565070	0
B-ALL	Microglia-like	LGALS9_HAVCR2	0.0536680	0
B-ALL	CD8_Naive	LGALS9_IGHM	0.0536433	0
B-ALL	Neutrophils	LGALS9_CD44	0.0521016	0
B-ALL	Other	LGALS9_CD44	0.0518921	0

Step 1e — Vg6Vd4 Galectin Output

vg6_gal <- cns_net %>%
  filter(source == "Vg6Vd4", pathway_name == "GALECTIN") %>%
  arrange(desc(prob))

if (nrow(vg6_gal) > 0) {
  cat("Vg6Vd4 GALECTIN outgoing in CNS:", nrow(vg6_gal), "\n\n")
  vg6_gal %>%
    select(source, target, interaction_name, prob, pval) %>%
    kable(caption = "Vg6Vd4 → GALECTIN interactions (CNS)")
} else {
  cat("No GALECTIN interactions from Vg6Vd4\n")
}

## Vg6Vd4 GALECTIN outgoing in CNS: 14

Vg6Vd4 → GALECTIN interactions (CNS)

source	target	interaction_name	prob	pval
Vg6Vd4	Pre-B	LGALS9_IGHM	0.0192403	0.00
Vg6Vd4	Monocytes	LGALS9_CD44	0.0128589	0.02
Vg6Vd4	Other	LGALS9_IGHM	0.0085935	0.00
Vg6Vd4	DCs	LGALS9_IGHM	0.0081073	0.00
Vg6Vd4	Microglia-like	LGALS9_HAVCR2	0.0076801	0.00
Vg6Vd4	CD8_Naive	LGALS9_IGHM	0.0076764	0.00
Vg6Vd4	Monocytes	LGALS9_HAVCR2	0.0058655	0.00
Vg6Vd4	CD8_Tpex	LGALS9_HAVCR2	0.0055052	0.00
Vg6Vd4	Neutrophils	LGALS9_IGHM	0.0036530	0.00
Vg6Vd4	CD8_Tex	LGALS9_HAVCR2	0.0025859	0.00
Vg6Vd4	Microglia-like	LGALS9_IGHM	0.0023398	0.00
Vg6Vd4	CD4_T	LGALS9_IGHM	0.0018786	0.00
Vg6Vd4	NKT-like	LGALS9_IGHM	0.0017911	0.00
Vg6Vd4	CD8_Teff	LGALS9_HAVCR2	0.0014242	0.00

Step 1f — Galectin Network Visualisation

tryCatch({
  netVisual_aggregate(cellchat_cns, signaling = "GALECTIN", layout = "circle")
}, error = function(e) cat("GALECTIN circle plot failed:", e$message, "\n"))

tryCatch({
  netVisual_aggregate(cellchat_bm, signaling = "GALECTIN", layout = "circle")
}, error = function(e) cat("GALECTIN circle plot (BM) failed:", e$message, "\n"))

tryCatch({
  ht <- netVisual_heatmap(cellchat_cns, signaling = "GALECTIN",
                          color.heatmap = "Reds")
  if (inherits(ht, c("HeatmapList", "Heatmap"))) ComplexHeatmap::draw(ht)
}, error = function(e) cat("GALECTIN heatmap failed:", e$message, "\n"))

tryCatch({
  print(netAnalysis_contribution(cellchat_cns, signaling = "GALECTIN") +
          ggtitle("GALECTIN L-R contribution — CNS"))
}, error = function(e) cat("Contribution plot failed:", e$message, "\n"))

Step 2 — TREM2 / APP Myeloid Programming

Stroma sends APP → TREM2/TYROBP and ApoE → TREM2/TYROBP to microglia-like cells and macrophages. TREM2 signalling promotes a homeostatic/tolerogenic macrophage state — potentially shielding the leukaemia from immune attack.

Step 2a — APP Pathway

app_in_cns <- "APP" %in% cellchat_cns@netP$pathways
app_in_bm  <- "APP" %in% cellchat_bm@netP$pathways

cat("APP pathway — CNS:", app_in_cns, "| BM:", app_in_bm, "\n\n")

## APP pathway — CNS: TRUE | BM: TRUE

if (app_in_cns) {
  netVisual_aggregate(cellchat_cns, signaling = "APP", layout = "circle")
}

if (app_in_cns) {
  print(netAnalysis_contribution(cellchat_cns, signaling = "APP") +
          ggtitle("APP L-R contribution — CNS"))
}

app_int <- cns_net %>%
  filter(pathway_name == "APP") %>%
  arrange(desc(prob))

if (nrow(app_int) > 0) {
  app_int %>%
    select(source, target, interaction_name, prob, pval) %>%
    head(20) %>%
    kable(caption = "Top 20 APP pathway interactions (CNS)")
}

Top 20 APP pathway interactions (CNS)

source	target	interaction_name	prob	pval
Stroma	DCs	APP_CD74	0.2961797	0
Stroma	Pre-B	APP_CD74	0.2893632	0
Stroma	Plasma_B	APP_CD74	0.2013547	0
Stroma	Microglia-like	APP_TREM2_TYROBP	0.1883189	0
DCs	DCs	APP_CD74	0.1527674	0
Stroma	Other	APP_CD74	0.1497289	0
DCs	Pre-B	APP_CD74	0.1485548	0
Neutrophils	DCs	APP_CD74	0.1354687	0
Microglia-like	DCs	APP_CD74	0.1327085	0
Neutrophils	Pre-B	APP_CD74	0.1316590	0
Stroma	Macrophages	APP_TREM2_TYROBP	0.1299598	0
Microglia-like	Pre-B	APP_CD74	0.1289648	0
Macrophages	DCs	APP_CD74	0.1234323	0
Monocytes	DCs	APP_CD74	0.1200592	0
Macrophages	Pre-B	APP_CD74	0.1199142	0
Monocytes	Pre-B	APP_CD74	0.1166244	0
DCs	Plasma_B	APP_CD74	0.0974970	0
DCs	Microglia-like	APP_TREM2_TYROBP	0.0904237	0
Neutrophils	Plasma_B	APP_CD74	0.0858229	0
Microglia-like	Plasma_B	APP_CD74	0.0839759	0

Step 2b — ApoE Pathway

apoe_in_cns <- "ApoE" %in% cellchat_cns@netP$pathways

cat("ApoE pathway in CNS:", apoe_in_cns, "\n\n")

## ApoE pathway in CNS: TRUE

if (apoe_in_cns) {
  netVisual_aggregate(cellchat_cns, signaling = "ApoE", layout = "circle")
}

apoe_int <- cns_net %>%
  filter(pathway_name == "ApoE") %>%
  arrange(desc(prob))

if (nrow(apoe_int) > 0) {
  apoe_int %>%
    select(source, target, interaction_name, prob, pval) %>%
    kable(caption = "ApoE pathway interactions (CNS)")
}

ApoE pathway interactions (CNS)

source	target	interaction_name	prob	pval
Macrophages	Microglia-like	APOE_TREM2_TYROBP	0.2692462	0
Monocytes	Microglia-like	APOE_TREM2_TYROBP	0.2058687	0
Macrophages	Macrophages	APOE_TREM2_TYROBP	0.1917321	0
Stroma	Microglia-like	APOE_TREM2_TYROBP	0.1462329	0
Monocytes	Macrophages	APOE_TREM2_TYROBP	0.1430293	0
Stroma	Macrophages	APOE_TREM2_TYROBP	0.0993201	0
Pre-B	Microglia-like	APOE_TREM2_TYROBP	0.0897621	0
Pre-B	Macrophages	APOE_TREM2_TYROBP	0.0596989	0
Microglia-like	Microglia-like	APOE_TREM2_TYROBP	0.0503086	0
Microglia-like	Macrophages	APOE_TREM2_TYROBP	0.0329804	0
Basophils	Microglia-like	APOE_TREM2_TYROBP	0.0261705	0
Basophils	Macrophages	APOE_TREM2_TYROBP	0.0170075	0

Step 2c — TREM2 Target Gene Expression

trem2_genes <- c("Trem2", "Tyrobp", "App", "Apoe", "Lpl", "Cst7", "Spp1")
trem2_present <- trem2_genes[trem2_genes %in% rownames(combined)]

# Detection in CNS myeloid populations
myeloid_groups <- c("Microglia-like", "Macrophages", "Monocytes", "DCs")
cns_meta <- combined@meta.data[combined$Tissue == "CNS" &
                                 combined$cellchat_group %in% myeloid_groups, ]
cns_counts <- GetAssayData(combined, layer = "counts")[, rownames(cns_meta)]

results <- list()
for (gene in trem2_present) {
  det <- tapply(cns_counts[gene, ], cns_meta$cellchat_group,
                function(x) round(mean(x > 0) * 100, 1))
  for (grp in names(det)) {
    results[[length(results) + 1]] <- data.frame(
      Gene = gene, Group = grp, Pct = det[grp], stringsAsFactors = FALSE
    )
  }
}

trem2_df <- bind_rows(results) %>%
  pivot_wider(names_from = Gene, values_from = Pct, values_fill = 0)

trem2_mat <- as.matrix(trem2_df[, -1])
rownames(trem2_mat) <- trem2_df$Group

ht <- Heatmap(trem2_mat,
              name = "% Detected",
              column_title = "TREM2 programme — CNS myeloid cells",
              col = circlize::colorRamp2(c(0, 10, 40, 80),
                                          c("white", "lightyellow", "orange", "red")),
              cluster_rows = FALSE, cluster_columns = FALSE,
              cell_fun = function(j, i, x, y, width, height, fill) {
                grid.text(sprintf("%.1f", trem2_mat[i, j]), x, y,
                          gp = gpar(fontsize = 9))
              })
ComplexHeatmap::draw(ht)

Step 3 — Niche Retention Circuits

Two parallel retention systems keep B-ALL and Vγ6Vδ4 in the CNS:

• CXCL12 → CXCR4: Stroma → B-ALL (niche retention)
• CXCL16 → CXCR6: Stroma → Vγ6Vδ4 (tissue residency)

Step 3a — CXCL Pathway Detail

cxcl_int <- cns_net %>%
  filter(pathway_name == "CXCL") %>%
  arrange(desc(prob))

cat("All CXCL interactions in CNS:", nrow(cxcl_int), "\n\n")

## All CXCL interactions in CNS: 33

cxcl_int %>%
  select(source, target, interaction_name, prob, pval) %>%
  kable(caption = "All CXCL interactions (CNS)")

