pickgene

pickgene implements an adaptive statistical framework for identifying informative genes from microarray expression data, preserving low-abundance transcripts such as transcription factors and receptors by addressing background-correction artifacts that produce low or negative expression values through a normal-scores approach that adapts to expression-dependent variability.

Key Features:

• Adaptive statistical framework: Implements an expression-dependent statistical model that adapts to varying signal intensity across the microarray expression spectrum.
• Normal-scores transformation: Uses normal scores to stabilize distributions and adapt to expression-dependent variability.
• Variance modeling: Explicitly models changes in variance across the expression spectrum to account for heteroscedasticity.
• Gene-level p-values: Produces gene-level p-values that are sensitive to heteroscedasticity for improved inference on weakly expressed transcripts.
• Preservation of low-abundance signals: Retains biologically important low-abundance transcripts, mitigating effects of background correction that yield low or negative expression values.

Scientific Applications:

• Exploratory analyses: Supports clustering, visualization, and feature selection workflows that rely on retention of low-abundance signals.
• Regulatory network analysis: Facilitates interpretation of regulatory networks by preserving transcription factors and receptors for downstream analysis.
• Microarray differential inference: Improves detection and inference for weakly expressed transcripts in microarray studies.

Methodology:

Applies a normal-scores-based transformation within an adaptive statistical framework that models expression-dependent variance across the microarray spectrum and computes gene-level p-values accounting for heteroscedasticity.