SVDMAN

SVDMAN performs Singular Value Decomposition (SVD) analysis on microarray gene expression data to identify gene groups from left singular vectors and to quantify the confidence of SVD-derived hypotheses.

Key Features:

• Threshold-Based Gene Grouping: Identifies non-exclusive gene groups from left singular vectors (gene coefficient vectors) by applying a magnitude threshold defined as W sqrt(N^{-1/2}) with N equal to the number of genes and default weight W = 3, allowing inclusion of genes with both positively and negatively correlated responses.
• Confidence Measure in SVD Analysis: Assesses reliability by systematically removing assays, performing SVD on the reduced dataset to interpolate missing assay values, and calculating the Pearson correlation between interpolated and original assays, applicable when assays represent interpolatable parameters such as time, dose, or concentration.

Scientific Applications:

• Hypothesis Development: Supports formulation of hypotheses about gene interactions and regulatory networks by identifying significant gene groups and providing confidence metrics for those associations.
• Gene Expression Analysis: Facilitates detailed analysis of microarray gene expression data to reveal underlying patterns and associations among genes.

Methodology:

Apply a magnitude threshold W sqrt(N^{-1/2}) to left singular vectors to select gene groups; remove assays and perform SVD on the reduced data to interpolate missing assay values; compute Pearson correlation between interpolated and original assays.