acde

acde performs large-scale multiple hypothesis testing to detect differential gene expression and gene co-expression associations in high-throughput gene expression datasets.

Key Features:

• Differential Expression Testing: Evaluates associations between gene expression levels and biological or clinical covariates, including censored outcomes.
• Co-Expression Analysis: Tests pairwise correlations between gene expression profiles to identify significantly co-expressed gene pairs.
• Large-Scale Multiple Testing Framework: Supports simultaneous hypothesis testing across thousands of genes in high-throughput datasets such as microarrays.
• Error Control in High-Dimensional Data: Implements statistical procedures designed to manage multiplicity in genome-wide expression analyses.

Scientific Applications:

• Genome-Wide Differential Expression Studies: Identifies genes whose expression levels are associated with biological or clinical variables.
• Gene Co-Expression Network Analysis: Detects statistically significant correlations between gene expression profiles across large datasets.
• High-Throughput Transcriptomics Analysis: Enables statistical interpretation of microarray-based gene expression measurements.

Methodology:

acde formulates differential expression and co-expression detection as large sets of simultaneous hypothesis tests evaluating gene–covariate associations and pairwise gene expression correlations while applying statistical procedures to control errors under large-scale multiple testing.