Metagene projection for cross-platform, cross-species characterization of global transcriptional states

Abstract

The high dimensionality of global transcription profiles, the expression level of 20,000 genes in a much small number of samples, presents challenges that affect the sensitivity and general applicability of analysis results. In principle, it would be better to describe the data in terms of a small number of metagenes, positive linear combinations of genes, which could reduce noise while still capturing the invariant biological features of the data. Here, we describe how to accomplish such a reduction in dimension by a metagene projection methodology, which can greatly reduce the number of features used to characterize microarray data. We show, in applications to the analysis of leukemia and lung cancer data sets, how this approach can help assess and interpret similarities and differences between independent data sets, enable crossplatform and cross-species analysis, improve clustering and class prediction, and provide a computational means to detect and remove sample contamination.cancer ͉ dimension reduction ͉ expression analysis ͉ noise reduction ͉ sample contamination A major challenge in the analysis of global transcription profiles is the high level of noise and the lack of reproducibility across data sets, which results from fitting models to small numbers of samples in a high-dimensional space (i.e., thousands of genes). Ideally we would prefer to reduce the data to a small number of metagenes that better capture the essential behavior of the samples.There are many advantages to such a metagene approach. By capturing the major, invariant biological features and reducing noise, metagenes provide descriptions of data sets that allow them to be more easily combined and compared. This is especially important when we are considering cross-platform or cross-species data. Ultimately, this can result in more sensitive clustering and classification. In addition, interpretation of the metagenes, which characterize a subtype or subset of samples, can give us insight into underlying mechanisms and processes of a disease.Here, we describe a general methodology, metagene projection, that creates a low-dimensional representation of a training (model) data set using nonnegative metagene factors into which an independently obtained new (test) set of samples or data can be projected and analyzed. The metagene factors are a small number of gene combinations that distinguish expression patterns of subclasses in a data set. We obtain the factors by the application of nonnegative matrix factorization (NMF) (1, 2) used to extract facial features from images. We showed (3) how NMF can extract metagenes that provide stable, robust clustering of expression data. Moreover, by using gene set enrichment analysis (GSEA) to annotate the metagene factors themselves, we can gain insight into the underlying biology of both the training and test data sets.Importantly, we illustrate the utility of metagene projection by its application to leukemia and lung cancer data sets. We show how the projection of new data sets into the space of meta...

Modeling distinct osteosarcoma subtypes in vivo using Cre:lox and lineage-restricted transgenic shRNA

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Conditional mouse osteosarcoma, dependent on p53 loss and potentiated by loss of Rb, mimics the human disease

“…To determine whether mOS reflects the molecular features of human OS, we undertook expression profiling using Affymetrix A430 2.0 gene chips. To enable a direct comparison between murine and human samples, we performed metagene projection analysis (Isakoff et al 2005;Tamayo et al 2007). Metagene projection analysis permits a robust cross-species and cross-platform comparison enabling an assessment of the degree to which mOS displays a transcriptional profile comparable to human OS, as distinguished from other human sarcomas.…”

“…Metagene analysis was performed essentially as described by Tamayo et al (2007) using the available program from http:// www.broad.mit.edu/cgi-bin/cancer/publications/pub_paper.cgi? mode=view&paper_id=161.…”

Automated multidimensional phenotypic profiling using large public microarray repositories

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