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Part II: Single-cell RNA sequencing data interpretation using QIAGEN Ingenuity Pathway Analysis
This two-part series will walk you through single-cell RNA sequencing (scRNA-seq) analysis starting with a matrix file or FASTQ files and ending with a deep understanding of key pathways, regulators and cell type signatures within your data.
In this session, we'll explore how you can take your scRNA-seq differential expression results produced from QIAGEN CLC Genomics Workbench (or from another software/package) and upload them into QIAGEN Ingenuity Pathway Analysis (IPA) to uncover a deep understanding of the biology in your experiment by exploring enrichment and activity prediction for pathways, regulators and more.
Specifically, you'll learn how to:
• Import data using the QIAGEN IPA plugin from QIAGEN CLC or data generated from a different software/pipeline/package
• Explore canonical pathway enrichment and predicted activity for your comparisons
• Find key regulators that may be responsible for the biology in your experiment
• Compare multiple core analyses and see side-by-side results for various cell types, clusters and more
• Compare the expression patterns for pathways and regulators in your dataset with over 135,000 precomputed public analyses in IPA
• Compare your complete analysis signature with over 135,000 precomputed public analyses in IPA
Part I focuses on the secondary analysis of scRNA-seq data using QIAGEN CLC Genomics Workbench. You'll learn to explore differential expression, UMAP, dot plot and more. Click here to watch Part I: https://tv.qiagenbioinformatics.com/video/87882199/part-i-single-cell-rna-sequencing
01:31:19 minutes
Tags: ipa, single-cell