SCTransform_normalization: SCTransform based scRNAseq Workflow This function accepts UMI...

In tolgaturan-github/IBRIDGE: IBRIDGE integrates bulk and single-cell datasets to profile granular heterogeneity of TIME, acting as a 'bridge' between data types.

SCTransform based scRNAseq Workflow This function accepts UMI count matrix and patient metadata as arguments and replaces NormalizeData(), ScaleData(), and FindVariableFeatures() functions from the Seurat package The function also performs UMAP dimensio reduction and clustering

Description

SCTransform based scRNAseq Workflow This function accepts UMI count matrix and patient metadata as arguments and replaces NormalizeData(), ScaleData(), and FindVariableFeatures() functions from the Seurat package The function also performs UMAP dimensio reduction and clustering

Usage

SCTransform_normalization(countmatrix, metadata)

Arguments

countmatrix	Numeric matrix of UMI counts genes as rows and cell_ids as columns from malignant subset of the scRNAseq data This matrix is usually a subset of a scRNAseq dataset profiling the whole TIME.
metadata	Character vector (1-dimension) of length matching the cell count for the input countmatrix Preferably all or at least 1 feature in each geneset must be present in the rownames of the expression matrix.

Value

A seurat object with SCTransform normalized counts and PErson residuals as well as patient metadata and dimension reduction slots

Author(s)

Tolga Turan, [e-mail]

References

http://github.com/tolgaturan-github/IBRIDGE

Examples

seurat_object1<-SCTransform_normalization(countmatrix1, patient_metadata1)

tolgaturan-github/IBRIDGE documentation built on July 30, 2023, 12:08 p.m.