scMerge

scMerge integrates and normalizes single-cell RNA sequencing (scRNA-seq) datasets in R by identifying stably expressed genes (SEGs) to enable accurate data integration and downstream analysis.

Key Features:

• Identification of Stably Expressed Genes (SEGs): Ranks gene expression stability at the single-cell level to distinguish SEGs from traditionally defined housekeeping genes (HKGs).
• scSEGIndex computation: Computes the single-cell Stably Expressed Gene index (scSEGIndex) for given count data matrices to quantify gene stability.
• Cross-species reproducibility: Demonstrates reproducible and conserved scSEGIndex results across species using early human and mouse development datasets and the Mouse Atlas dataset.
• Enhanced stability assessment: Identifies SEGs that exhibit greater single-cell stability compared to HKGs and reflect characteristics associated with essential cellular functions.
• Data normalization and integration: Uses pre-computed genes or top SEG genes from user data as control genes via the ctl argument to normalize and integrate multiple scRNA-seq count matrices.

Scientific Applications:

• Single-cell transcriptome stability analysis: Enables analysis of variation and stability within single-cell transcriptomes using identified SEGs.
• scRNA-seq data normalization and integration: Provides control gene sets and stability indices to support normalization and accurate integration of diverse scRNA-seq datasets.

Methodology:

Evaluates gene expression stability at the single-cell level by ranking genes and computing the scSEGIndex on count data matrices, and applies pre-computed or top SEG genes as control genes via the ctl argument for normalization and dataset integration.