ISA

ISA decomposes genome-wide gene expression data into context-dependent, potentially overlapping transcription modules by iteratively refining gene and condition sets using a linear map of the normalized expression matrix combined with thresholding to improve robustness relative to singular value decomposition (SVD).

Key Features:

• Context-dependent transcription modules: Dynamically assigns both genes and experimental conditions to modules, allowing genes to belong to multiple context-specific modules.
• Iterative refinement algorithm: Repeatedly refines sets of genes and conditions until they conform to a rigorous definition of a transcription module.
• Linear map from normalized expression matrix: Uses the linear map induced by the normalized expression matrix as a core computational step in module identification.
• Threshold function: Applies a threshold function to enhance classification accuracy and mitigate the effects of noise in expression data.
• Generalization of singular value decomposition (SVD): Recovers SVD behavior when no threshold is applied and extends it by incorporating thresholding for greater robustness to noisy data.

Scientific Applications:

• Gene expression analysis: Identifies coherent transcription modules in large-scale expression datasets where traditional clustering methods are limited.
• Noise reduction in classification: Uses thresholding to reduce the impact of noisy measurements on module assignment.
• Yeast genomics (Saccharomyces cerevisiae): Has been applied to Saccharomyces cerevisiae expression data to reveal context-specific transcriptional modules.

Methodology:

Iterative refinement of gene and condition sets using the linear map induced by the normalized expression matrix and application of a threshold function until sets meet the defined criteria for transcription modules; SVD is recovered in the special case of no threshold.