Advances in technology have reduced the cost of single cell sequencing, opening the doors to many new areas of study including transcriptome, DNA genomics, epigenomics and microbial systems. This workshop,
Advances in technology have reduced the cost of single cell sequencing, opening the doors to many new areas of study including transcriptome, DNA genomics, epigenomics and microbial systems. This workshop, provided by experts from across NIH, will give an overview of single-cell sequencing, especially in single cell RNA-seq, highlight tips and potential related to single-cell RNA sequencing, and introduce the major methods and tools available for single-cell RNA sequencing and analysis (both commercial and open source).
Strategies and Methods in scRNA-seq Data Analysis
Li Jia, Bioinformatician, NIH Library
Single cell sequencing has reopened the definition of a cell’s identity and the ways in which that identity is regulated by the cell’s molecular circuitry. Learn the types of studies that are well suited for single cell sequencing analysis as well as how to design a single cell experiment. The speaker will also walk through the steps of a Single Cell RNA-Sequence (scRNA-seq) processing, common analysis strategies, and state-of-the-art analysis methods using R.
Avoiding Common Pitfalls in Single Cell RNA-Seq Experiments
Michael Kelly, Senior Scientist, Single Cell Analysis Facility, Frederick National Laboratory
As the use of single cell sequencing becomes increasingly common, researchers may have a false sense that the technique is immune to issues that undermine the experiment, only to find limitations at the data analysis stage. The speaker will discuss various examples of potential data issues that can arise such as variability in number of targets datapoints, low gene detection, and technical batch effects. As part of this discussion the speaker will address some strategies for how to avoid them, and what they might look like in the final dataset. The speaker will also discuss some of the approaches used during a typical single cell RNA-Seq analysis workflow to help mitigate effects on your data.
The Applications of Current Single Cell Sequencing
Brian J. Henson, Senior Specialist, Illumina, Inc.
The speaker will provide an overview and demonstration of the current single-cell applications available, including RNA, ATAC, CNV, TCR, Epitope, and spatial gene expression. Several examples from the literature will be highlighted as use cases for the tools. The speaker will conclude with a practical discussion on the utility and capacity of using the single-cell applications on the NovaSeq and the NextSeq 2000 benchtop sequencers.
Single Cell Analysis in Partek Flow
Xiaowen Wang, Senior Technical Support, Partek, Inc.
Demonstration from a Partek scientist who will utilize Single Cell RNA-Seq data within Partek Flow to streamline Multi-omics data analysis. This GUI-based tool helps to overcome common analysis challenges on scRNA-Seq data and has built in data visualization options.
Identifying and Interpreting the Human Liver Cellular Landscape using OmicSoft and IPA
Eric Seiser, Senior Application Scientist, QIAGEN Bioinformatics
The speaker will provide a practical example of how they utilized publicly available scRNA-Seq data in a research study. Specifically, the speaker processed scRNA-Seq human liver data using the OmicSoft single-cell analysis pipeline to identify numerous discrete cell populations. Gene signatures from these resident cells were then analyzed in Ingenuity Pathway Analysis to determine both shared and distinct cell biology in the context of pathways, regulation, and functional characteristics. These results provide insight into hepatic cells as well as the immune microenvironment within the liver.
(Wednesday) 9:30 am - 4:00 pm
NIH Training LibraryNIH Training Library