README.Rmd

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# ISCHIA

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ISCHIA is a framework for analysis of cell-types and Ligand-Receptor cooccurrences in spatial transcriptomics/proteomics data

## Installation

You can install the development version of ISCHIA from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("ati-lz/ISCHIA")
```

## Example

Here we present an example analysis of Visium IBD samples as presented in the manuscript

```{r dependencies, message=FALSE,warning=FALSE}

# Loading required packages
library(ISCHIA)
library(robustbase)
library(data.table)
library(ggplot2)
library(Seurat)
library(dplyr)

```


```{r preparation}

## You can download the example dataset from Zenodo repository,[10.5281/zenodo.7589581] IBD_Visium_SeuratObj.RData
# here we will subset the dataset to only one sample 
load(url("https://zenodo.org/record/7636442/files/IBD_visium_SeuratObj_small.RData?download=1"))
IBD.visium.P4

# Extracting the deconvoluted cell type probability matrix from metadata
deconv.mat <- as.matrix(IBD.visium.P4@meta.data[,9:28])
colnames(deconv.mat) <- sapply(colnames(deconv.mat), function(x) unlist(strsplit(x, split = "_"))[2])

head(deconv.mat)

```

## Celltype Composition clustering

```{r Composition_clustering}

# Deciding about the k
Composition.cluster.k(deconv.mat, 20)

# Composition clustering of the deconvoluted spatial spots
IBD.visium.P4 <- Composition.cluster(IBD.visium.P4, deconv.mat, 8)
#not good...Idents(IBD.visium.P4) <- "CompositionCluster_CC"

table(IBD.visium.P4$CompositionCluster_CC)

SpatialDimPlot(IBD.visium.P4, group.by = c("CompositionCluster_CC")) + scale_fill_manual(values = c("cyan", "orange", "purple","green","yellow","blue", "red","black"))

```
We can check the leading cell types of each CC

```{r Celltype_enrichment, message=FALSE, warning=FALSE}
Composition_cluster_enrichedCelltypes(IBD.visium.P4,"CC4", deconv.mat)
Composition_cluster_enrichedCelltypes(IBD.visium.P4,"CC7", deconv.mat)


```
We can explore the UMAP representation of the spot based on their deconvoluted cell type composition

```{r umap}
IBD.visium.P4.umap <- Composition_cluster_umap(IBD.visium.P4, deconv.mat)
IBD.visium.P4.umap$umap.cluster.gg
IBD.visium.P4.umap$umap.deconv.gg

```

## Celltype Cooccurrence analysis
We choose CC4 and CC7 to check the celltype cooccurrences

```{r celltype_cooccurrence}
CC4.celltype.cooccur <- spatial.celltype.cooccurence(spatial.object=IBD.visium.P4,
                                                      deconv.prob.mat=deconv.mat,
                                                      COI="CC4", prob.th= 0.05, Condition=unique(IBD.visium.P4$orig.ident))
plot.celltype.cooccurence(CC4.celltype.cooccur)


CC7.celltype.cooccur <- spatial.celltype.cooccurence(spatial.object=IBD.visium.P4,
                                                      deconv.prob.mat=deconv.mat,
                                                      COI="CC7", prob.th= 0.05, Condition=unique(IBD.visium.P4$orig.ident))
plot.celltype.cooccurence(CC7.celltype.cooccur)

```

##Cooccurrence of ligand and receptors
As the LR cooccurrence takes a lon time to run, for now Im just commenting out the command, will work on it later

```{r LR_cooccurrence}
lr_network = readRDS(url("https://zenodo.org/record/3260758/files/lr_network.rds"))
all.LR.network <- cbind(lr_network[,c("from","to")], LR_Pairs=paste(lr_network$from, lr_network$to, sep = "_"))
all.LR.network.exp <- all.LR.network[which(all.LR.network$from %in% rownames(IBD.visium.P4) & all.LR.network$to %in% rownames(IBD.visium.P4)),]

# To reduce the computation time for this example, we randomly sample from the whole dataset of LR interactions
all.LR.network.exp <- sample_n(all.LR.network.exp,500)
all.LR.genes <- unique(c(all.LR.network.exp$from, all.LR.network.exp$to))
all.LR.genes.comm <- intersect(all.LR.genes, rownames(IBD.visium.P4))
LR.pairs <- all.LR.network.exp$LR_Pairs
LR.pairs.AllCombos <- combn(all.LR.genes.comm, 2, paste0, collapse = "_")


#CC4.Enriched.LRs <- Enriched.LRs(IBD.visium.P4, c("CC4"), unique(IBD.visium.P4$orig.ident), all.LR.genes.comm, LR.pairs, 1, 0.2)

#CC7.Enriched.LRs <- Enriched.LRs(IBD.visium.P4, c("CC7"), unique(IBD.visium.P4$orig.ident), all.LR.genes.comm, LR.pairs, 1, 0.2)

#CC1vsCC4.Enriched.LRs.Specific <- Diff.cooc.LRs(CC4.Enriched.LRs, CC7.Enriched.LRs, 0.05, 0.1)

#ChordPlot.Enriched.LRs(CC4.Enriched.LRs$COI.enrcihed.LRs[1:20,])
#SankeyPlot.Diff.LRs(CC4.Enriched.LRs$COI.enrcihed.LRs, CC7.Enriched.LRs$COI.enrcihed.LRs)

```