Putting Single Cell Data to Work

The growing scale of single cell transcriptomics enables increasingly granular exploration of cell states, but traditional methods for identifying biological differences between single cell clusters assume rigid and distinct cell states. In this webinar, you'll learn how to predict activity of upstream regulators, canonical pathways and biological functions directly on single cell data, at the cell level. Cell-level activity prediction reveals subgroups of cells that are not easily identified by computational clustering. It also supports rapid generation of hypotheses, which can be explored with robust statistical analysis using Ingenuity Pathway Analysis.

This session marks the fifth installment in our single-cell webinar series, moving from experimental design and data generation to interpretation. As experiments scale to hundreds of thousands of cells, the bottleneck becomes making sense of the data, which is where cell-level activity prediction comes in.

What you’ll learn by attending:

• How to predict activity of upstream regulators, canonical pathways, and biological functions directly at the single-cell level
• How cell-level activity prediction uncovers subgroups that computational clustering alone can miss
• How to use cell-level insights to rapidly generate hypotheses
• How to validate those hypotheses with statistical analysis in QIAGEN Ingenuity Pathway Analysis