DaMiRseq

DaMiRseq performs biomarker discovery and classification from RNA-Seq transcriptome data by enabling differential expression analysis and feature selection for high-throughput transcriptomic studies.

Key Features:

• High-dimensional data handling: Processes high-dimensional RNA-Seq transcriptome data to support downstream analyses.
• Noise and bias removal: Systematically removes technical noise and bias from RNA-Seq counts during preprocessing.
• Feature selection: Identifies the most informative features (genes/transcripts) for biomarker discovery.
• Differential expression analysis: Performs differential expression analysis to detect differentially expressed genes or transcripts.
• Classification: Implements classification algorithms to categorize samples into binary and multi-class groups based on selected features.

Scientific Applications:

• Transcriptional biomarker discovery: Identification of gene- or transcript-level biomarkers from RNA-Seq data for diagnostic or prognostic studies.
• Class comparison and profiling: Comparative analysis of gene expression across conditions or disease states using differential expression and classification.

Methodology:

Data preprocessing to remove noise and bias; feature selection to identify informative genes/transcripts; and classification using algorithms for binary or multi-class sample categorization.