All CXCL interactions (CNS)

source	target	interaction_name	prob	pval
Macrophages	CD8_Tpex	CXCL4_CXCR3	0.0908567	0
Stroma	Monocytes	CXCL12_CXCR4	0.0876590	0
Macrophages	CD4_T	CXCL4_CXCR3	0.0744119	0
Macrophages	CD8_Teff	CXCL4_CXCR3	0.0717662	0
Macrophages	CD8_Tex	CXCL4_CXCR3	0.0450944	0
Stroma	Neutrophils	CXCL12_CXCR4	0.0395500	0
Stroma	B-ALL	CXCL12_CXCR4	0.0389521	0
Stroma	Vg6Vd4	CXCL16_CXCR6	0.0369221	0
Stroma	Vg4	CXCL12_CXCR4	0.0368038	0
Stroma	Pre-B	CXCL12_CXCR4	0.0353603	0
Stroma	Vg4	CXCL16_CXCR6	0.0325817	0
Stroma	CD8_Tex	CXCL16_CXCR6	0.0304104	0
Macrophages	Vg6Vd4	CXCL16_CXCR6	0.0282805	0
Stroma	NKT-like	CXCL16_CXCR6	0.0265831	0
Stroma	Basophils	CXCL12_CXCR4	0.0262778	0
Stroma	CD8_Teff	CXCL16_CXCR6	0.0259910	0
Macrophages	Vg4	CXCL16_CXCR6	0.0249296	0
Macrophages	CD8_Tex	CXCL16_CXCR6	0.0232560	0
Stroma	CD4_T	CXCL12_CXCR4	0.0230100	0
Stroma	CD8_Tpex	CXCL16_CXCR6	0.0220625	0
Macrophages	Vg6Vd4	CXCL4_CXCR3	0.0215555	0
Stroma	Other_gd	CXCL16_CXCR6	0.0210682	0
Macrophages	NKT-like	CXCL16_CXCR6	0.0203103	0
Macrophages	NKT-like	CXCL4_CXCR3	0.0202821	0
Macrophages	CD8_Teff	CXCL16_CXCR6	0.0198551	0
Stroma	DCs	CXCL12_CXCR4	0.0171118	0
Macrophages	CD8_Tpex	CXCL16_CXCR6	0.0168380	0
Macrophages	Other_gd	CXCL16_CXCR6	0.0160753	0
Macrophages	Other	CXCL4_CXCR3	0.0131770	0
Stroma	Stroma	CXCL12_ACKR3	0.0101097	0
Stroma	CD4_T	CXCL16_CXCR6	0.0066057	0
Stroma	Plasma_B	CXCL12_CXCR4	0.0064916	0
Macrophages	CD4_T	CXCL16_CXCR6	0.0050226	0

Step 3b — CXCL12/CXCR4 in BM vs CNS

cxcl12_cns <- cns_net %>%
  filter(grepl("CXCL12.*CXCR4", interaction_name)) %>%
  select(source, target, interaction_name, prob) %>%
  mutate(Tissue = "CNS")

cxcl12_bm <- bm_net %>%
  filter(grepl("CXCL12.*CXCR4", interaction_name)) %>%
  select(source, target, interaction_name, prob) %>%
  mutate(Tissue = "BM")

cxcl12_all <- bind_rows(cxcl12_cns, cxcl12_bm) %>% arrange(Tissue, desc(prob))

if (nrow(cxcl12_all) > 0) {
  cxcl12_all %>%
    kable(caption = "CXCL12 → CXCR4 interactions — BM vs CNS")
} else {
  cat("No CXCL12-CXCR4 interactions detected\n")
}

CXCL12 → CXCR4 interactions — BM vs CNS

source	target	interaction_name	prob	Tissue
Stroma	Basophils	CXCL12_CXCR4	0.0235702	BM
Stroma	Progenitors	CXCL12_CXCR4	0.0222942	BM
Stroma	Neutrophils	CXCL12_CXCR4	0.0124922	BM
Stroma	B-ALL	CXCL12_CXCR4	0.0104463	BM
Stroma	Monocytes	CXCL12_CXCR4	0.0088057	BM
Stroma	Pre-B	CXCL12_CXCR4	0.0086065	BM
Stroma	NKT-like	CXCL12_CXCR4	0.0065604	BM
Stroma	Other_gd	CXCL12_CXCR4	0.0031240	BM
Stroma	Stroma	CXCL12_CXCR4	0.0024642	BM
Stroma	Monocytes	CXCL12_CXCR4	0.0876590	CNS
Stroma	Neutrophils	CXCL12_CXCR4	0.0395500	CNS
Stroma	B-ALL	CXCL12_CXCR4	0.0389521	CNS
Stroma	Vg4	CXCL12_CXCR4	0.0368038	CNS
Stroma	Pre-B	CXCL12_CXCR4	0.0353603	CNS
Stroma	Basophils	CXCL12_CXCR4	0.0262778	CNS
Stroma	CD4_T	CXCL12_CXCR4	0.0230100	CNS
Stroma	DCs	CXCL12_CXCR4	0.0171118	CNS
Stroma	Plasma_B	CXCL12_CXCR4	0.0064916	CNS

Step 3c — CXCL16/CXCR6 (Vγ6Vδ4 Retention)

cxcl16_int <- cns_net %>%
  filter(grepl("CXCL16.*CXCR6", interaction_name)) %>%
  arrange(desc(prob))

if (nrow(cxcl16_int) > 0) {
  cat("CXCL16 → CXCR6 interactions in CNS:\n\n")
  cxcl16_int %>%
    select(source, target, interaction_name, prob, pval) %>%
    kable()
} else {
  cat("No CXCL16-CXCR6 interactions detected\n")
}

## CXCL16 → CXCR6 interactions in CNS:

source	target	interaction_name	prob	pval
Stroma	Vg6Vd4	CXCL16_CXCR6	0.0369221	0
Stroma	Vg4	CXCL16_CXCR6	0.0325817	0
Stroma	CD8_Tex	CXCL16_CXCR6	0.0304104	0
Macrophages	Vg6Vd4	CXCL16_CXCR6	0.0282805	0
Stroma	NKT-like	CXCL16_CXCR6	0.0265831	0
Stroma	CD8_Teff	CXCL16_CXCR6	0.0259910	0
Macrophages	Vg4	CXCL16_CXCR6	0.0249296	0
Macrophages	CD8_Tex	CXCL16_CXCR6	0.0232560	0
Stroma	CD8_Tpex	CXCL16_CXCR6	0.0220625	0
Stroma	Other_gd	CXCL16_CXCR6	0.0210682	0
Macrophages	NKT-like	CXCL16_CXCR6	0.0203103	0
Macrophages	CD8_Teff	CXCL16_CXCR6	0.0198551	0
Macrophages	CD8_Tpex	CXCL16_CXCR6	0.0168380	0
Macrophages	Other_gd	CXCL16_CXCR6	0.0160753	0
Stroma	CD4_T	CXCL16_CXCR6	0.0066057	0
Macrophages	CD4_T	CXCL16_CXCR6	0.0050226	0

Step 3d — Retention Receptor Expression

ret_genes <- c("Cxcl12", "Cxcr4", "Cxcl16", "Cxcr6")
ret_present <- ret_genes[ret_genes %in% rownames(combined)]

key_groups <- c("Stroma", "B-ALL", "Vg6Vd4", "Vg4", "Other_gd",
                "Macrophages", "Microglia-like", "Monocytes")

cns_meta <- combined@meta.data[combined$Tissue == "CNS" &
                                 combined$cellchat_group %in% key_groups, ]
cns_counts <- GetAssayData(combined, layer = "counts")[, rownames(cns_meta)]

results <- list()
for (gene in ret_present) {
  det <- tapply(cns_counts[gene, ], cns_meta$cellchat_group,
                function(x) round(mean(x > 0) * 100, 1))
  for (grp in names(det)) {
    results[[length(results) + 1]] <- data.frame(
      Gene = gene, Group = grp, Pct = det[grp], stringsAsFactors = FALSE
    )
  }
}

ret_df <- bind_rows(results) %>%
  pivot_wider(names_from = Gene, values_from = Pct, values_fill = 0)

ret_mat <- as.matrix(ret_df[, -1])
rownames(ret_mat) <- ret_df$Group

ht <- Heatmap(ret_mat,
              name = "% Detected",
              column_title = "Niche retention receptor expression — CNS",
              col = circlize::colorRamp2(c(0, 10, 30, 60),
                                          c("white", "lightyellow", "orange", "red")),
              cluster_rows = FALSE, cluster_columns = FALSE,
              cell_fun = function(j, i, x, y, width, height, fill) {
                grid.text(sprintf("%.1f", ret_mat[i, j]), x, y,
                          gp = gpar(fontsize = 9))
              })
ComplexHeatmap::draw(ht)

Step 4 — TGFβ Immunosuppressive Network

Step 4a — TGFβ Pathway in CNS

tgfb_in_cns <- grep("TGFb|TGFB", cellchat_cns@netP$pathways,
                     value = TRUE, ignore.case = TRUE)

cat("TGFb pathways in CNS:", paste(tgfb_in_cns, collapse = ", "), "\n\n")

## TGFb pathways in CNS: TGFb

for (p in tgfb_in_cns) {
  netVisual_aggregate(cellchat_cns, signaling = p, layout = "circle")
}

tgfb_int <- cns_net %>%
  filter(grepl("TGFb|TGFB", pathway_name, ignore.case = TRUE)) %>%
  arrange(desc(prob))

if (nrow(tgfb_int) > 0) {
  tgfb_int %>%
    select(source, target, interaction_name, prob, pval) %>%
    head(30) %>%
    kable(caption = "Top 30 TGFβ interactions (CNS)")
}

Top 30 TGFβ interactions (CNS)

source	target	interaction_name	prob	pval
Basophils	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0782119	0
Microglia-like	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0573102	0
Neutrophils	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0571360	0
DCs	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0557982	0
Monocytes	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0525632	0
Other	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0453157	0
Basophils	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0337040	0
Macrophages	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0276819	0
B-ALL	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0257600	0
Microglia-like	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0243822	0
Neutrophils	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0243255	0
DCs	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0237171	0
Other_gd	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0231569	0
Monocytes	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0222981	0
NKT-like	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0197716	0
Pre-B	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0191886	0
Other	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0191856	0
CD8_Tex	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0181498	0
Vg6Vd4	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0178296	0
CD8_Teff	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0174341	0
CD4_T	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0146419	0
CD8_Tpex	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0144551	0
Macrophages	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0115682	0
Vg4	Microglia-like	TGFB1_TGFBR1_TGFBR2	0.0111526	0
B-ALL	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0107526	0
Basophils	Basophils	TGFB1_TGFBR1_TGFBR2	0.0106710	0
Other_gd	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0096510	0
Basophils	Other	TGFB1_TGFBR1_TGFBR2	0.0096267	0
Basophils	Stroma	TGFB1_ACVR1_TGFBR1	0.0085978	0
NKT-like	Monocytes	TGFB1_TGFBR1_TGFBR2	0.0082235	0

