clusteringSeurat: Seurat clustering with log or SCT normalization
In fpestana-git/clusteringR: Transcriptomic clustering functions

View source: R/clusteringSeurat.R

clusteringSeurat

R Documentation

Seurat clustering with log or SCT normalization

Description

Function that takes a dataset (either spatial or scRNA-seq), creates Seurat object, runs log normalization, PCA, etc, clustering

Usage

clusteringSeurat(
  datasetObject,
  datasetName,
  metadataAvailable = TRUE,
  normalizationMethod = "LOG",
  mapTypeValue = "umap",
  metadataObject,
  reductionValue = "umap",
  resolutionValue = 0.1,
  pcaValueOptimal = NULL,
  integrateValue = FALSE,
  specificPCA = FALSE,
  ngenes = 200,
  scaleFactor = 10000,
  nPCAS = 30,
  selectFeatures = NULL
)

Arguments

`datasetObject`	dataset object
`datasetName`	name of the dataset
`metadataAvailable`	whether there is prior metadata available (default TRUE)
`normalizationMethod`	which normalization method to use (default LOG). options log normalization or SCTransform normalization (SCT)
`metadataObject`	metadata object
`reductionValue`	which dimensionality reduction to use, umap or tsne (default umap)
`resolutionValue`	define the resolution value to use for clustering (default 0.1)
`pcaValueOptimal`	define a specific PCA value to use (default NULL calculates automatically the optimal PCA value)
`integrateValue`	whether dataset integration is needed (default FALSE)
`specificPCA`	whether PCA values needs to be calculated or not
`ngenes`	minimum number of features expressed in a cell (default 200)
`scaleFactor`	value for scaling (default 10000)
`nPCAS`	number of principal components to use (default 30)
`selectFeatures`	whether features need to be selected. Takes all features as default (default NULL)