sRAP

sRAP performs comprehensive gene expression analysis of RNA-Seq data by integrating RNA-Seq-specific normalization, quality-control visualization, differential expression analysis, and functional enrichment including BD-Func (BiDirectional FUNCtional enrichment).

Key Features:

• RNA-Seq normalization: Performs normalization specific to RNA-Seq datasets.
• Quality-control visualization: Generates quality-control visualizations for RNA-Seq data.
• Differential expression analysis: Performs differential expression analysis on processed expression data.
• Functional enrichment: Conducts functional enrichment analyses to interpret gene-level results.
• BD-Func (BiDirectional FUNCtional enrichment): Implements BD-Func to compare lists of genes known to be activated or inhibited within pathways or by regulatory molecules.
• Predictive statistics and ROC plots: Computes predictive statistics and generates receiver operating characteristic (ROC) plots to quantify signature accuracy for binary phenotypic variables.
• Multi-group score comparison: Compares BD-Func scores across multiple sample groups.
• Computational performance: Reports accuracy comparable to leading algorithms with significantly reduced computational time.

Scientific Applications:

• RNA-Seq gene expression analysis: Analyzing gene expression from RNA-Seq datasets including normalization, QC and differential expression.
• Functional interpretation: Interpreting functional consequences by comparing activated and inhibited gene lists to predict cellular alterations and patient characteristics.
• Signature evaluation and biomarker assessment: Quantifying accuracy of gene expression signatures for binary phenotypes using predictive statistics and ROC analysis.
• Translational prediction: Evaluating whether custom gene expression signatures derived from cell line data predict biological activity in vivo, exemplified by progesterone receptor and LBH589 signatures.

Methodology:

Normalization specific to RNA-Seq, quality-control visualization, differential expression analysis, functional enrichment via BD-Func (comparing activated/inhibited gene lists), computation of predictive statistics, generation of ROC plots, and comparison of scores across multiple sample groups.