Step 4b — TGFβ: BM vs CNS Comparison

tgfb_cns_sum <- cns_net %>%
  filter(grepl("TGFb|TGFB", pathway_name, ignore.case = TRUE)) %>%
  group_by(source) %>%
  summarise(cns_prob = round(sum(prob), 3), cns_n = n(), .groups = "drop")

tgfb_bm_sum <- bm_net %>%
  filter(grepl("TGFb|TGFB", pathway_name, ignore.case = TRUE)) %>%
  group_by(source) %>%
  summarise(bm_prob = round(sum(prob), 3), bm_n = n(), .groups = "drop")

tgfb_comp <- full_join(tgfb_cns_sum, tgfb_bm_sum, by = "source") %>%
  mutate(across(where(is.numeric), ~replace_na(., 0))) %>%
  arrange(desc(cns_prob))

tgfb_comp %>%
  kable(caption = "TGFβ sender comparison — CNS vs BM")

TGFβ sender comparison — CNS vs BM

source	cns_prob	cns_n	bm_prob	bm_n
Basophils	0.176	11	0.105	12
Microglia-like	0.128	11	0.020	5
Neutrophils	0.127	11	0.103	12
DCs	0.124	11	0.104	12
Monocytes	0.117	11	0.079	12
Other	0.101	11	0.011	4
Macrophages	0.052	7	0.105	12
B-ALL	0.048	7	0.028	7
Other_gd	0.038	5	0.008	3
NKT-like	0.032	5	0.006	3
Pre-B	0.031	5	0.040	9
CD8_Tex	0.030	5	0.001	2
Vg6Vd4	0.029	5	0.000	0
CD8_Teff	0.028	5	0.007	3
Stroma	0.028	28	0.058	11
CD4_T	0.024	5	0.007	3
CD8_Tpex	0.023	5	0.007	3
Vg4	0.018	5	0.007	3
Plasma_B	0.004	4	0.000	2
CD8_Naive	0.000	0	0.008	3
Progenitors	0.000	0	0.103	12

Step 5 — Cell-Type Focused Analysis

Step 5a — Vg6Vd4 Communication

vg6_out <- cns_net %>% filter(source == "Vg6Vd4") %>% arrange(desc(prob))
if (nrow(vg6_out) > 0) {
  cat("Vg6Vd4 → targets in CNS:", nrow(vg6_out), "interactions\n\n")
  vg6_out %>%
    select(source, target, interaction_name, pathway_name, prob, pval) %>%
    head(40) %>%
    kable(caption = "Top 40 Vg6Vd4 outgoing interactions (CNS)")
} else {
  cat("No outgoing interactions from Vg6Vd4\n")
}

## Vg6Vd4 → targets in CNS: 194 interactions

Top 40 Vg6Vd4 outgoing interactions (CNS)

source	target	interaction_name	pathway_name	prob	pval
Vg6Vd4	CD8_Tpex	H2-K1_CD8B1	MHC-I	0.2324739	0
Vg6Vd4	CD8_Tpex	H2-D1_CD8B1	MHC-I	0.2310024	0
Vg6Vd4	Stroma	PPIA_BSG	CypA	0.1891784	0
Vg6Vd4	CD8_Tpex	H2-K1_CD8A	MHC-I	0.1872090	0
Vg6Vd4	CD8_Tpex	H2-D1_CD8A	MHC-I	0.1859546	0
Vg6Vd4	CD8_Tex	H2-K1_CD8B1	MHC-I	0.1822292	0
Vg6Vd4	CD8_Tex	H2-D1_CD8B1	MHC-I	0.1810008	0
Vg6Vd4	CD8_Tex	H2-K1_CD8A	MHC-I	0.1704870	0
Vg6Vd4	CD8_Tex	H2-D1_CD8A	MHC-I	0.1693213	0
Vg6Vd4	Monocytes	THY1_ADGRE5	ADGRE	0.1442768	0
Vg6Vd4	Microglia-like	PPIA_BSG	CypA	0.1259764	0
Vg6Vd4	Other	SELPLG_SELL	SELPLG	0.1248258	0
Vg6Vd4	Monocytes	THY1_ITGAM_ITGB2	THY1	0.1176268	0
Vg6Vd4	CD8_Naive	SELPLG_SELL	SELPLG	0.1119500	0
Vg6Vd4	Basophils	THY1_ADGRE5	ADGRE	0.1077053	0
Vg6Vd4	Pre-B	CD52_SIGLECG	CD52	0.0988059	0
Vg6Vd4	CD8_Tpex	H2-Q4_CD8B1	MHC-I	0.0969942	0
Vg6Vd4	CD8_Tpex	H2-T23_CD8B1	MHC-I	0.0942558	0
Vg6Vd4	CD8_Tpex	H2-Q7_CD8B1	MHC-I	0.0910767	0
Vg6Vd4	Macrophages	PTPRC_MRC1	CD45	0.0890579	0
Vg6Vd4	CD8_Tpex	THY1_ADGRE5	ADGRE	0.0871154	0
Vg6Vd4	Monocytes	THY1_ITGAX_ITGB2	THY1	0.0850646	0
Vg6Vd4	Microglia-like	THY1_ITGAM_ITGB2	THY1	0.0837339	0
Vg6Vd4	Basophils	THY1_ITGAM_ITGB2	THY1	0.0835026	0
Vg6Vd4	Pre-B	SELPLG_SELL	SELPLG	0.0793487	0
Vg6Vd4	DCs	THY1_ITGAM_ITGB2	THY1	0.0785057	0
Vg6Vd4	CD8_Tpex	H2-Q4_CD8A	MHC-I	0.0755132	0
Vg6Vd4	CD8_Tpex	H2-T23_CD8A	MHC-I	0.0733320	0
Vg6Vd4	CD8_Tex	H2-Q4_CD8B1	MHC-I	0.0732368	0
Vg6Vd4	CD8_Tex	H2-T23_CD8B1	MHC-I	0.0711163	0
Vg6Vd4	CD8_Tpex	H2-Q7_CD8A	MHC-I	0.0708035	0
Vg6Vd4	CD8_Tex	H2-Q7_CD8B1	MHC-I	0.0686585	0
Vg6Vd4	Neutrophils	THY1_ITGAM_ITGB2	THY1	0.0683062	0
Vg6Vd4	CD8_Tex	THY1_ADGRE5	ADGRE	0.0681645	0
Vg6Vd4	CD8_Tex	H2-Q4_CD8A	MHC-I	0.0679343	0
Vg6Vd4	CD4_T	THY1_ADGRE5	ADGRE	0.0671047	0
Vg6Vd4	CD8_Teff	H2-K1_CD8B1	MHC-I	0.0665865	0
Vg6Vd4	CD8_Teff	H2-D1_CD8B1	MHC-I	0.0660746	0
Vg6Vd4	CD8_Tex	H2-T23_CD8A	MHC-I	0.0659564	0
Vg6Vd4	Basophils	SELPLG_SELL	SELPLG	0.0656115	0

vg6_in <- cns_net %>% filter(target == "Vg6Vd4") %>% arrange(desc(prob))
if (nrow(vg6_in) > 0) {
  cat("Sources → Vg6Vd4 in CNS:", nrow(vg6_in), "interactions\n\n")
  vg6_in %>%
    select(source, target, interaction_name, pathway_name, prob, pval) %>%
    head(30) %>%
    kable(caption = "Top 30 incoming to Vg6Vd4 (CNS)")
}

## Sources → Vg6Vd4 in CNS: 204 interactions

Top 30 incoming to Vg6Vd4 (CNS)

source	target	interaction_name	pathway_name	prob	pval
Basophils	Vg6Vd4	CCL6_CCR2	CCL	0.2034164	0
Monocytes	Vg6Vd4	CCL6_CCR2	CCL	0.1263597	0
Macrophages	Vg6Vd4	CCL6_CCR2	CCL	0.1252960	0
B-ALL	Vg6Vd4	LGALS9_CD45	GALECTIN	0.1004244	0
Stroma	Vg6Vd4	PTN_NCL	PTN	0.0886199	0
Stroma	Vg6Vd4	COL1A2_CD44	COLLAGEN	0.0850295	0
Stroma	Vg6Vd4	MDK_NCL	MK	0.0814651	0
DCs	Vg6Vd4	CCL6_CCR2	CCL	0.0809162	0
Stroma	Vg6Vd4	COL6A2_CD44	COLLAGEN	0.0669959	0
Stroma	Vg6Vd4	COL1A1_CD44	COLLAGEN	0.0657416	0
Macrophages	Vg6Vd4	CCL8_CCR2	CCL	0.0610375	0
Stroma	Vg6Vd4	COL6A1_CD44	COLLAGEN	0.0596367	0
Macrophages	Vg6Vd4	IGF1_IGF1R	IGF	0.0570904	0
Stroma	Vg6Vd4	FN1_CD44	FN1	0.0533237	0
Microglia-like	Vg6Vd4	JAM1_ITGAL_ITGB2	JAM	0.0515015	0
B-ALL	Vg6Vd4	LGALS9_CD44	GALECTIN	0.0501173	0
Basophils	Vg6Vd4	CTSG_F2R	PARs	0.0463306	0
B-ALL	Vg6Vd4	GZMA_F2R	PARs	0.0382336	0
Stroma	Vg6Vd4	CXCL16_CXCR6	CXCL	0.0369221	0
B-ALL	Vg6Vd4	LTB4-LTA4H_LTB4R1	CysLTs	0.0368535	0
Microglia-like	Vg6Vd4	LGALS9_CD44	GALECTIN	0.0361118	0
Monocytes	Vg6Vd4	Cholesterol-Cholesterol-LIPA_RORA	Cholesterol	0.0360642	0
Other	Vg6Vd4	GZMA_F2R	PARs	0.0354417	0
Basophils	Vg6Vd4	LGALS9_CD44	GALECTIN	0.0339574	0
Stroma	Vg6Vd4	COL4A1_CD44	COLLAGEN	0.0321864	0
Monocytes	Vg6Vd4	SIRPB1A_CD47	SIRP	0.0319590	0
Neutrophils	Vg6Vd4	LTB4-LTA4H_LTB4R1	CysLTs	0.0319548	0
Monocytes	Vg6Vd4	Cholesterol-Cholesterol-LIPA_RORC	Cholesterol	0.0308640	0
Macrophages	Vg6Vd4	LGALS9_CD44	GALECTIN	0.0303297	0
Monocytes	Vg6Vd4	SIRPB1B_CD47	SIRP	0.0302450	0

tryCatch({
  print(netVisual_bubble(cellchat_cns, sources.use = "Vg6Vd4",
                         remove.isolate = TRUE,
                         title.name = "Vg6Vd4 → targets (CNS)"))
}, error = function(e) cat("Bubble plot failed\n"))

tryCatch({
  print(netVisual_bubble(cellchat_cns, targets.use = "Vg6Vd4",
                         remove.isolate = TRUE,
                         title.name = "Sources → Vg6Vd4 (CNS)"))
}, error = function(e) cat("Bubble plot failed\n"))

