RNAseq data analysis: getting more insight by combining it with public RNAseq experiments

Abstract
Most biological experiments are analyzed individually by comparing experimental factors being tested in that particular experiment. Typically, in microarray or RNAseq experiments, one would try to identify genes and processes having significantly altered expression levels in the tested conditions relative to control conditions. The main drawback with this approach is that it merely provides genes showing altered expression, but it doesn’t indicate whether they are SPECIFIC for that factor. For example, inflammatory genes are upregulated in a wide variety of diseases and frequently appear in the list of most significantly regulated genes when comparing diseased with healthy control samples. To find which inflammation genes are specific for one chosen disease therefore requires that this particular disease is analyzed in combination with datasets from other diseases. In this talk, I will show how RNAseq or microarray experiments can be better analyzed and interpreted when analyzing them in combination with chosen public experiments or with reference datasets.