scRNA-seq
The world of single-cell genomics is buzzing with excitement, and for good reason. A new wave of powerful AI tools, including foundation models like Geneformer and scGPT, promises to automate one of the most laborious steps in scRNA-seq analysis: cell type annotation. What once took experts weeks of painstaking manual
scRNA-seq
Single-cell RNA sequencing (scRNA-seq) has become a cornerstone of modern biology, offering unprecedented resolution into cellular diversity, states, and functions. With this power comes complexity: analyzing scRNA-seq data involves multiple steps, from quality control and normalization to clustering, visualization, and differential expression analysis. Each step can be approached through two
scRNA-seq
Introduction Recent advances in single-cell RNA sequencing (scRNAseq) technologies have revolutionized the study of cellular heterogeneity, characteristics, and activity in various environments. Yet accurate, reproducible, harmonized, consistent, and automated cell type annotation remains a significant challenge. In this blog post, we will explore some of the major approaches of automated
scRNA-seq
Uncovering hidden treasures in cellular diversity The advent of single-cell technologies has revolutionized our understanding of biological complexity, providing a lens through which we can discern the fine details of cellular heterogeneity. This transformative approach enables researchers to characterize cell types and discover new subtypes that have remained elusive with