Vg6Vd4 Outgoing by Pathway

if (nrow(vg6_out) > 0) {
  vg6_out %>%
    group_by(pathway_name) %>%
    summarise(
      n_targets = n_distinct(target),
      n_LR = n(),
      total_prob = round(sum(prob), 3),
      .groups = "drop"
    ) %>%
    arrange(desc(total_prob)) %>%
    kable(caption = "Vg6Vd4 outgoing pathways ranked (CNS)")
}

Vg6Vd4 outgoing pathways ranked (CNS)

pathway_name	n_targets	n_LR	total_prob
MHC-I	5	57	3.199
ADGRE	12	12	0.755
THY1	7	12	0.704
SELPLG	7	7	0.508
MIF	6	12	0.340
CypA	2	2	0.315
CD45	2	2	0.152
CD52	3	3	0.124
GALECTIN	12	14	0.089
CLEC	4	10	0.087
IL16	3	3	0.070
SEMA4	7	7	0.069
Cholesterol	8	10	0.065
Prostaglandin	8	9	0.064
CysLTs	8	8	0.043
CD48	4	4	0.035
TGFb	4	5	0.029
KLK	4	4	0.022
LIGHT	5	5	0.017
FLT3	3	3	0.015
RANKL	2	2	0.005
DHEA	1	1	0.002
ADGRA	1	1	0.001
BST2	1	1	0.001

Step 5b — All γδ T Cell Communication

gdt_groups <- c("Vg6Vd4", "Vg4", "Other_gd")

gdt_involved <- cns_net %>%
  filter(source %in% gdt_groups | target %in% gdt_groups)

cat("Total interactions involving γδ T cells in CNS:", nrow(gdt_involved), "\n\n")

## Total interactions involving γδ T cells in CNS: 1226

gdt_involved %>%
  group_by(source, target, pathway_name) %>%
  summarise(n_LR = n(), total_prob = round(sum(prob), 4), .groups = "drop") %>%
  arrange(desc(total_prob)) %>%
  head(40) %>%
  kable(caption = "Top 40 γδ T cell interactions — CNS")

Top 40 γδ T cell interactions — CNS

source	target	pathway_name	n_LR	total_prob
Vg6Vd4	CD8_Tpex	MHC-I	15	1.4473
Vg4	CD8_Tpex	MHC-I	17	1.4163
Other_gd	CD8_Tpex	MHC-I	17	1.3444
Vg6Vd4	CD8_Tex	MHC-I	14	1.1868
Vg4	CD8_Tex	MHC-I	16	1.1627
Other_gd	CD8_Tex	MHC-I	16	1.1037
Stroma	Vg6Vd4	COLLAGEN	21	0.5507
Vg6Vd4	CD8_Teff	MHC-I	14	0.4042
Vg4	CD8_Teff	MHC-I	16	0.3949
Other_gd	CD8_Teff	MHC-I	16	0.3744
Stroma	Vg4	COLLAGEN	7	0.3184
Macrophages	Vg6Vd4	CCL	5	0.2350
Vg4	Monocytes	THY1	2	0.2043
Basophils	Vg6Vd4	CCL	1	0.2034
Vg6Vd4	Monocytes	THY1	2	0.2027
Stroma	Other_gd	COLLAGEN	7	0.1939
Vg6Vd4	Stroma	CypA	1	0.1892
Other_gd	Stroma	CypA	1	0.1874
Macrophages	Vg4	CCL	5	0.1821
Vg4	Stroma	CypA	1	0.1759
Other_gd	Monocytes	THY1	2	0.1744
Basophils	Vg4	CCL	1	0.1623
B-ALL	Vg4	GALECTIN	2	0.1523
B-ALL	Vg6Vd4	GALECTIN	2	0.1505
Vg4	Monocytes	ADGRE	1	0.1454
Vg6Vd4	Monocytes	ADGRE	1	0.1443
Macrophages	Other_gd	CCL	5	0.1358
Vg4	DCs	THY1	2	0.1340
Vg6Vd4	DCs	THY1	2	0.1329
B-ALL	Other_gd	GALECTIN	2	0.1308
Monocytes	Vg6Vd4	CCL	1	0.1264
Vg6Vd4	Microglia-like	CypA	1	0.1260
Other_gd	Monocytes	ADGRE	1	0.1249
Vg6Vd4	Other	SELPLG	1	0.1248
Other_gd	Microglia-like	CypA	1	0.1247
Basophils	Other_gd	CCL	1	0.1242
Vg6Vd4	CD8_Naive	MHC-I	7	0.1233
Monocytes	Other_gd	SIRP	3	0.1229
Vg4	Other	SELPLG	1	0.1213
Vg4	CD8_Naive	MHC-I	8	0.1204

Step 5c — B-ALL Communication

ball_in <- cns_net %>% filter(target == "B-ALL") %>% arrange(desc(prob))
cat("B-ALL incoming in CNS:", nrow(ball_in), "\n\n")

## B-ALL incoming in CNS: 309

ball_in %>%
  select(source, target, interaction_name, pathway_name, prob, pval) %>%
  head(40) %>%
  kable(caption = "Top 40 incoming to B-ALL (CNS)")

Top 40 incoming to B-ALL (CNS)

source	target	interaction_name	pathway_name	prob	pval
Stroma	B-ALL	PTN_NCL	PTN	0.1279171	0
B-ALL	B-ALL	PPIA_BSG	CypA	0.1218360	0
Stroma	B-ALL	MDK_NCL	MK	0.1180004	0
B-ALL	B-ALL	LGALS9_CD45	GALECTIN	0.0917183	0
Stroma	B-ALL	COL1A2_ITGA9_ITGB1	COLLAGEN	0.0603417	0
Monocytes	B-ALL	SIRPB1A_CD47	SIRP	0.0496955	0
B-ALL	B-ALL	LGALS9_P4HB	GALECTIN	0.0493893	0
Stroma	B-ALL	COL6A2_ITGA9_ITGB1	COLLAGEN	0.0472735	0
Monocytes	B-ALL	SIRPB1B_CD47	SIRP	0.0470765	0
Stroma	B-ALL	COL1A1_ITGA9_ITGB1	COLLAGEN	0.0463701	0
Monocytes	B-ALL	SIRPB1C_CD47	SIRP	0.0453306	0
Stroma	B-ALL	FN1_ITGA4_ITGB1	FN1	0.0433364	0
Stroma	B-ALL	COL6A1_ITGA9_ITGB1	COLLAGEN	0.0419833	0
Stroma	B-ALL	THBS2_CD47	THBS	0.0407578	0
Stroma	B-ALL	CXCL12_CXCR4	CXCL	0.0389521	0
Stroma	B-ALL	MDK_ITGA4_ITGB1	MK	0.0357699	0
Monocytes	B-ALL	PECAM1_PECAM1	PECAM1	0.0357338	0
Basophils	B-ALL	PECAM1_PECAM1	PECAM1	0.0255439	0
Stroma	B-ALL	THBS3_CD47	THBS	0.0252625	0
Pre-B	B-ALL	PECAM1_PECAM1	PECAM1	0.0238706	0
CD8_Tpex	B-ALL	THY1_ADGRE5	ADGRE	0.0227994	0
Stroma	B-ALL	COL4A1_ITGA9_ITGB1	COLLAGEN	0.0224646	0
CD8_Tex	B-ALL	THY1_ADGRE5	ADGRE	0.0217347	0
Vg4	B-ALL	THY1_ADGRE5	ADGRE	0.0207327	0
CD8_Teff	B-ALL	THY1_ADGRE5	ADGRE	0.0206161	0
Stroma	B-ALL	LAMB2_ITGA9_ITGB1	LAMININ	0.0205940	0
Vg6Vd4	B-ALL	THY1_ADGRE5	ADGRE	0.0205558	0
B-ALL	B-ALL	THY1_ADGRE5	ADGRE	0.0195692	0
Monocytes	B-ALL	SEMA4D_CD72	SEMA4	0.0172237	0
B-ALL	B-ALL	PECAM1_PECAM1	PECAM1	0.0168901	0
Stroma	B-ALL	FN1_ITGA4_ITGB7	FN1	0.0160582	0
Stroma	B-ALL	COL4A2_ITGA9_ITGB1	COLLAGEN	0.0152985	0
Stroma	B-ALL	LAMA4_ITGA9_ITGB1	LAMININ	0.0141585	0
CD8_Tex	B-ALL	H2-K1_CD8B1	MHC-I	0.0140360	0
CD8_Tpex	B-ALL	H2-K1_CD8B1	MHC-I	0.0137820	0
CD8_Tpex	B-ALL	SEMA4D_CD72	SEMA4	0.0137302	0
Stroma	B-ALL	THBS1_CD47	THBS	0.0137107	0
CD8_Teff	B-ALL	H2-K1_CD8B1	MHC-I	0.0137012	0
CD4_T	B-ALL	H2-K1_CD8B1	MHC-I	0.0133907	0
Microglia-like	B-ALL	SELPLG_SELL	SELPLG	0.0133738	0

ball_out <- cns_net %>% filter(source == "B-ALL") %>% arrange(desc(prob))
cat("B-ALL outgoing in CNS:", nrow(ball_out), "\n\n")

## B-ALL outgoing in CNS: 337

ball_out %>%
  select(source, target, interaction_name, pathway_name, prob, pval) %>%
  head(40) %>%
  kable(caption = "Top 40 outgoing from B-ALL (CNS)")

Top 40 outgoing from B-ALL (CNS)

