MaCSBio's Michael Lenz's Article published
Principal components analysis (PCA) is a common unsupervised method for the analysis of gene expression microarray data, providing information on the overall structure of the analyzed dataset.
In the recent years, it has been applied to very large datasets involving many different tissues and cell types, in order to create a low dimensional global map of human gene expression. Here, we reevaluate this approach and show that the linear intrinsic dimensionality of this global map is higher than previously reported. Furthermore, we analyze in which cases PCA fails to detect biologically relevant information and point the reader to methods that overcome these limitations.
Our results refine the current understanding of the overall structure of gene expression spaces and show that PCA critically depends on the effect size of the biological signal as well as on the fraction of samples containing this signal.
Also read
-
“We want to give young academics a voice”
Maastricht Young Academy ensures that the experiences of “young” academics are also heard. Outgoing MYA chair Jenny Schell-Leugers and her successor Michelle Moerel explain why this is so important.Featured
-
Working at UM: “a life-changing experience”
"I am proud that our new Circular Plastics group published its first completely in-house research," Kim Ragaert says. She founded the research group three years ago, when she moved to Maastricht. Her work has laid the foundations for many innovations in the field of plastic recycling, and she is...Featured andResearchers
-
How does the universe taste?
Gerco Onderwater investigates the flavour of the universe while guarding the flavour of the Maastricht Science Programme. On 31 May, during his inaugural lecture, he provided a pre-taste of his work in Maastricht.Featured andResearchers
