ProcessSeurat1: Run the primary seurat processing steps.
In bimberlabinternal/OOSAP: OHSU-ONPRC Single-cell Analysis Package

This is the primary entry point for processing scRNAseq data with Seurat

ProcessSeurat1(
  seuratObj,
  saveFile = NULL,
  doCellCycle = T,
  doCellFilter = F,
  nCount_RNA.high = 20000,
  nFeature.high = 3000,
  pMito.high = 0.15,
  nCount_RNA.low = 0.99,
  nFeature.low = 200,
  pMito.low = -Inf,
  forceReCalc = F,
  variableGeneTable = NULL,
  variableFeatureSelectionMethod = "vst",
  nVariableFeatures = 2000,
  printDefaultPlots = T,
  npcs = 50,
  ccPcaResultFile = NULL,
  useSCTransform = F,
  mean.cutoff = c(0.0125, 3),
  dispersion.cutoff = c(0.5, Inf),
  spikeGenes = NULL,
  excludedGenes = NULL
)

`seuratObj,`	A Seurat object.
`saveFile`	If provided, the seuratObj will be saved here as it is processed, providing some ability to resume if there is a failure
`doCellCycle`	If true, CellCycle genes will be regressed
`doCellFilter`	If true, basic filtering will be performed using nCount_RNA, nFeature_RNA, and pMito
`nCount_RNA.high`	If doCellFilter=T, cells with nCount_RNA above this value will be filtered
`nFeature.high`	If doCellFilter=T, cells with nFeature above this value will be filtered
`pMito.high`	If doCellFilter=T, cells with percent mito above this value will be filtered
`nCount_RNA.low`	If doCellFilter=T, cells with nCount_RNA below this value will be filtered
`nFeature.low`	If doCellFilter=T, cells with nFeature below this value will be filtered
`pMito.low`	If doCellFilter=T, cells with percent mito below this value will be filtered
`forceReCalc`	If true, all steps will be repeated even if already marked as complete
`variableGeneTable`	If provided, a table of variable genes will be written to this file
`variableFeatureSelectionMethod`	The selection method to be passed to FindVariableFeatures()
`nVariableFeatures`	The number of variable features to find
`printDefaultPlots`	If true, the default set of QC plots will be printed
`npcs`	Number of PCs to use for RunPCA()
`ccPcaResultFile`	If provided, the PCA results from cell cycle regression will be written here
`useSCTransform`	If true, SCTransform will be used in place of the standard Seurat workflow (NormalizeData, ScaleData, FindVariableFeatures)
`mean.cutoff`	Passed directly to FindVariableFeatures
`dispersion.cutoff`	Passed directly to FindVariableFeatures
`spikeGenes`	If provided these will be appended to the set of VariableFeatures
`excludedGenes`	If provided these will be removed from the set of VariableFeatures