Modular RNA-seq Analytics for Exploratory Biomarker Discovery using Public Data.
TL;DR
This study presents a modular analytics pipeline that combines publicly available datasets with established open-source tools; standardizes quality control, differential expression analysis, and pathway analysis; and leverages competitive machine learning to unify disparate RNA-seq datasets for robust biomarker identification.
Abstract
Publicly available RNA sequencing (RNA-seq) data provide a cost-effective springboard for biomarker discovery. However, heterogeneity across studies often complicates analysis. This study presents a modular analytics pipeline that combines publicly available datasets with established open-source tools; standardizes quality control, differential expression analysis, and pathway analysis; and leverages competitive machine learning to unify disparate RNA-seq datasets for robust biomarker identification. The workflow is demonstrated across three disease contexts, ranging from small pilot datasets to larger, integrated analyses: (1) identifying differentially expressed gene signatures associated with COVID-19 severity, (2) combining differential expression and machine learning techniques to analyze multi-cohort sepsis datasets, resulting in concise biomarker panels, and (3) utilizing both bulk and single-cell data to examine tissue and cell-type specificity of N-acyl-phosphatidylethanolamine phospholipase D in atherosclerosis. These applications demonstrate how an adaptable, modular pipeline using open-source tools can repurpose public data to reduce noise, generate new hypotheses, and reveal meaningful biological insights, thereby establishing a foundation for future research and underscoring the importance of public data in exploratory biomarker discovery.