SelectIntegrationFeatures: Select integration features
In ibseq/scs-analysis: Tools for Single Cell Genomics

Description Usage Arguments Details Value Examples

Choose the features to use when integrating multiple datasets. This function ranks features by the number of datasets they are deemed variable in, breaking ties by the median variable feature rank across datasets. It returns the top scoring features by this ranking.

SelectIntegrationFeatures(
  object.list,
  nfeatures = 2000,
  assay = NULL,
  verbose = TRUE,
  fvf.nfeatures = 2000,
  ...
)

`object.list`	List of seurat objects
`nfeatures`	Number of features to return
`assay`	Name or vector of assay names (one for each object) from which to pull the variable features.
`verbose`	Print messages
`fvf.nfeatures`	nfeatures for `FindVariableFeatures`. Used if `VariableFeatures` have not been set for any object in `object.list`.
`...`	Additional parameters to `FindVariableFeatures`

If for any assay in the list, FindVariableFeatures hasn't been run, this method will try to run it using the fvf.nfeatures parameter and any additional ones specified through the ....

A vector of selected features

## Not run: 
# to install the SeuratData package see https://github.com/satijalab/seurat-data
library(SeuratData)
data("panc8")

# panc8 is a merged Seurat object containing 8 separate pancreas datasets
# split the object by dataset and take the first 2
pancreas.list <- SplitObject(panc8, split.by = "tech")[1:2]

# perform SCTransform normalization
pancreas.list <- lapply(X = pancreas.list, FUN = SCTransform)

# select integration features
features <- SelectIntegrationFeatures(pancreas.list)

## End(Not run)