Apr 30, 2015

GO-PCA: An Unsupervised Method to Explore Biological Heterogeneity Based on Gene Expression and Prior Knowledge

BioRxiv : the Preprint Server for Biology
Florian Wagner

Abstract

Genome-wide expression profiling is a cost-efficient and widely used method to characterize heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such datasets typically relies on generic unsupervised methods, e.g. principal component analysis or hierarchical clustering. However, generic methods fail to exploit the significant amount of knowledge that exists about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that incorporates prior knowledge about gene functions in the form of gene ontology (GO) annotations. GO-PCA aims to discover and represent biological heterogeneity along all major axes of variation in a given dataset, while suppressing heterogeneity due to technical biases. To this end, GO-PCA combines principal component analysis (PCA) with nonparametric GO enrichment analysis, and uses the results to generate expression signatures based on small sets of functionally related genes. I first applied GO-PCA to expression data from diverse lineages of the human hematopoietic system, and obtained a small set of signatures that captured known cell characteristics for most lineages. I then applied the method to expression profiles of...Continue Reading

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Mentioned in this Paper

Genome-Wide Association Study
Guanosine
Laboratory Procedures
Graphene
Classification
2-Dimensional
Genes
GORAB
Molecular Profiling
Gene Expression

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