source	target	interaction_name	pathway_name	prob	pval
B-ALL	Stroma	PPIA_BSG	CypA	0.2283420	0
B-ALL	Microglia-like	PPIA_BSG	CypA	0.1545499	0
B-ALL	Monocytes	LGALS9_CD45	GALECTIN	0.1424436	0
B-ALL	Monocytes	THY1_ADGRE5	ADGRE	0.1381902	0
B-ALL	CD8_Tpex	LGALS9_CD45	GALECTIN	0.1370330	0
B-ALL	CD8_Teff	LGALS9_CD45	GALECTIN	0.1357315	0
B-ALL	CD8_Tex	LGALS9_CD45	GALECTIN	0.1353338	0
B-ALL	CD4_T	LGALS9_CD45	GALECTIN	0.1281408	0
B-ALL	Other	LGALS9_CD45	GALECTIN	0.1278967	0
B-ALL	CD8_Naive	LGALS9_CD45	GALECTIN	0.1278645	0
B-ALL	Pre-B	LGALS9_IGHM	GALECTIN	0.1256823	0
B-ALL	CD8_Tpex	LCK_CD8A_CD8B1	LCK	0.1237069	0
B-ALL	B-ALL	PPIA_BSG	CypA	0.1218360	0
B-ALL	NKT-like	LGALS9_CD45	GALECTIN	0.1192309	0
B-ALL	DCs	LGALS9_CD45	GALECTIN	0.1158341	0
B-ALL	Monocytes	THY1_ITGAM_ITGB2	THY1	0.1125167	0
B-ALL	DCs	MIF_CD74_CD44	MIF	0.1110026	0
B-ALL	Vg4	LGALS9_CD45	GALECTIN	0.1048894	0
B-ALL	Basophils	THY1_ADGRE5	ADGRE	0.1029759	0
B-ALL	CD8_Tex	LCK_CD8A_CD8B1	LCK	0.1026416	0
B-ALL	Other_gd	LGALS9_CD45	GALECTIN	0.1021184	0
B-ALL	Pre-B	LGALS9_CD45	GALECTIN	0.1016737	0
B-ALL	Other	MIF_CD74_CD44	MIF	0.1008936	0
B-ALL	Vg6Vd4	LGALS9_CD45	GALECTIN	0.1004244	0
B-ALL	B-ALL	LGALS9_CD45	GALECTIN	0.0917183	0
B-ALL	Monocytes	LGALS9_CD44	GALECTIN	0.0871337	0
B-ALL	Plasma_B	MIF_CD74_CD44	MIF	0.0865253	0
B-ALL	Pre-B	MIF_CD74_CXCR4	MIF	0.0858232	0
B-ALL	Pre-B	MIF_CD74_CD44	MIF	0.0837749	0
B-ALL	Stroma	LGALS9_P4HB	GALECTIN	0.0835998	0
B-ALL	CD8_Tpex	THY1_ADGRE5	ADGRE	0.0832058	0
B-ALL	Macrophages	PTPRC_MRC1	CD45	0.0812482	0
B-ALL	Monocytes	THY1_ITGAX_ITGB2	THY1	0.0812389	0
B-ALL	Microglia-like	THY1_ITGAM_ITGB2	THY1	0.0799628	0
B-ALL	Basophils	THY1_ITGAM_ITGB2	THY1	0.0797410	0
B-ALL	DCs	THY1_ITGAM_ITGB2	THY1	0.0749508	0
B-ALL	Basophils	LGALS9_P4HB	GALECTIN	0.0748385	0
B-ALL	Neutrophils	LGALS9_P4HB	GALECTIN	0.0683865	0
B-ALL	Other	SELPLG_SELL	SELPLG	0.0680590	0
B-ALL	Neutrophils	THY1_ITGAM_ITGB2	THY1	0.0651805	0

tryCatch({
  print(netVisual_bubble(cellchat_merged,
                         targets.use = "B-ALL",
                         comparison = c(1, 2),
                         remove.isolate = TRUE,
                         title.name = "Incoming to B-ALL — BM vs CNS",
                         angle.x = 45))
}, error = function(e) cat("Bubble comparison failed:", e$message, "\n"))

Step 5d — Stroma Outgoing (Niche Signals)

stroma_out <- cns_net %>% filter(source == "Stroma") %>% arrange(desc(prob))
cat("Stroma outgoing in CNS:", nrow(stroma_out), "\n\n")

## Stroma outgoing in CNS: 1133

stroma_out %>%
  select(source, target, interaction_name, pathway_name, prob, pval) %>%
  head(40) %>%
  kable(caption = "Top 40 Stroma → target interactions (CNS)")

Top 40 Stroma → target interactions (CNS)

source	target	interaction_name	pathway_name	prob	pval
Stroma	DCs	APP_CD74	APP	0.2961797	0
Stroma	Pre-B	APP_CD74	APP	0.2893632	0
Stroma	Plasma_B	APP_CD74	APP	0.2013547	0
Stroma	Microglia-like	APP_TREM2_TYROBP	APP	0.1883189	0
Stroma	Stroma	PPIA_BSG	CypA	0.1671204	0
Stroma	Other	APP_CD74	APP	0.1497289	0
Stroma	Microglia-like	APOE_TREM2_TYROBP	ApoE	0.1462329	0
Stroma	Monocytes	COL1A2_CD44	COLLAGEN	0.1439247	0
Stroma	CD8_Tpex	H2-D1_CD8B1	MHC-I	0.1358563	0
Stroma	Macrophages	APP_TREM2_TYROBP	APP	0.1299598	0
Stroma	B-ALL	PTN_NCL	PTN	0.1279171	0
Stroma	Monocytes	FN1_ITGA4_ITGB1	FN1	0.1186244	0
Stroma	B-ALL	MDK_NCL	MK	0.1180004	0
Stroma	CD8_Tpex	H2-K1_CD8B1	MHC-I	0.1173175	0
Stroma	Monocytes	COL6A2_CD44	COLLAGEN	0.1149697	0
Stroma	Monocytes	COL1A1_CD44	COLLAGEN	0.1129259	0
Stroma	CD8_Tpex	H2-D1_CD8A	MHC-I	0.1067865	0
Stroma	CD8_Tex	H2-D1_CD8B1	MHC-I	0.1036730	0
Stroma	Monocytes	COL6A1_CD44	COLLAGEN	0.1029221	0
Stroma	Macrophages	APOE_TREM2_TYROBP	ApoE	0.0993201	0
Stroma	Monocytes	MDK_ITGA4_ITGB1	MK	0.0992769	0
Stroma	Stroma	MDK_LRP1	MK	0.0972419	0
Stroma	CD8_Tex	H2-D1_CD8A	MHC-I	0.0963960	0
Stroma	CD4_T	FN1_ITGA4_ITGB1	FN1	0.0949843	0
Stroma	Basophils	COL1A2_ITGA2_ITGB1	COLLAGEN	0.0940224	0
Stroma	Other_gd	PTN_NCL	PTN	0.0930765	0
Stroma	Monocytes	FN1_CD44	FN1	0.0924776	0
Stroma	CD8_Tpex	H2-K1_CD8A	MHC-I	0.0917931	0
Stroma	NKT-like	PTN_NCL	PTN	0.0896040	0
Stroma	CD8_Tex	H2-K1_CD8B1	MHC-I	0.0890733	0
Stroma	Vg4	PTN_NCL	PTN	0.0889289	0
Stroma	Vg6Vd4	PTN_NCL	PTN	0.0886199	0
Stroma	Neutrophils	COL1A2_CD44	COLLAGEN	0.0882676	0
Stroma	Other	COL1A2_CD44	COLLAGEN	0.0879262	0
Stroma	Monocytes	CXCL12_CXCR4	CXCL	0.0876590	0
Stroma	CD4_T	PTN_NCL	PTN	0.0868904	0
Stroma	Other_gd	MDK_NCL	MK	0.0855957	0
Stroma	Vg6Vd4	COL1A2_CD44	COLLAGEN	0.0850295	0
Stroma	CD8_Teff	FN1_ITGA4_ITGB1	FN1	0.0849639	0
Stroma	CD8_Naive	PTN_NCL	PTN	0.0838981	0

tryCatch({
  print(netVisual_bubble(cellchat_cns, sources.use = "Stroma",
                         remove.isolate = TRUE,
                         title.name = "Stroma → targets (CNS)"))
}, error = function(e) cat("Stroma bubble failed\n"))

Stroma Outgoing by Pathway

stroma_out %>%
  group_by(pathway_name) %>%
  summarise(
    n_targets = n_distinct(target),
    n_LR = n(),
    total_prob = round(sum(prob), 3),
    .groups = "drop"
  ) %>%
  arrange(desc(total_prob)) %>%
  head(25) %>%
  kable(caption = "Stroma outgoing pathways ranked (CNS)")

Stroma outgoing pathways ranked (CNS)

pathway_name	n_targets	n_LR	total_prob
COLLAGEN	18	203	6.144
MK	20	52	2.570
FN1	20	64	2.445
APP	19	30	2.005
PTN	20	32	1.766
THBS	20	96	1.510
MHC-I	5	49	1.374
LAMININ	17	104	1.246
ICAM	17	57	0.991
CXCL	16	18	0.524
MIF	6	12	0.499
ADGRE	18	36	0.282
GALECTIN	14	19	0.267
ApoE	2	2	0.246
ANGPTL	9	17	0.226
CypA	1	1	0.167
COMPLEMENT	7	13	0.165
Prostaglandin	8	20	0.103
CX3C	4	4	0.095
SPP1	18	47	0.081
CLEC	4	5	0.076
MHC-II	3	12	0.074
GAS	2	2	0.057
ADGRG	3	6	0.056
PROS	2	2	0.054

Step 5e — Microglia-like Communication

mg_out <- cns_net %>% filter(source == "Microglia-like") %>% arrange(desc(prob))
mg_in  <- cns_net %>% filter(target == "Microglia-like") %>% arrange(desc(prob))

cat("Microglia-like outgoing:", nrow(mg_out), "| incoming:", nrow(mg_in), "\n\n")

## Microglia-like outgoing: 396 | incoming: 269

mg_out %>%
  select(source, target, interaction_name, pathway_name, prob, pval) %>%
  head(30) %>%
  kable(caption = "Top 30 Microglia-like outgoing (CNS)")

Top 30 Microglia-like outgoing (CNS)

source	target	interaction_name	pathway_name	prob	pval
Microglia-like	Other	SELPLG_SELL	SELPLG	0.1726795	0.00
Microglia-like	CD8_Tpex	H2-D1_CD8B1	MHC-I	0.1607619	0.00
Microglia-like	CD8_Naive	SELPLG_SELL	SELPLG	0.1557460	0.00
Microglia-like	Stroma	PPIA_BSG	CypA	0.1483455	0.00
Microglia-like	CD8_Tpex	H2-K1_CD8B1	MHC-I	0.1457623	0.00
Microglia-like	Basophils	LAIR1_LILRB4A	LAIR1	0.1395942	0.00
Microglia-like	DCs	APP_CD74	APP	0.1327085	0.00
Microglia-like	Pre-B	APP_CD74	APP	0.1289648	0.00
Microglia-like	CD8_Tpex	H2-D1_CD8A	MHC-I	0.1271469	0.00
Microglia-like	CD8_Tex	H2-D1_CD8B1	MHC-I	0.1235220	0.00
Microglia-like	Monocytes	JAM1_ITGAL_ITGB2	JAM	0.1186302	0.00
Microglia-like	CD8_Tex	H2-D1_CD8A	MHC-I	0.1150306	0.00
Microglia-like	CD8_Tpex	H2-K1_CD8A	MHC-I	0.1148545	0.00
Microglia-like	Pre-B	SELPLG_SELL	SELPLG	0.1119993	0.00
Microglia-like	CD8_Tex	H2-K1_CD8B1	MHC-I	0.1115353	0.00
Microglia-like	Monocytes	LGALS9_CD45	GALECTIN	0.1055026	0.00
Microglia-like	CD8_Tex	H2-K1_CD8A	MHC-I	0.1037703	0.00
Microglia-like	Microglia-like	PROS1_MERTK	PROS	0.1014361	0.00
Microglia-like	CD8_Tpex	LGALS9_CD45	GALECTIN	0.1013294	0.01
Microglia-like	CD8_Teff	LGALS9_CD45	GALECTIN	0.1003276	0.00
Microglia-like	CD8_Tex	LGALS9_CD45	GALECTIN	0.1000216	0.00
Microglia-like	CD4_T	LGALS9_CD45	GALECTIN	0.0945003	0.00
Microglia-like	Other	LGALS9_CD45	GALECTIN	0.0943134	0.02
Microglia-like	CD8_Naive	LGALS9_CD45	GALECTIN	0.0942887	0.00
Microglia-like	Basophils	SELPLG_SELL	SELPLG	0.0931816	0.00
Microglia-like	Pre-B	LGALS9_IGHM	GALECTIN	0.0926187	0.00
Microglia-like	Microglia-like	F11R_F11R	JAM	0.0861215	0.00
Microglia-like	Neutrophils	SELPLG_SELL	SELPLG	0.0845421	0.00
Microglia-like	Plasma_B	APP_CD74	APP	0.0839759	0.00
Microglia-like	CD8_Tpex	JAM1_ITGAL_ITGB2	JAM	0.0830251	0.00

mg_in %>%
  select(source, target, interaction_name, pathway_name, prob, pval) %>%
  head(30) %>%
  kable(caption = "Top 30 incoming to Microglia-like (CNS)")

