Single-Cell and Ultra-Low-Input RNA-Seq

The DRAGEN Single-Cell RNA (scRNA) Pipeline can process multiplexed single-cell RNA-Seq data sets from reads to a cell-by-gene UMI count gene expression matrix. The pipeline is compatible with library designs that have one read in a fragment match to a transcript and the other containing a cell-barcode and UMI. The pipeline includes the following functions:

• RNA-Seq (splice-aware) alignment and matching to annotated genes for the transcript reads
• Cell-barcode and UMI error correction for the barcode reads
• Genotype-based and genotype-free sample demultiplexing
• UMI counting per cell and gene to measure gene expression
• Cell hashing and feature counting by read 2 UMI
• Sparse gene expression matrix output
• Single cell RNA QC metrics

Partek Flow takes you from raw RNA-Seq data to pathways with powerful statistics and visualizations. Seamlessly analyze data with easy-to-use workflows and interactive visualizations with no command-line experience needed. It combines the powerful statistics you trust with information-rich, interactive visualizations to take your analysis from start to finish. It’s as simple as point, click, and done.

• Import your own data or publicly available data from popular online repositories
• Remove batch effects to discover biological information
• Perform pseduo bulk analysis
• Discover biomarkers that define a cell population
• Find differentially expressed genes and proteins

The DRAGEN Single-Cell RNA offers a cell x gene expression matrix output starting point for downstream single cell analysis.

Key features include:

• Ultra-rapid. Analysis times less than 40 minutes for ~8000 cells with > 1 billion reads^**
• Widely compatible: Supports a wide range of input library types, giving a common output of cell x gene expression matrix compatible with downstream analysis tools
• Efficient: Goes from BCL files to quantified expression per cell with a single tool

In this webinar, a team of experts outline the essential concepts and benefits of single cell analysis and why scientists should consider it for their research. They illuminate the analytical process and discuss strategies to overcome common analysis challenges.

You’ll learn:

• What single cell data analysis is and how it differs from bulk analysis
• The basic steps of the single cell analysis pipeline including cell typing
• How to explore and interpret data visualizations; what exactly is a tSNE or UMAP plot?

From Crohn's disease and peanut allergies to cell therapies and immunotherapies, the lab at NIHR Guys' and St. Thomas' Biomedical Research Centre is using Illumina technology for single-cell genomics investigations.

Swetha Anandhan from the MD Anderson Cancer Center joins Illumina and 10x Genomics for this webinar. She highlights the use of single cell RNA-sequencing to identify a unique population of macrophages in glioblastoma multiforme that persists after treatment with immune checkpoint inhibitors.

Single-cell sequencing methods can be distinguished by cell throughput. Low-throughput methods include mechanical manipulation or cell sorting/partitioning technologies and are able to process dozens to a few hundred cells per experiment.

Recent advances in microfluidic technologies have enabled high-throughput single cell profiling where researchers can examine hundreds to tens of thousands of cells per experiment in a cost-effective manner. Both the high - and low- throughput methods utilize Illumina sequencing by synthesis (SBS) chemistry, an industry leading sequencing technology. Illumina SBS technology generates >90% of the world's sequencing data.^*