Principled PCA separates signal from noise in omics count data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics
• Transcriptomics
• Proteomics
• Metabolomics
• Single-cell Omics

Background:

• Principal Component Analysis (PCA) is crucial for high-throughput omics data analysis, aiming to extract biological variability and reduce noise.
• Standard PCA requires appropriate normalization, transformation, and accurate selection of principal components, which are often based on heuristics.
• Improper data processing in PCA can lead to loss of biological information or signal corruption due to noise.

Purpose of the Study:

• To introduce Biwhitened PCA (BiPCA), a theoretically grounded framework for rank estimation and data denoising in omics datasets.
• To address the challenges of count noise in omics data through adaptive rescaling.
• To enhance the biological interpretability and robustness of high-throughput count data analysis across various omics modalities.

Main Methods:

• Developed Biwhitened PCA (BiPCA), a novel framework for omics data analysis.
• Implemented adaptive rescaling of rows and columns to standardize noise variances across dimensions.
• Validated BiPCA through simulations and analysis of over 100 datasets from seven omics modalities.

Main Results:

• BiPCA reliably recovers the data rank and enhances the biological interpretability of omics count data.
• Demonstrated improvement in marker gene expression identification and preservation of cell neighborhood structures.
• Showcased BiPCA's effectiveness in mitigating batch effects in high-throughput omics data.

Conclusions:

• BiPCA provides a theoretically sound and robust approach for analyzing diverse omics data types.
• The framework effectively handles count noise, leading to improved data quality and biological insights.
• BiPCA is a versatile tool for advancing high-throughput omics data analysis, offering enhanced signal recovery and noise reduction.