SDA_ser_preproc: Preprocess Seurat Object for Single-cell Data Analysis

In eisascience/scCustFx: single-cell custom fxs Package

Preprocess Seurat Object for Single-cell Data Analysis

Description

This function prepares a Seurat object for single-cell data analysis (SDA) by filtering features (genes) and cells based on various criteria such as minimum feature count, minimum number of cells expressing a feature, expression thresholds, inclusion and exclusion lists, and library size. It also checks and warns about the maximum number of cells the SDA can handle. The function utilizes the Seurat package for accessing assay data and performs initial quality control checks and data normalization.

Usage

SDA_ser_preproc(
  seuratObj,
  assayName = "RNA",
  Layer = "counts",
  minFeatureCount = 1,
  minCellsExpressingFeature = 0,
  perCellExpressionThreshold = 0,
  featureInclusionList = NULL,
  featureExclusionList = NULL,
  maxFeaturesDiscarded = 0.75,
  minLibrarySize = 0
)

Arguments

seuratObj	A Seurat object containing single-cell RNA sequencing data.
assayName	The name of the assay to use, default is "RNA".
Layer	The data layer to use, default is "counts".
minFeatureCount	Minimum count for a feature to be included, default is 1.
minCellsExpressingFeature	Minimum number of cells that must express a feature, default is 0.
perCellExpressionThreshold	Threshold for expression level per cell, default is 0.
featureInclusionList	A list of features to forcibly include, default is NULL.
featureExclusionList	A list of features to exclude, default is NULL.
maxFeaturesDiscarded	The maximum proportion (0-1) or absolute number of features to discard, default is 0.75.
minLibrarySize	The minimum library size required for a cell to be included, default is 0.

Value

A list containing the filtered assay data ('SerObj.DGE'), the list of features used ('featuresToUse'), and the minimum library size considered ('minLibrarySize').

eisascience/scCustFx documentation built on June 2, 2025, 3:59 a.m.