Top 30 incoming to Microglia-like (CNS)

source	target	interaction_name	pathway_name	prob	pval
Macrophages	Microglia-like	APOE_TREM2_TYROBP	ApoE	0.2692462	0
CD8_Tpex	Microglia-like	CCL5_CCR5	CCL	0.2241862	0
CD8_Tex	Microglia-like	CCL5_CCR5	CCL	0.2092469	0
Monocytes	Microglia-like	APOE_TREM2_TYROBP	ApoE	0.2058687	0
Stroma	Microglia-like	APP_TREM2_TYROBP	APP	0.1883189	0
CD8_Teff	Microglia-like	CCL5_CCR5	CCL	0.1878446	0
B-ALL	Microglia-like	PPIA_BSG	CypA	0.1545499	0
Stroma	Microglia-like	APOE_TREM2_TYROBP	ApoE	0.1462329	0
Macrophages	Microglia-like	PPIA_BSG	CypA	0.1324007	0
Neutrophils	Microglia-like	PPIA_BSG	CypA	0.1320624	0
Basophils	Microglia-like	CSF1_CSF1R	CSF	0.1298026	0
Vg6Vd4	Microglia-like	PPIA_BSG	CypA	0.1259764	0
Other_gd	Microglia-like	PPIA_BSG	CypA	0.1246880	0
Other	Microglia-like	PPIA_BSG	CypA	0.1245714	0
Microglia-like	Microglia-like	PROS1_MERTK	PROS	0.1014361	0
CD8_Tpex	Microglia-like	THY1_ITGAM_ITGB2	THY1	0.0922240	0
DCs	Microglia-like	APP_TREM2_TYROBP	APP	0.0904237	0
Pre-B	Microglia-like	APOE_TREM2_TYROBP	ApoE	0.0897621	0
CD8_Tex	Microglia-like	THY1_ITGAM_ITGB2	THY1	0.0882101	0
Microglia-like	Microglia-like	F11R_F11R	JAM	0.0861215	0
Vg4	Microglia-like	THY1_ITGAM_ITGB2	THY1	0.0844080	0
CD8_Teff	Microglia-like	THY1_ITGAM_ITGB2	THY1	0.0839640	0
Vg6Vd4	Microglia-like	THY1_ITGAM_ITGB2	THY1	0.0837339	0
B-ALL	Microglia-like	THY1_ITGAM_ITGB2	THY1	0.0799628	0
Neutrophils	Microglia-like	APP_TREM2_TYROBP	APP	0.0795219	0
Basophils	Microglia-like	TGFB1_TGFBR1_TGFBR2	TGFb	0.0782119	0
Microglia-like	Microglia-like	APP_TREM2_TYROBP	APP	0.0777990	0
Macrophages	Microglia-like	GAS6_MERTK	GAS	0.0744734	0
Macrophages	Microglia-like	APP_TREM2_TYROBP	APP	0.0720421	0
Other_gd	Microglia-like	THY1_ITGAM_ITGB2	THY1	0.0717990	0

Step 6 — Pathway Summary Tables

Step 6a — Top CNS Pathways

cns_net %>%
  group_by(pathway_name) %>%
  summarise(
    n_interactions = n(),
    total_prob = round(sum(prob), 4),
    mean_prob  = round(mean(prob), 4),
    top_sender = names(sort(table(source), decreasing = TRUE))[1],
    top_receiver = names(sort(table(target), decreasing = TRUE))[1],
    .groups = "drop"
  ) %>%
  arrange(desc(total_prob)) %>%
  head(40) %>%
  kable(caption = "Top 40 pathways by total probability — CNS")

Top 40 pathways by total probability — CNS

pathway_name	n_interactions	total_prob	mean_prob	top_sender	top_receiver
MHC-I	1101	54.7625	0.0497	CD8_Tpex	CD8_Tpex
CCL	299	10.5343	0.0352	Macrophages	DCs
GALECTIN	389	9.9747	0.0256	B-ALL	Monocytes
ADGRE	329	9.1344	0.0278	CD4_T	Basophils
SELPLG	118	7.6787	0.0651	B-ALL	Basophils
THY1	120	6.2746	0.0523	B-ALL	Other
MIF	240	6.2476	0.0260	B-ALL	Neutrophils
COLLAGEN	203	6.1436	0.0303	Stroma	Basophils
APP	180	6.0920	0.0338	DCs	DCs
CypA	26	4.3411	0.1670	B-ALL	Stroma
MHC-II	114	3.3670	0.0295	DCs	CD4_T
CD45	38	2.9572	0.0778	B-ALL	Macrophages
ICAM	466	2.9208	0.0063	Vg4	Monocytes
MK	52	2.5696	0.0494	Stroma	Neutrophils
FN1	64	2.4452	0.0382	Stroma	Other
SIRP	80	2.3484	0.0294	Monocytes	B-ALL
LCK	30	2.0591	0.0686	B-ALL	CD8_Teff
CD52	54	1.8278	0.0338	Basophils	DCs
PTN	32	1.7658	0.0552	Stroma	Other
CD48	94	1.6484	0.0175	Basophils	Monocytes
THBS	96	1.5101	0.0157	Stroma	Macrophages
LAMININ	130	1.3777	0.0106	Stroma	Basophils
ApoE	12	1.3314	0.1109	Basophils	Macrophages
CLEC	150	1.2973	0.0086	DCs	NKT-like
TGFb	157	1.1577	0.0074	Stroma	Stroma
Prostaglandin	197	1.1056	0.0056	Stroma	Basophils
CysLTs	136	1.0691	0.0079	B-ALL	Basophils
CXCL	33	1.0153	0.0308	Stroma	CD4_T
SEMA4	174	0.9624	0.0055	Basophils	Microglia-like
JAM	36	0.8995	0.0250	Microglia-like	Microglia-like
Cholesterol	150	0.8885	0.0059	B-ALL	Vg4
LAIR1	49	0.8308	0.0170	Basophils	DCs
PECAM1	24	0.7999	0.0333	Monocytes	Monocytes
L1CAM	54	0.7674	0.0142	Basophils	Basophils
IL16	54	0.7614	0.0141	B-ALL	CD4_T
CEACAM	32	0.4999	0.0156	B-ALL	B-ALL
PARs	40	0.4977	0.0124	B-ALL	Vg6Vd4
VCAM	58	0.4566	0.0079	B-ALL	Macrophages
PECAM2	60	0.4075	0.0068	B-ALL	CD8_Teff
COMPLEMENT	52	0.3852	0.0074	B-ALL	Basophils

Step 6b — Top BM Pathways

bm_net %>%
  group_by(pathway_name) %>%
  summarise(
    n_interactions = n(),
    total_prob = round(sum(prob), 4),
    mean_prob  = round(mean(prob), 4),
    top_sender = names(sort(table(source), decreasing = TRUE))[1],
    top_receiver = names(sort(table(target), decreasing = TRUE))[1],
    .groups = "drop"
  ) %>%
  arrange(desc(total_prob)) %>%
  head(40) %>%
  kable(caption = "Top 40 pathways by total probability — BM")

Top 40 pathways by total probability — BM

pathway_name	n_interactions	total_prob	mean_prob	top_sender	top_receiver
MHC-I	1021	57.0599	0.0559	Progenitors	CD8_Tpex
GALECTIN	352	10.8326	0.0308	B-ALL	Monocytes
ADGRE	379	9.9951	0.0264	Basophils	Basophils
SELPLG	214	9.5173	0.0445	CD4_T	Progenitors
CCL	217	6.7551	0.0311	Macrophages	DCs
MIF	180	4.8066	0.0267	B-ALL	NKT-like
THY1	108	4.0276	0.0373	B-ALL	Basophils
VCAM	99	3.9788	0.0402	B-ALL	Macrophages
CD45	57	3.7311	0.0655	B-ALL	DCs
APP	192	3.5154	0.0183	DCs	DCs
MHC-II	148	3.3841	0.0229	DCs	CD4_T
CLEC	261	3.2529	0.0125	DCs	Other
SIRP	60	2.4253	0.0404	Monocytes	B-ALL
LCK	30	2.4218	0.0807	B-ALL	CD8_Naive
CD52	76	1.9594	0.0258	Basophils	DCs
CD48	127	1.8828	0.0148	Basophils	Monocytes
PECAM1	48	1.7549	0.0366	B-ALL	B-ALL
CypA	13	1.7364	0.1336	B-ALL	Neutrophils
ICAM	410	1.3320	0.0032	Neutrophils	Monocytes
SEMA4	190	1.1271	0.0059	CD8_Tpex	DCs
Cholesterol	136	1.0596	0.0078	B-ALL	Vg4
COLLAGEN	140	0.9854	0.0070	Stroma	Stroma
ApoE	12	0.8840	0.0737	Basophils	Macrophages
Prostaglandin	171	0.8814	0.0052	Neutrophils	Progenitors
TGFb	133	0.8072	0.0061	Basophils	Basophils
IL16	72	0.7994	0.0111	B-ALL	CD4_T
SPP1	102	0.7668	0.0075	Progenitors	Stroma
L1CAM	60	0.7433	0.0124	Basophils	Basophils
PECAM2	84	0.6239	0.0074	B-ALL	CD8_Tex
SELL	40	0.6032	0.0151	B-ALL	Basophils
CXCL	27	0.5641	0.0209	Macrophages	NKT-like
LAMININ	116	0.4543	0.0039	Stroma	Stroma
CysLTs	90	0.4479	0.0050	B-ALL	Basophils
CEACAM	50	0.4472	0.0089	Monocytes	Neutrophils
IGF	18	0.4385	0.0244	Macrophages	Basophils
LAIR1	49	0.3338	0.0068	Basophils	Basophils
CSF	10	0.3259	0.0326	Basophils	DCs
CD6	32	0.2737	0.0086	CD4_T	CD8_Teff
COMPLEMENT	66	0.2606	0.0039	Stroma	Basophils
GRN	30	0.2399	0.0080	Basophils	Monocytes

