RNA-Seq Blog Transcriptome Research & Industry News
The ability to routinely measure gene expression within thousands of individual cells opens exciting opportunities in many domains of neurobiology. In this talk, Evan Macosko from the Stanley Center for Psychiatric Research will introduce the basic experimental and computational concepts in single cell analysis, and provide some practical guidance on its application to problems in neuroscience. Single cell analysis is, in many ways, the harbinger of a broader transition towards “contextualized genomics,” in which the information-rich readouts of DNA sequencing can be leveraged to make many new kinds of measurements of cells and tissues. Evan describes the ways in which this technological frontier is likely to impact neurobiology.
Don't Miss
- Learning to predict RNA sequence expressions from whole slide images
- CRIC-seq – global profiling of in-situ RNA–RNA contacts associated with a specific RNA-binding protein
- Novel machine-learning algorithm creates atlas of cancer with potential as universal diagnostic platform
- Long noncoding RNA profiling may be effective for stratification in pediatric AML
- SoCube – an innovative end-to-end doublet detection algorithm for analyzing scRNA-seq data
- Quantabio adds powerful bead booster chemistry to ultra-fast sparQ RNA-Seq HMR kit
- SiT – spatial isoform transcriptomics
- Analysis of single-cell rna-seq data
- AllenDigger – spatial expression data visualization, spatial heterogeneity delineation, and single-cell registration based on the Allen Brain Atlas
- New research identifies an inaccuracy in the single cell GENCODE gene model
