In this blog, we examine how the “perturbation effect” can vary depending on the metrics used to define it, and why these differences matter. While these metrics may appear interchangeable, they often capture fundamentally different aspects of the underlying biology. As Perturb-seq datasets continue to grow exponentially, understanding how perturbation effects are measured becomes critical for reliable downstream analysis. When suppression is not an on-off switch In 2025, Nadig and colleagues

The Virtual Cell is a concept at the intersection of computational biology and systems science. At its core, it aims to represent a predictive model of how a living cell responds to internal and external cues. In its ideal form, a Virtual Cell captures every molecular detail - dynamic proteins, metabolic fluxes, physical interactions, and more. Building such a fully mechanistic model remains beyond current computational capabilities. A more tractable approximation has emerged over the past deca

Single-cell RNA sequencing (scRNA-seq) is revolutionizing our understanding of cellular biology, but the computational challenges of processing these massive datasets continue to evolve. As datasets grow from thousands to millions of cells, the choice of data format and processing pipeline becomes critical.  Parquet files, with their columnar storage and excellent compression ratios, seem like a natural fit for intermediate data storage in machine learning workflows. In a previous blog post, we