Step 6c — CNS-Enriched Pathways

cns_prob <- cns_net %>%
  group_by(pathway_name) %>%
  summarise(cns_prob = sum(prob), .groups = "drop")

bm_prob <- bm_net %>%
  group_by(pathway_name) %>%
  summarise(bm_prob = sum(prob), .groups = "drop")

pathway_compare <- full_join(cns_prob, bm_prob, by = "pathway_name") %>%
  mutate(
    cns_prob = replace(cns_prob, is.na(cns_prob), 0),
    bm_prob  = replace(bm_prob, is.na(bm_prob), 0),
    log2_fc  = log2((cns_prob + 0.001) / (bm_prob + 0.001)),
    location = case_when(
      cns_prob > 0 & bm_prob == 0 ~ "CNS-only",
      cns_prob == 0 & bm_prob > 0 ~ "BM-only",
      TRUE ~ "Both"
    )
  ) %>%
  arrange(desc(log2_fc))

cat("CNS-only pathways:\n")

## CNS-only pathways:

pathway_compare %>% filter(location == "CNS-only") %>%
  select(pathway_name, cns_prob) %>%
  mutate(cns_prob = round(cns_prob, 4)) %>%
  arrange(desc(cns_prob)) %>%
  kable()

pathway_name	cns_prob
MK	2.5696
FN1	2.4452
PTN	1.7658
THBS	1.5101
JAM	0.8995
GAS	0.1618
CD86	0.1248
CX3C	0.0953
DHEAS	0.0900
TNF	0.0600
CD80	0.0572
BST2	0.0479
Glutamate	0.0398
CD200	0.0391
FLRT	0.0229
ncWNT	0.0191
CHEMERIN	0.0158
CLDN	0.0129
ADGRA	0.0104
EGF	0.0104
Histamine	0.0097
BMP	0.0093
SEMA3	0.0087
PLAU	0.0084
PTPRM	0.0070
CDH5	0.0069
LXA4	0.0067
PCDH	0.0061
CADM	0.0054
EPHA	0.0052
SLIT	0.0052
RANKL	0.0048
HSPG	0.0047
Netrin	0.0041
SerotoninDopamin	0.0036
TWEAK	0.0034
ANGPT	0.0029
EPHB	0.0020
PDGF	0.0018
CD137	0.0011
WNT	0.0011

cat("\n\nTop CNS-enriched pathways (present in both):\n")

## 
## 
## Top CNS-enriched pathways (present in both):

pathway_compare %>% filter(location == "Both") %>%
  select(pathway_name, cns_prob, bm_prob, log2_fc) %>%
  mutate(across(where(is.numeric), ~round(., 3))) %>%
  head(20) %>%
  kable()

pathway_name	cns_prob	bm_prob	log2_fc
GAP	0.049	0.002	4.097
ADGRL	0.029	0.002	3.562
KLK	0.147	0.018	2.958
CD39	0.248	0.036	2.735
PROS	0.173	0.025	2.728
COLLAGEN	6.144	0.985	2.639
DHT	0.012	0.002	2.366
PARs	0.498	0.132	1.902
LAMININ	1.378	0.454	1.598
Testosterone	0.044	0.016	1.420
LIGHT	0.031	0.011	1.406
PTPR	0.028	0.010	1.394
CypA	4.341	1.736	1.321
LAIR1	0.831	0.334	1.313
IL4	0.069	0.028	1.271
CysLTs	1.069	0.448	1.253
ANGPTL	0.252	0.111	1.184
ICAM	2.921	1.332	1.132
APRIL	0.023	0.010	1.119
CDH	0.115	0.054	1.074

cat("\n\nTop BM-enriched pathways:\n")

## 
## 
## Top BM-enriched pathways:

pathway_compare %>% filter(location == "Both") %>%
  arrange(log2_fc) %>%
  select(pathway_name, cns_prob, bm_prob, log2_fc) %>%
  mutate(across(where(is.numeric), ~round(., 3))) %>%
  head(20) %>%
  kable()

pathway_name	cns_prob	bm_prob	log2_fc
VCAM	0.457	3.979	-3.120
Desmosterol	0.004	0.023	-2.194
SELL	0.131	0.603	-2.191
CTSG	0.014	0.063	-2.055
SPP1	0.205	0.767	-1.899
NKG2D	0.011	0.040	-1.761
CLEC	1.297	3.253	-1.326
IL1	0.067	0.154	-1.192
IGF	0.195	0.438	-1.168
PECAM1	0.800	1.755	-1.133
DHEA	0.036	0.068	-0.914
IFN-II	0.102	0.186	-0.859
VISFATIN	0.014	0.025	-0.855
SN	0.068	0.115	-0.758
CD40	0.087	0.137	-0.660
CD6	0.174	0.274	-0.647
ADGRG	0.067	0.105	-0.639
PECAM2	0.408	0.624	-0.613
NRXN	0.005	0.009	-0.581
IL6	0.020	0.031	-0.574

Step 7 — Communication Patterns

Step 7a — Outgoing (CNS)

tryCatch(selectK(cellchat_cns, pattern = "outgoing"),
         error = function(e) cat("selectK failed:", e$message, "\n"))

n_out <- 4  # ADJUST based on selectK elbow plot

tryCatch({
  cellchat_cns <- identifyCommunicationPatterns(cellchat_cns,
                                                 pattern = "outgoing", k = n_out)
  if (requireNamespace("ggalluvial", quietly = TRUE)) {
    print(netAnalysis_river(cellchat_cns, pattern = "outgoing", font.size = 3))
  }
  print(netAnalysis_dot(cellchat_cns, pattern = "outgoing"))
}, error = function(e) cat("Outgoing patterns failed:", e$message, "\n"))

## Outgoing patterns failed: Can't find stat called "stratum".

Step 7b — Incoming (CNS)

tryCatch(selectK(cellchat_cns, pattern = "incoming"),
         error = function(e) cat("selectK failed:", e$message, "\n"))

n_in <- 4  # ADJUST based on selectK elbow plot

tryCatch({
  cellchat_cns <- identifyCommunicationPatterns(cellchat_cns,
                                                 pattern = "incoming", k = n_in)
  if (requireNamespace("ggalluvial", quietly = TRUE)) {
    print(netAnalysis_river(cellchat_cns, pattern = "incoming", font.size = 3))
  }
  print(netAnalysis_dot(cellchat_cns, pattern = "incoming"))
}, error = function(e) cat("Incoming patterns failed:", e$message, "\n"))

## Incoming patterns failed: Can't find stat called "stratum".

Step 8 — Immunosuppressive Summary

A consolidated view of all immunosuppressive interactions in the CNS.

Step 8a — Immunosuppressive Interaction Inventory

# Define immunosuppressive pathways/interactions
immunosup_pathways <- c("GALECTIN", "TGFb", "IL10", "ApoE", "APP", "Prostaglandin")

# Also capture specific checkpoint-like interactions
immunosup_lr <- c("LGALS9", "HAVCR2", "TGFB1", "IL10", "PDCD1", "CD274",
                   "CTLA4", "TIGIT", "LAG3")

immunosup_int <- cns_net %>%
  filter(
    pathway_name %in% immunosup_pathways |
    grepl(paste(immunosup_lr, collapse = "|"), interaction_name, ignore.case = TRUE)
  ) %>%
  arrange(desc(prob))

cat("Total immunosuppressive interactions in CNS:", nrow(immunosup_int), "\n\n")

## Total immunosuppressive interactions in CNS: 993

# Summarise by sender
immunosup_int %>%
  group_by(source) %>%
  summarise(
    n_targets = n_distinct(target),
    n_interactions = n(),
    total_prob = round(sum(prob), 3),
    key_pathways = paste(unique(pathway_name), collapse = ", "),
    .groups = "drop"
  ) %>%
  arrange(desc(total_prob)) %>%
  kable(caption = "Immunosuppressive signal senders — CNS")

Immunosuppressive signal senders — CNS

source	n_targets	n_interactions	total_prob	key_pathways
B-ALL	19	63	3.225	GALECTIN, Prostaglandin, TGFb
Stroma	20	106	2.655	APP, ApoE, GALECTIN, Prostaglandin, TGFb, CD80
Microglia-like	20	96	2.625	APP, GALECTIN, TGFb, ApoE, CD86, Prostaglandin, ICOS
Macrophages	20	76	2.015	ApoE, APP, GALECTIN, TGFb, Prostaglandin
Neutrophils	20	73	1.529	APP, GALECTIN, TGFb, Prostaglandin
DCs	20	73	1.393	APP, TGFb, GALECTIN, Prostaglandin, ICOS
Monocytes	19	60	1.311	ApoE, APP, TGFb, Prostaglandin, PD-L1
Basophils	20	68	1.197	GALECTIN, TGFb, ApoE, Prostaglandin, PDL2, PD-L1
CD8_Teff	18	39	0.718	GALECTIN, Prostaglandin, TGFb
CD8_Tpex	18	46	0.683	GALECTIN, Prostaglandin, TGFb, CD80
Other_gd	18	39	0.672	GALECTIN, Prostaglandin, TGFb
Pre-B	15	31	0.331	ApoE, GALECTIN, TGFb, Prostaglandin
Other	16	34	0.248	TGFb, GALECTIN, Prostaglandin
CD8_Tex	14	29	0.228	GALECTIN, Prostaglandin, TGFb
NKT-like	14	29	0.225	GALECTIN, Prostaglandin, TGFb
CD4_T	14	29	0.222	GALECTIN, Prostaglandin, TGFb
Vg4	14	29	0.200	GALECTIN, Prostaglandin, TGFb
Vg6Vd4	14	28	0.182	Prostaglandin, GALECTIN, TGFb
CD8_Naive	14	24	0.140	GALECTIN, Prostaglandin
Plasma_B	15	21	0.072	Prostaglandin, PD-L1, TGFb

Step 8b — Immunosuppressive Receivers

immunosup_int %>%
  group_by(target) %>%
  summarise(
    n_senders = n_distinct(source),
    n_interactions = n(),
    total_prob = round(sum(prob), 3),
    key_pathways = paste(unique(pathway_name), collapse = ", "),
    .groups = "drop"
  ) %>%
  arrange(desc(total_prob)) %>%
  kable(caption = "Immunosuppressive signal receivers — CNS")

