The latest updates, guides and stories from DataXight

The Clinical Data Analysis Pipelines (CDAP), originally developed by the NIH Office of Cancer Clinical Proteomics Research (OCCPR), formerly Clinical Proteomic Tumor Analysis Consortium (CPTAC), and now hosted by the NIH Proteomic Data Commons (PDC) standardize proteomics data processing to reduce variability and enable cross-dataset comparisons. Public dissemination of these Galaxy workflows on GitHub is part of  the NIH's support of FAIR data principles. While these pipelines represent a promi

The Challenge: Big Data Meets Complex Biology Developing a scalable solution for rare disease screening at population scale presents a perfect storm of computational and biological complexity. The numbers can be staggering: over a billion variant alleles distributed across hundreds of thousands of genomic shards, often totaling tens of terabytes of data. But beyond the computational demands lies an even greater hurdle—the nuanced molecular complexity of rare genetic diseases themselves. Unlik

Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to explore complex biological systems, but batch effects remain a significant challenge to accurate insights. In this first part of our series, we will delve into what batch effects is and how batch effects can hinder discoveries, illustrated with data visualizations and analysis. Future posts will explore strategies for correcting and visualizing batch effects to ensure reliable and reproducible results across multiomic data.