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You asked for it, and we’re here to deliver. We are hosting a comprehensive training on effectively using sample-level public data and metadata from sources like GEO, SRA, TCGA, GTEx, Blueprint, CCLE and others through QIAGEN Ingenuity Pathway Analysis (IPA) and the IPA Analysis Match Explorer feature. We’ll walk you through use cases involving biomarker discovery, drug target investigation, studying survival in custom patient cohorts, multi-gene correlation and more.
We’ll cover topics like:
• How is a gene of interest expressed across different conditions?(‘conditions’ refers to diseases, disease subtypes, treatments, cell types, cell lines and more)
• Is there a correlation in expression for two genes or biomarkers of user interest for a given condition?
• Can we compare more than two genes in a heatmap?
• For a given condition of interest, can we derive a list of genes (for example, genes specific to a disease, treatment or cell type)?
• Can we generate custom cohorts of patients (for example, TP53 wt vs. mutant or PDCD1 high vs. low expression) and then create survival curves representing those cohorts? Can we generate a p-value to see if there is a significant difference?
• Can we detect the expression of a gene in different cell types from single-cell data?