Immunosuppressive signal receivers — CNS

target	n_senders	n_interactions	total_prob	key_pathways
Microglia-like	20	76	2.575	ApoE, APP, TGFb, GALECTIN
Pre-B	19	32	2.207	APP, GALECTIN
Monocytes	20	93	1.872	GALECTIN, APP, TGFb, Prostaglandin, CD80
DCs	20	65	1.855	APP, GALECTIN, Prostaglandin
Other	20	76	1.611	APP, GALECTIN, TGFb, Prostaglandin
Macrophages	8	18	0.971	ApoE, APP
CD8_Tpex	20	59	0.967	GALECTIN, APP, PD-L1, Prostaglandin, PDL2
Basophils	20	96	0.941	GALECTIN, Prostaglandin, APP, TGFb, CD80
Neutrophils	20	76	0.906	GALECTIN, APP, TGFb, Prostaglandin
CD8_Teff	20	64	0.786	GALECTIN, APP, PD-L1, Prostaglandin, PDL2, CD86, CD80, ICOS
Plasma_B	11	23	0.764	APP, GALECTIN, CD86, CD80, ICOS, TGFb
NKT-like	20	45	0.701	GALECTIN, APP, PD-L1, PDL2
CD8_Naive	18	28	0.641	GALECTIN, Prostaglandin, CD86, CD80, ICOS
Stroma	20	88	0.641	GALECTIN, APP, TGFb, Prostaglandin
CD8_Tex	20	30	0.490	GALECTIN, APP, PD-L1, PDL2
Vg6Vd4	12	21	0.465	GALECTIN, APP, PD-L1, PDL2
CD4_T	20	38	0.464	GALECTIN, APP, CD86, PD-L1, CD80, ICOS, PDL2
Vg4	12	19	0.427	GALECTIN, APP, PD-L1, PDL2
Other_gd	13	29	0.382	GALECTIN, APP, TGFb, PD-L1, PDL2
B-ALL	9	17	0.205	GALECTIN, APP, TGFb

Step 8c — Top Immunosuppressive Interactions

immunosup_int %>%
  select(source, target, interaction_name, pathway_name, prob, pval) %>%
  head(40) %>%
  kable(caption = "Top 40 immunosuppressive interactions (CNS)")

Top 40 immunosuppressive interactions (CNS)

source	target	interaction_name	pathway_name	prob	pval
Stroma	DCs	APP_CD74	APP	0.2961797	0.00
Stroma	Pre-B	APP_CD74	APP	0.2893632	0.00
Macrophages	Microglia-like	APOE_TREM2_TYROBP	ApoE	0.2692462	0.00
Monocytes	Microglia-like	APOE_TREM2_TYROBP	ApoE	0.2058687	0.00
Stroma	Plasma_B	APP_CD74	APP	0.2013547	0.00
Macrophages	Macrophages	APOE_TREM2_TYROBP	ApoE	0.1917321	0.00
Stroma	Microglia-like	APP_TREM2_TYROBP	APP	0.1883189	0.00
DCs	DCs	APP_CD74	APP	0.1527674	0.00
Stroma	Other	APP_CD74	APP	0.1497289	0.00
DCs	Pre-B	APP_CD74	APP	0.1485548	0.00
Stroma	Microglia-like	APOE_TREM2_TYROBP	ApoE	0.1462329	0.00
Monocytes	Macrophages	APOE_TREM2_TYROBP	ApoE	0.1430293	0.00
B-ALL	Monocytes	LGALS9_CD45	GALECTIN	0.1424436	0.00
B-ALL	CD8_Tpex	LGALS9_CD45	GALECTIN	0.1370330	0.00
B-ALL	CD8_Teff	LGALS9_CD45	GALECTIN	0.1357315	0.00
Neutrophils	DCs	APP_CD74	APP	0.1354687	0.00
B-ALL	CD8_Tex	LGALS9_CD45	GALECTIN	0.1353338	0.00
Microglia-like	DCs	APP_CD74	APP	0.1327085	0.00
Neutrophils	Pre-B	APP_CD74	APP	0.1316590	0.00
Stroma	Macrophages	APP_TREM2_TYROBP	APP	0.1299598	0.00
Microglia-like	Pre-B	APP_CD74	APP	0.1289648	0.00
B-ALL	CD4_T	LGALS9_CD45	GALECTIN	0.1281408	0.00
B-ALL	Other	LGALS9_CD45	GALECTIN	0.1278967	0.00
B-ALL	CD8_Naive	LGALS9_CD45	GALECTIN	0.1278645	0.00
B-ALL	Pre-B	LGALS9_IGHM	GALECTIN	0.1256823	0.00
Macrophages	DCs	APP_CD74	APP	0.1234323	0.00
Monocytes	DCs	APP_CD74	APP	0.1200592	0.00
Macrophages	Pre-B	APP_CD74	APP	0.1199142	0.00
B-ALL	NKT-like	LGALS9_CD45	GALECTIN	0.1192309	0.00
Monocytes	Pre-B	APP_CD74	APP	0.1166244	0.00
B-ALL	DCs	LGALS9_CD45	GALECTIN	0.1158341	0.00
Microglia-like	Monocytes	LGALS9_CD45	GALECTIN	0.1055026	0.00
B-ALL	Vg4	LGALS9_CD45	GALECTIN	0.1048894	0.00
B-ALL	Other_gd	LGALS9_CD45	GALECTIN	0.1021184	0.00
B-ALL	Pre-B	LGALS9_CD45	GALECTIN	0.1016737	0.00
Microglia-like	CD8_Tpex	LGALS9_CD45	GALECTIN	0.1013294	0.01
B-ALL	Vg6Vd4	LGALS9_CD45	GALECTIN	0.1004244	0.00
Microglia-like	CD8_Teff	LGALS9_CD45	GALECTIN	0.1003276	0.00
Microglia-like	CD8_Tex	LGALS9_CD45	GALECTIN	0.1000216	0.00
Basophils	Monocytes	LGALS9_CD45	GALECTIN	0.0996365	0.01

Session Information

sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Rocky Linux 9.5 (Blue Onyx)
## 
## Matrix products: default
## BLAS/LAPACK: /opt/intel/oneapi/mkl/2024.0/lib/libmkl_gf_lp64.so.2;  LAPACK version 3.10.1
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/London
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] parallel  grid      stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] doParallel_1.0.17     iterators_1.0.14      foreach_1.5.2        
##  [4] NMF_0.28              cluster_2.1.6         rngtools_1.5.2       
##  [7] registry_0.5-1        knitr_1.51            ComplexHeatmap_2.22.0
## [10] patchwork_1.3.2       tidyr_1.3.1           qs2_0.1.7            
## [13] Seurat_5.4.0          SeuratObject_5.3.0    sp_2.1-4             
## [16] CellChat_2.2.0.9001   Biobase_2.66.0        BiocGenerics_0.52.0  
## [19] ggplot2_4.0.2         igraph_2.2.2          dplyr_1.1.4          
## 
## loaded via a namespace (and not attached):
##   [1] RcppAnnoy_0.0.23       splines_4.4.1          later_1.4.6           
##   [4] tibble_3.3.1           polyclip_1.10-7        ggnetwork_0.5.14      
##   [7] fastDummies_1.7.5      lifecycle_1.0.5        rstatix_0.7.3         
##  [10] globals_0.16.3         lattice_0.22-6         MASS_7.3-60.2         
##  [13] backports_1.4.1        magrittr_2.0.3         plotly_4.12.0         
##  [16] sass_0.4.9             rmarkdown_2.30         jquerylib_0.1.4       
##  [19] yaml_2.3.12            httpuv_1.6.16          otel_0.2.0            
##  [22] collapse_2.1.6         sctransform_0.4.3      spam_2.11-3           
##  [25] spatstat.sparse_3.1-0  reticulate_1.45.0      cowplot_1.2.0         
##  [28] pbapply_1.7-4          RColorBrewer_1.1-3     abind_1.4-5           
##  [31] Rtsne_0.17             purrr_1.0.2            circlize_0.4.17       
##  [34] IRanges_2.40.1         S4Vectors_0.44.0       ggrepel_0.9.6         
##  [37] irlba_2.3.7            listenv_0.9.1          spatstat.utils_3.2-1  
##  [40] goftest_1.2-3          RSpectra_0.16-2        spatstat.random_3.4-4 
##  [43] fitdistrplus_1.2-6     parallelly_1.37.1      svglite_2.2.2         
##  [46] codetools_0.2-20       tidyselect_1.2.1       shape_1.4.6.1         
##  [49] farver_2.1.2           matrixStats_1.5.0      stats4_4.4.1          
##  [52] spatstat.explore_3.7-0 jsonlite_2.0.0         GetoptLong_1.1.0      
##  [55] BiocNeighbors_2.0.1    progressr_0.18.0       Formula_1.2-5         
##  [58] ggridges_0.5.6         ggalluvial_0.12.6      survival_3.6-4        
##  [61] systemfonts_1.3.1      tools_4.4.1            sna_2.8               
##  [64] ica_1.0-3              Rcpp_1.0.12            glue_1.8.0            
##  [67] gridExtra_2.3          xfun_0.56              withr_3.0.2           
##  [70] BiocManager_1.30.27    fastmap_1.2.0          fansi_1.0.6           
##  [73] digest_0.6.35          R6_2.6.1               mime_0.12             
##  [76] textshaping_0.3.7      colorspace_2.1-0       Cairo_1.7-0           
##  [79] scattermore_1.2        tensor_1.5.1           dichromat_2.0-0.1     
##  [82] spatstat.data_3.1-9    utf8_1.2.4             generics_0.1.3        
##  [85] data.table_1.15.4      FNN_1.1.4.1            httr_1.4.7            
##  [88] htmlwidgets_1.6.4      uwot_0.2.4             pkgconfig_2.0.3       
##  [91] gtable_0.3.6           lmtest_0.9-40          S7_0.2.1              
##  [94] htmltools_0.5.8.1      carData_3.0-6          dotCall64_1.2         
##  [97] clue_0.3-67            scales_1.4.0           png_0.1-8             
## [100] spatstat.univar_3.1-6  rstudioapi_0.16.0      reshape2_1.4.4        
## [103] rjson_0.2.23           nlme_3.1-164           coda_0.19-4.1         
## [106] statnet.common_4.13.0  cachem_1.1.0           zoo_1.8-15            
## [109] GlobalOptions_0.1.3    stringr_1.5.1          KernSmooth_2.23-24    
## [112] miniUI_0.1.2           pillar_1.9.0           vctrs_0.6.5           
## [115] RANN_2.6.2             promises_1.5.0         ggpubr_0.6.2          
## [118] stringfish_0.18.0      car_3.1-5              xtable_1.8-4          
## [121] evaluate_1.0.5         cli_3.6.5              compiler_4.4.1        
## [124] rlang_1.1.7            crayon_1.5.2           future.apply_1.11.2   
## [127] ggsignif_0.6.4         labeling_0.4.3         plyr_1.8.9            
## [130] stringi_1.8.4          deldir_2.0-4           viridisLite_0.4.2     
## [133] network_1.20.0         gridBase_0.4-7         lazyeval_0.2.2        
## [136] spatstat.geom_3.7-0    Matrix_1.7-0           RcppHNSW_0.6.0        
## [139] future_1.33.2          shiny_1.12.1           ROCR_1.0-12           
## [142] broom_1.0.5            RcppParallel_5.1.8     bslib_0.7.0