ROSeq

ROSeq models expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data by taking a filtered and normalized read count matrix and cell-annotation/condition as input to determine differentially expressed genes between contrasting groups of single cells.

Key Features:

• Rank-based expression modeling: Uses ranks rather than raw expression values to model gene expression, providing tolerance to noise in single-cell RNA sequencing data.
• Multi-core processing: Accepts the number of cores as an input parameter to enable parallel computation.

Scientific Applications:

• Differential expression analysis of scRNA-Seq data: Identifies differentially expressed genes between contrasting groups of single cells from filtered and normalized read count matrices.

Methodology:

ROSeq takes a filtered and normalized read count matrix and cell-annotation/condition as input, then applies a rank-based approach to model gene expression and determine differentially expressed genes between contrasting groups of single cells.