# 5. Sankey plotting of inter- and intracellular communication

**Language:** R · **Input:** `LR_data_final.Rds`

In this step, we will visualise the inter- and intracellular communication events that
CrossTalkeR2 identified. Starting from the final CrossTalkeR object, we draw the
ligand–receptor Sankey for the Il1b → Il1r1 axis, the intracellular signalling graph
downstream of Il1r1 in MSCs, and a ranking of the strongest differential cell–cell
interactions.

## Load the CrossTalkeR object

We start by loading the required libraries and the final CrossTalkeR
object produced in the previous step.

```r
library(CrossTalkeR)
library(dplyr)

BASE    <- "/path/to/Il1rn_KO"
CTR_OUT <- file.path(BASE, "crosstalker")

CTR_data <- readRDS(file.path(CTR_OUT, "LR_data_final.Rds"))
```

## Top 20 cell-cell interactions by edge weight

Before we dive into the inter- and intracellular signals involving MSC's, we can make sure that MSC's are a central and important signaling node in the network. One way is to check the interactions edge weights in the network. We can print the top 20 cell-cell interactions on the absolute LR-Score as barplot:

```r
edges <- igraph::as_data_frame(CTR_data@graphs[["KO_x_WT"]])
edges$edges  <- paste(edges$from, edges$to, sep = "/")
edges$signal <- ifelse(edges$weight > 0, "pos",
                ifelse(edges$weight < 0, "neg", "none"))

top <- edges %>% filter(signal %in% c("pos", "neg")) %>%
       group_by(signal) %>% slice_max(abs(weight), n = 10) %>% ungroup()

ggplot(top, aes(x = weight, y = reorder(edges, weight), fill = signal)) +
  geom_bar(stat = "identity") +
  ylab("Node Pairs") + xlab("Edge Weight") +
  scale_fill_manual(values = c("#3B4CC0", "#DD0029")) +
  theme_minimal()
```

:::{figure} images/step5_top20cci_barplot.png
:alt: Intracellular signalling graph of Il1r1 in MSCs
:width: 95%

Top 20 positive and negative cell-cell interactions.
:::

## Sankey of Il1b → Il1r1_Il1rap towards MSCs

Now that we have seen that MSC's are within the top interacting cell types, we take a look at the incoming signals to Il1r1_Il1rap in MSC's. The best was is to plot a Sankey plot of the Ligand Receptor interactions:  

```r
table <- CTR_data@tables$KO_x_WT
table <- table[table$type_gene_A == "Ligand" &
               table$gene_A %in% c("Il1b|L") &
               table$type_gene_B == "Receptor" &
               table$gene_B %in% c("Il1r1_Il1rap|R", "Il1r2_Il1rap|R", "Il1r1|R"), ]
table <- table %>% arrange(gene_B)

plot_sankey(table,
  target    = "Il1r1_Il1rap|R",
  plt_name  = "Il1r1_Il1rap|R",
  threshold = 50,
  low_col   = "#3B4CC0",
  high_col  = "#DD0029",
  score_col = "LRScore")
```

:::{figure} images/step5_sankey_Il1b_Il1r1_Il1rap.png
:alt: Sankey of Il1b to Il1r1_Il1rap towards MSCs
:width: 95%

Il1b → Il1r1_Il1rap interactions towards MSCs (KO vs WT).
:::

We can see that the Il1b–Il1r1 interactions are upregulated in KO, and the incoming signals are coming from arteriolar endothelial cells
and G4 neutrophils. The signaling is exclusively targeting MSCs.


