SCAN.UPC

SCAN.UPC normalizes and standardizes gene expression data across one- and two-channel microarrays and RNA sequencing to correct platform-specific biases and produce comparable Universal exPression Code (UPC) values for downstream analyses.

Key Features:

• Universal exPression Code (UPC): Implements the UPC methodology to produce standardized expression values on a zero-to-one scale for platform-independent interpretation.
• Per-sample normalization: Normalizes each sample individually by modeling and removing probe- and array-specific background noise using data intrinsic to each array.
• Genomic composition and target-length adjustment: Accounts for genomic base composition and the length of target regions in the normalization process.
• Mixture model for activity estimation: Utilizes a mixture model to estimate gene activity status within profiling samples.
• Platform agnosticism: Supports one- and two-channel expression microarrays and next-generation sequencing (RNA sequencing) for cross-technology integration.
• Individual-sample compatibility: Processes individual samples without requiring ancillary batch samples, enabling analyses in personalized-medicine contexts.
• Comparative performance: UPC values have demonstrated performance comparable to methods developed specifically for microarrays or RNA sequencing.

Scientific Applications:

• Cross-platform data integration: Facilitates integration of gene expression datasets generated on different profiling technologies.
• Consistent downstream analysis: Enables consistent downstream analyses irrespective of underlying microarray or RNA-seq technology.
• Personalized medicine workflows: Supports individualized sample processing for studies where batch processing is not feasible.

Methodology:

Calculates UPC values standardized to a zero-to-one scale; normalizes each sample by modeling and removing probe- and array-specific background noise using array-intrinsic data; accounts for genomic base composition and target region length; applies a mixture model to estimate gene activity.