SQN

SQN performs subset quantile normalization of high-throughput assay data using non-specific negative control probes to normalize measured intensities while preserving biological variation across samples.

Key Features:

• Non-Specific Control-Based Normalization: Utilizes a large number of negative control probes that span nearly the entire range of measured signal intensities as the basis for normalization.
• Assumption-Free Approach: Relies on non-specific control features to avoid assumptions about the behavior of specific biological signals across samples.
• Preservation of Biological Variation: Maintains more biological variation post-normalization compared to other leading nonlinear normalization procedures.
• Versatility Across Platforms and Experimental Designs: Demonstrated across three different platforms and experimental settings and applicable to microarray and other high-throughput technologies that include a substantial set of control features with constant expectations across samples.
• Robustness to Data Variability: Does not require equal numbers of features across samples and is tolerant of missing data.

Scientific Applications:

• Genomics: Normalization of genomic high-throughput datasets that include control probes or features.
• Transcriptomics: Preprocessing and normalization of transcriptomic measurements from microarray and related platforms.
• Proteomics: Normalization of proteomic high-throughput data where non-specific control features are available.
• Microarray and other high-throughput experiments: Applicability to experiments that incorporate substantial sets of control features with constant expectations across samples.

Methodology:

Implements subset quantile normalization using non-specific negative control probes as the normalization subset, operates without assumptions about specific signals, and handles unequal feature counts and missing data.