Post-doctoral researcher in Computer Science
University of Verona (ITALY)
Pietro Lovato, Manuele Bicego, Maria Kesa, Nebojsa Jojic, Vittorio Murino, Alessandro Perina
Objective: High-throughput technologies have generated an unprecedented amount of high-dimensional gene expression data. Algorithmic approaches could be extremely useful to distill information and derive compact interpretable representations of the statistical patterns present in the data. This paper proposes a mining approach to extract an informative representation of gene expression profiles based on a generative model called the counting grid (CG).
Method: Using the CG model, gene expression values are arranged on a discrete grid, learned in a way that "similar" co-expression patterns are arranged in close proximity, thus resulting in an intuitive visualization of the dataset. More than this, the model permits to identify the genes that distinguish between classes (e.g. different types of cancer). Finally, each sample can be characterized with a discriminative signature -- extracted from the model -- that can be effectively employed for classification.
Results: A thorough evaluation on several gene expression datasets demonstrate the suitability of the proposed approach from a two-fold perspective: numerically, we reached state-of-the-art classification accuracies on 5 datasets out of 7, and similar results when the approach is tested in a gene selection setting; clinically, by confirming that many of the genes highlighted by the model as significant play also a key role for cancer biology.
Conclusion: The proposed framework can be successfully exploited to meaningfully visualize the samples; detect medically relevant genes; properly classify samples.
You can download the matlab source code, along with a demo, here.
The code is provided on an "as is" without support or guarantees.
(C) Pietro Lovato 2016
Dipartimento di Informatica
Università degli Studi di Verona
Ca' Vignal 2, Strada Le Grazie 15, 37134 Verona, Italy