New Single-Cell Analysis Tools Reveal Changes in the Transcriptome Profiles of Endothelial Tumor Cells
Recorded On: 04/22/2020 at 11:00 AM EST Location: Online
With the cost of sequencing a whole human genome dropping to $1,000, the size, complexity, and locations of datasets has expanded dramatically. This expansion has been particularly prominent in the field of single-cell genomics, where just one sample is often composed of thousands of whole transcriptomes. Compounding the challenges of the sheer volume of data in a single-cell experiment, the data from the individual cells is noisy and sparse, requiring new tools to identify patterns in populations of individual cells. Adapting these new analysis methods to cloud computing environments addresses the challenges in the size, complexity and location of the datasets.
Key points you will learn:
- Toolkits and workflows optimized for processing UMI-based and full-length single-cell protocols such as: 10x Chromium, Drop-seq, inDrops, CEL-seq and Smart-seq2.
- Comparative benchmarking of different tools for processing 10x Chromium datasets on Seven Bridges Platform.
- Methods for correction of batch effects, single-cell clustering, identification of marker genes and trajectory inference. These include interactive RMarkdown notebooks based on popular Bioconductor packages which can be executed within RStudio environment.
- How we used a workflow based on HISAT2 aligner and RSEM quantification tool in combination with interactive analysis based on Monocle R package to identify temporal transcriptional changes in tumor endothelial cells.