integrated using seurat method.txt

### Part #1: Data Integration ###

## Data integration in EWS Cell Lines ##

# Get Ewing Sarcoma Cell Lines CHLA9 and CHLA10 
# From: https://www.mdpi.com/2072-6694/12/4/948
CHLA10_mat <- read.csv("CHLA10_matrix.csv.gz", row.names = 1)
CHLA9_mat <- read.csv("CHLA9_matrix.csv.gz", row.names = 1)
cells <- merge(x = CreateSeuratObject(CHLA9_mat, project = "CHLA9"),
                  y = CreateSeuratObject(CHLA10_mat, project = "CHLA10")) %>%
  PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
  subset(nFeature_RNA > 2500 & nCount_RNA > 12000 & percent.mt < 18) %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() %>%
  FindNeighbors() %>%
  FindClusters() %>%
  RunUMAP(dims = 1:50)

# Dim Plots
DimPlot(cells, group.by = "orig.ident") 
DimPlot(cells, group.by = "orig.ident") + DimPlot(cells, group.by = "seurat_clusters") 
# perform integration to correct for batch effects ------
obj.list <- SplitObject(merged_seurat_filtered, split.by = 'Patient')
for(i in 1:length(obj.list)){
  obj.list[[i]] <- NormalizeData(object = obj.list[[i]])
  obj.list[[i]] <- FindVariableFeatures(object = obj.list[[i]])
}


# select integration features
features <- SelectIntegrationFeatures(object.list = obj.list)

# find integration anchors (CCA)
anchors <- FindIntegrationAnchors(object.list = obj.list,
                       anchor.features = features)

# integrate data
seurat.integrated <- IntegrateData(anchorset = anchors)


# Scale data, run PCA and UMAP and visualize integrated data
seurat.integrated <- ScaleData(object = seurat.integrated)
seurat.integrated <- RunPCA(object = seurat.integrated)
seurat.integrated <- RunUMAP(object = seurat.integrated, dims = 1:50)