## Intracellular signalling graph of Il1r1 in MSCs

Next we can trace the intracellular downstream signaling of Il1r1_Il1rap in MSCs. To plot this signaling, we first filter R-TF and TF-L interactions
to only include fully connected intracellular signaling (full R-TF-L path). Then we calculate the mean LR Score for incoming signals to Il1r1_Il1rap
and for each outgoing downstream L signal.

```r
table <- CTR_data@tables$KO_x_WT
gene_list1 <- table[table$gene_A == "Il1r1_Il1rap|R" & table$source == "MSCs", ]
gene_list2 <- table[table$gene_A %in% gene_list1$gene_B & table$source == "MSCs", ]

pagerank_table <- as.data.frame(CTR_data@rankings$KO_x_WT_ggi %>% select(nodes, Pagerank))
rownames(pagerank_table) <- pagerank_table$nodes

# InterScore = mean LRScore over all intercellular interactions using that R (or L)
LR_receptors <- table[table$gene_B %in% gene_list1$gene_A & table$target == "MSCs", ]
LR_ligands   <- table[table$gene_A %in% gene_list2$gene_B & table$source == "MSCs", ]

rec_scores <- sapply(unique(LR_receptors$gene_B),
                     \(r) mean(LR_receptors[LR_receptors$gene_B == r, ]$LRScore))
lig_scores <- sapply(unique(LR_ligands$gene_A),
                     \(l) mean(LR_ligands[LR_ligands$gene_A == l, ]$LRScore))

gene_list1 <- gene_list1[gene_list1$gene_A %in% names(rec_scores), ]
gene_list1$InterScore <- rec_scores[gene_list1$gene_A]
gene_list2$InterScore <- lig_scores[gene_list2$gene_B]

# node size: mean LRScore per TF across both hops
tf_scores <- rbind(rename(gene_list1[, c("gene_B", "LRScore")], gene = gene_B),
                   rename(gene_list2[, c("gene_A", "LRScore")], gene = gene_A))
gene_avg  <- rename(aggregate(LRScore ~ gene, tf_scores, mean, na.rm = TRUE),
                    AvgTFScore = LRScore)
gene_list1 <- merge(gene_list1, gene_avg, by.x = "gene_B", by.y = "gene", all.x = TRUE)
gene_list2 <- merge(gene_list2, gene_avg, by.x = "gene_A", by.y = "gene", all.x = TRUE)

top20_tf2L <- gene_list2[order(abs(gene_list2$InterScore), decreasing = TRUE), ][1:20, ]

plot_graph_sankey_tf_custom(
  gene_list1, 
  top20_tf2L, 
  "MSCs", 
  "InterScore", 
  pagerank_table,
  outpath     = PLOTS,
  file_name   = "Intra_Sankey_Il1r1_MSC_Top20",
  legend_name = "mean(LRScore of R or L)",
  min_max_val = 1,
  title       = "Il1r1 MSCs mean(LRScore) Top 20 ligands",
  size_node   = "fixed_size",
  colors      = c("#3B4CC0", "#D1EAFA", "#e3e3e3", "#F9AC9F", "#DD0029"))
```

:::{figure} images/step5_il1r1_downstream_signaling.png
:alt: Intracellular signalling graph of Il1r1 in MSCs
:width: 95%

Intracellular signalling downstream of Il1r1 in MSCs (top 20 ligands).
:::

**Nfkb1** appears as a mediator of Il1r1 signalling (the canonical NF-κB route),
alongside fibrosis-related **Smad3/Smad4**, which upregulate the **Tnc** ligand.


:::{admonition} Handoff: R → Python
:class: note

The analysis returns to Python. Three sets of files cross:

| File | From | Needed by |
|------|------|-----------|
| `edges_directed_KO_WT_filtered.csv` | [step 4](04_intratalker_crosstalker.md) | [step 6](06_communities.md) |
| `CrossTalkeR_input_{KO,WT}.csv` | [step 4](04_intratalker_crosstalker.md) | [step 7](07_receptome.md) |
| `intracellular_network_{KO,WT}.csv` | [step 4](04_intratalker_crosstalker.md), exported in [step 7](07_receptome.md) | [step 7](07_receptome.md) |

:::

**Next:** [Communication communities](06_communities.md)