scqc: Single cell quality control and basic data pre-process.
In cailab-tamu/scTypeGSEA: Single Cell Type Gene Set Enrichment Analysis

Description Usage Arguments Value Examples

For single cell type data, it will create a Seurat object and perform single-cell data quality control, including checking for minimum cell library size, mitochondrial ratio, outlier cells, and the fraction of cells where a gene is expressed. And then the function will do basic data pre-process, which includes “Normalization”, “Scale data”, “Find HVG” and “PCA”. For other type of data, it will create a Seurat object to save the matrix.

scqc(
  obj,
  datatype = "RNA",
  metadata = NULL,
  min.cells = 10,
  min.features = 1000,
  percent.mt = 10,
  oversd = NULL,
  normalization.method = "LogNormalize",
  scale.factor = 10000,
  selection.method = "vst",
  nfeatures = 2000,
  npcs = 50
)

`obj`	A seurat object or a raw count gene expression.
`datatype`	Data type for your data, default is 'datatype = "RNA"', which is used for scRNAseq data.
`metadata`	Add metadata when creating Seurat object.
`min.cells`	An integer value. Include features detected in at least this many cells.
`min.features`	An integer value. Include cells where at least this many features are detected.
`percent.mt`	Define the highest percentage of reads that map to the mitochondrial genome.
`oversd`	Remove cells whose library size is greater than mean + oversd * sd. Default is null, which doesn't remove cells.
`normalization.method`	Method for normalization. Include 'LogNormalize', 'CLR' and 'RC'.
`scale.factor`	Sets the scale factor for cell-level normalization.
`selection.method`	How to choose top variable features. Include 'vst', 'mean.var.plot' and 'dispersion'.
`nfeatures`	An integer value. Define the number of features to select as top variable features.
`npcs`	An integer value. Define total Number of PCs to compute and store (50 by default).