sscore

sscore quantifies differential gene expression from oligonucleotide microarray data by assigning statistical significance scores to expression changes to detect biologically meaningful differences between conditions.

Key Features:

• Statistical significance scoring: Expresses expression changes as significance scores rather than relying solely on fold changes or summary intensities.
• Probe-pair integration: Integrates signals from multiple probe pairs to derive probe-set level measures.
• Error model for probe-level variability: Uses an error model tailored to the characteristics of high-density oligonucleotide arrays to account for probe-level variability.
• Measurement noise handling: Explicitly accounts for measurement noise inherent in high-density oligonucleotide microarray experiments.
• Sensitivity for small sample sizes: Provides a rigorous and sensitive approach suited to experiments with modest sample sizes and high probe-level complexity.
• High-density array focus: Designed specifically for high-density oligonucleotide arrays and the large numbers of measurements they generate.

Scientific Applications:

• Differential expression analysis: Identification of genes with statistically significant changes in expression between experimental conditions on oligonucleotide microarrays.
• Microarray data interpretation: Improved robustness and interpretability of high-density oligonucleotide microarray analyses.
• Studies with limited samples: Detection of biologically meaningful differences in experiments with modest sample sizes and complex probe-level signals.
• Biological system profiling: Exploration of gene expression patterns in various biological systems, including studies of the central nervous system.

Methodology:

Integrates signals from multiple probe pairs using an error model tailored to high-density oligonucleotide arrays to quantify differential gene expression as statistical significance scores, explicitly accounting for probe-level variability and measurement noise rather than relying on fold changes or summary intensities.