NOISeq

NOISeq provides quality control, exploratory analysis, and non-parametric differential expression analysis of RNA-seq count data.

Key Features:

• Input data: Operates on count data derived from RNA sequencing (RNA-seq).
• Quality control diagnostics: Provides diagnostic tools for monitoring quality issues within RNA-seq datasets.
• Exploratory plots: Generates exploratory plots including saturation levels, count distributions, chromosome-level expression patterns, and summaries of detected feature types and lengths.
• Non-parametric differential expression: Implements non-parametric methods for differential expression analysis that do not rely on parametric assumptions.
• NOISeqBIO: Includes the NOISeqBIO non-parametric method tailored for experiments with biological replication.
• False discovery control: Emphasizes control of false discoveries in differential expression results.
• Condition comparison: Supports comparison between two experimental conditions to identify differentially expressed genes.
• Implementation: Distributed as an R package.

Scientific Applications:

• RNA-seq quality assessment: Assess and monitor quality issues in RNA-seq count datasets.
• Exploratory data analysis: Explore saturation, count distributions, chromosome-level expression, and feature type and length distributions.
• Differential expression analysis: Identify differentially expressed genes between two experimental conditions, particularly in studies with biological replication.
• False discovery evaluation: Evaluate and control false discoveries in differential expression studies.

Methodology:

Applies non-parametric statistical analysis (NOISeqBIO) to RNA-seq count data, generates diagnostic/exploratory plots (saturation levels, count distributions, chromosome-level expression, feature types and lengths), and compares two experimental conditions with emphasis on controlling false discoveries in the presence of biological replication.