For single cell analysis, seuratobojects were created for 6 samples and integration was performed after going through basic procedures. However, we confirmed that the clusters were not clearly divided. Can you tell me the cause and solution for this? The overall process followed the Seurat V5 process. However, even when following the Harmony or V4 process, there was no clear division. In the figure, the first figure is before integration, and the second and third figures are after integration.

Seurat <- merge(AS1, c(AS2, AS3, CO1, CO2, CO3))
Seurat <- NormalizeData(object = Seurat)
Seurat <- FindVariableFeatures(object = Seurat)
Seurat <- ScaleData(object = Seurat)
Seurat <- RunPCA(object = Seurat)
Seurat <- FindNeighbors(object = Seurat, dims = 1:15)
Seurat <- FindClusters(object = Seurat, resolution = 0.2)
Seurat <- RunUMAP(object = Seurat, dims = 1:15)
S1 <- DimPlot(Seurat, reduction = "umap", label = TRUE, group.by = "orig.ident")
Seurat_I <- IntegrateLayers(object = Seurat, method = CCAIntegration, orig.reduction = "pca", new.reduction = "integrated.cca",
                           verbose = FALSE)
Seurat_I[["RNA"]] <- JoinLayers(Seurat_I[["RNA"]])
Seurat_I <- FindNeighbors(Seurat_I, reduction = "integrated.cca", dims = 1:15)
Seurat_I <- FindClusters(Seurat_I, resolution = 0.3)
Seurat_I <- RunUMAP(Seurat_I, dims = 1:15, reduction = "integrated.cca")
S2 <- DimPlot(Seurat_I, reduction = "umap", group.by = "orig.ident")
S3 <- DimPlot(Seurat_I, reduction = "umap", label=T)
S1+S2+S3