title: Traveling on discrete embeddings of gene expression.
abstract: 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).
  
  
  
  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.
  
  
  
  A thorough evaluation on several gene expression datasets demonstrate the suitability of the proposed approach from a twofold 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 (with a stability always above 0.87); clinically, by confirming that many of the genes highlighted by the model as significant play also a key role for cancer biology.
  
  
  
  The proposed framework can be successfully exploited to meaningfully visualize the samples; detect medically relevant genes; properly classify samples.
authors.affiliation: Department of Computer Science, University of Verona, Strada le Grazie 15, 37134 Verona, Italy. Electronic address: pietro.lovato@univr.it.
authors.forename: Pietro
authors.initials: P
authors.lastname: Lovato
authors.affiliation: Department of Computer Science, University of Verona, Strada le Grazie 15, 37134 Verona, Italy.
authors.forename: Manuele
authors.initials: M
authors.lastname: Bicego
authors.affiliation: Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia.
authors.forename: Maria
authors.initials: M
authors.lastname: Kesa
authors.affiliation: Microsoft Research, One Microsoft Way, 98052 Redmond, WA, USA.
authors.forename: Nebojsa
authors.initials: N
authors.lastname: Jojic
authors.affiliation: Pattern Analysis and Computer Vision (PAVIS), Istituto Italiano di Tecnologia (IIT), Via Morego 30, 16163 Genova, Italy.
authors.forename: Vittorio
authors.initials: V
authors.lastname: Murino
authors.affiliation: Microsoft Research, One Microsoft Way, 98052 Redmond, WA, USA.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Perina
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D057225:Data Mining; D020869:Gene Expression Profiling; D052138:Genes, Neoplasm; D006801:Humans; D009369:Neoplasms / Q000235:genetics
keywords: Bag of words classification; Bag of words clustering; Data visualization; Gene expression matrix; Gene selection