Per user feedback, we are hosting a comprehensive training on how to effectively use sample level public data and metadata from sources like GEO, SRA, TCGA, GTEx, Blueprint, CCLE and other sources through Ingenuity Pathway Analysis (IPA) and IPA Analysis Match Explorer feature. The trainer will walk through usecases in realms of biomarker discovery, drug target investigation, studying survival in custom patient cohorts, multi-gene correlation and more.
Note: The word “condition” below refers to different diseases, disease subtypes, treatments, cell types, cell lines and more.
This training will cover topics like
• How is a gene of interest expressed across different conditions?
• Is there correlation in expression for two genes or biomarkers of user interest for a given condition?
• For a given condition of interest, can we derive a list of genes (example genes specific to a disease, treatment or cell type)?
• Can we generate custom cohorts of patients (example TP53 wt vs mutant or PDCD1 high vs low expression) and then generate survival curves representing those cohorts? Can we generate p-value to see if there is significant difference?
• Recent update: Can we detect expression of a gene in different cell types from single cell data?