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 can resolve subgroups that computational clustering alone may miss, and it supports rapid hypothesis generation that you can interrogate with statistical analysis in QIAGEN Ingenuity Pathway Analysis.
This session is the fifth installment in the Educational Webinar Series 2026, moving from experimental design and data generation to interpretation.
By watching the recording, you’ll learn:
-How to predict activity of upstream regulators, canonical pathways, and biological functions directly at the single-cell level
-How cell-level activity prediction can uncover subgroups that computational clustering alone may miss
-How to use cell-level insights to generate hypotheses for follow-up analysis
-How to validate those hypotheses with statistical analysis in QIAGEN Ingenuity Pathway Analysis
