protGear

protGear pre-processes protein microarray data to correct systematic and non-systematic biases and prepare datasets for downstream proteomic analysis of the human proteome.

Key Features:

• Background correction: Implements background correction algorithms for protein microarray signal intensities.
• Within-sample variation quantification: Quantifies within-sample variation including metrics that account for low-intensity signals.
• Normalization: Performs normalization across arrays with attention to the impact of experimental design on algorithm performance.
• Batch effect correction: Detects and corrects batch effects to minimize technical variability between experimental batches.
• Low-intensity CV adjustment: Applies a novel function to adjust interpretation of the coefficient of variation when signal intensities approach the lower detection threshold.
• Scanner compatibility: Accepts data from major protein microarray scanners.
• Reproducibility records: Generates auditable records to support reproducibility of preprocessing steps.
• R interface and script integration: Exposes functionality via an R interface and enables integration of custom analysis scripts.

Scientific Applications:

• High-throughput proteomics: Prepares protein microarray datasets for high-throughput studies of the human proteome.
• Infectious disease research: Processes microarray data used in infectious disease investigations.
• Vaccine development: Supports preprocessing of protein microarray data used in vaccine-related studies.
• Proteomic studies: Improves data quality for downstream proteomic analyses reliant on protein microarray measurements.

Methodology:

Computational steps explicitly include background correction, quantification of within-sample variation, normalization, batch-effect correction, a novel adjustment for coefficient of variation at low signal intensities, generation of auditable records, and exposure of functionality via an R interface while accepting data from major protein microarray scanners.