Preprocess: Preprocess
In mheffe3/SCNVC: Generates Single Cell Networks that can be used in Cytoscape

Description Usage Arguments Value Author(s) Examples

View source: R/SCRNA.R

Takes in a dataframe containing a scppin and calls Seurat functions on it to get it in the proper format to use scPPIN functions with it.

1	Preprocess(Data)

Data

A dataframe containing scppin data

returns a Seurat object with Seurat functions called on it

Matt Heffernan, University of Illinois at Chicago

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

Preprocess(uterusData)

## The function is currently defined as
function (Data) 
{
    SData <- CreateSeuratObject(counts = Data, min.cells = 3, 
        min.features = 300, names.delim = "\.")
    SData[["percent.mito"]] <- PercentageFeatureSet(object = SData, 
        pattern = "^MT-")
    SData <- subset(SData, subset = nFeature_RNA > 300 & nFeature_RNA < 
        2500 & percent.mito < 20)
    SData <- NormalizeData(object = SData, normalization.method = "LogNormalize", 
        scale.factor = 10000)
    SData <- ScaleData(SData, vars.to.regress = c("nCount_RNA", 
        "percent.mito"))
    SData <- FindVariableFeatures(SData, selection.method = "disp")
    length(SData@assays$RNA@var.features)
    var.gene <- SData@assays$RNA@var.features
    var.gene <- var.gene[!grepl(pattern = "*RPS", x = var.gene)]
    var.gene <- var.gene[!grepl(pattern = "*RPL", x = var.gene)]
    var.gene <- var.gene[!grepl(pattern = "*MT", x = var.gene)]
    length(var.gene)
    SData <- RunPCA(object = SData, features = var.gene, npcs = 50, 
        do.print = TRUE, pcs.print = 1:5, nfeatures.print = 5)
    SData <- FindNeighbors(object = SData, reduction = "pca", 
        dims = 1:15)
    SData <- FindClusters(object = SData, reduction = "pca", 
        dims = 1:15, resolution = c(0.6))
    return(SData)
  }