Single-cell RNA sequencing (scRNA-seq) is an important technology that reveals gene expression differences between different cell types. Yet scRNA-seq data can be challenging to analyze and interpret. In this webinar, we’ll show how our QIAGEN Digital Insights bioinformatics tools can help you analyze and interpret whole transcriptome data from a human single-cell sequencing experiment.

We will analyze and compare the differentially expressed genes between stage III and normal for significant cell types in the tumor microenvironment from patient cases with HGSOC. We will use QIAGEN Ingenuity Pathway Analysis (IPA) to illuminate the underlying biology. We’ll show how IPA’s Land Explorer feature lets us compare and contextualize specific findings in HGSOC to other related or seemingly unrelated datasets.

In this webinar, you’ll learn how to:
Analyze scRNA-seq samples using QIAGEN CLC Genomic Workbench
Analyze and explore the biology of a processed dataset in QIAGEN IPA
Identify significant cell-specific canonical pathways or function-specific regulators
Generate hypotheses about novel regulatory networks that suggest drivers of the expression changes seen in the different cell types in stage III HGSOC
Visualize valuable information about a gene of interest in other contexts (disease, expression)
Compare gene signatures enriched in clusters of cells versus tens of thousands of gene expression signatures in QIAGEN IPA to pinpoint significant regulator genes associated with HGSOC stage III
Gain added insights about single-cell biology by comparing the HGSOC dataset in QIAGEN IPA to more than 118,000 public datasets which have been curated and processed in QIAGEN OmicSoft Suite