Single-cell RNA-seq

Please find the individual cellxgene matrices attached.

For integration and UMAP we used Seurat V2 with these functions:

For each matrix:

CreateSeuratObject(OBJ, min.cells = 3, min.features = 200, project = NamesProject[i])

#filtered
subset(OBJ, subset = nCount_RNA > 10000 & nFeature_RNA > 1650 & nCount_RNA < 200000 & nFeature_RNA < 12000)

 

#All replicates were included in a list called list.data

for (i in 1:length(list.data)) {
  list.data[[i]] = NormalizeData(object = list.data[[i]])
  list.data[[i]] = FindVariableFeatures(list.data[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}
 

ctr.anchors <- FindIntegrationAnchors(object.list = list.data, dims = 1:100,anchor.features = 2000)

ctr.integrated <- IntegrateData(anchorset = ctr.anchors, dims = 1:100)
library(ggplot2)
library(cowplot)
# switch to integrated assay. The variable features of this assay are automatically
# set during IntegrateData
DefaultAssay(ctr.integrated) <- "integrated"

# Run the standard workflow for visualization and clustering
ctr914224.integrated <- ScaleData(ctr.integrated, verbose = FALSE)
#moi npcs 100
ctr914224.integrated <- RunPCA(ctr914224.integrated, npcs = 30, verbose = FALSE)
#moi dims 100
ctr914224.integrated2 = RunUMAP(object = ctr914224.integrated, dims = 1:30)

DimPlot(ctr914224.integrated2, reduction = "umap", label = T)
#find clusters
ctr4914224VST.integratedbeforeneighbours = FindNeighbors(object = ctr914224.integrated2,  dims = 1:30, reduction = "pca", assay = "integrated", verbose = T, force.recalc = T)
ctr4914224VST.integrated0_8 = FindClusters(object = ctr4914224VST.integratedbeforeneighbours, resolution = 0.8, verbose = T)
DimPlot(ctr4914224VST.integrated0_8, reduction = "umap", label = T)