Within QIAGEN Ingenuity Pathway Analysis (IPA), public data sourced from OmicSoft is already processed and standardized, making it easy to jump to the actual research.

In this training, we'll show how to effectively use sample-level public data and metadata from sources like GEO, SRA, TCGA, GTEx, Blueprint, CCLE and other sources using the IPA Analysis Match and Land Explorer features. We will walk through various use cases, such as biomarker discovery, drug target investigation, studying survival in custom patient cohorts, multi-gene correlation and more.

You will learn to answer questions such as:
• How is a gene of interest expressed across different conditions (e.g. diseases, treatments, cell lines)?
• Is there correlation in the 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 (e.g., genes specific to a disease, treatment or cell type)?
• Can we generate custom cohorts of patients (e.g., example TP53 wt vs mutant or PDCD1 high vs low expression) and then, generate survival curves representing those cohorts?
Can we generate p-values to see if there is significant difference?
• Can we detect expression of a gene in different cell types from single cell data?