RNA-Seq Blog Transcriptome Research & Industry News
This video looks at how you can apply some of the tools within SeqMonk to the analysis of single-cell RNA-Seq data. It goes through QC, quantitation, variation analysis, clustering and PCA so show how these can all use useful in trying to understand the structure of large RNA-Seq datasets. – Babraham Bioinformatics
Don't Miss
- Application of Single Cell Sequencing (SCS) Technology
- Researchers use single-cell approaches and deep learning to map all stages of fruit fly embryo development
- DeLTa-Seq – direct-lysate targeted RNA-Seq from crude tissue lysate
- Tumor bulk-RNA seq identifies patients at high risk of progression in non-complete pathological responders from NADIM trial
- STRIDE – a topic-model-based method to deconvolve spatial transcriptomics using scRNA-seq
- Researchers find heart failure arises from mutations in numerous distinct genes
- Transcriptome diversity in human tissues mapped with emerging long-read RNA sequencing technology
- Significance analysis for clustering with single-cell RNA-sequencing data
- BacDrop – droplet-based single-cell RNA-seq reveals heterogeneity in bacterial populations and in response to antibiotic perturbation
- Gruffi – an algorithm for improved delineation of developmental trajectories in organoids and resemblance to in vivo data.
