Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Sanguinetti, Guido
IMPACT FACTOR 2017: 0.812
5-year IMPACT FACTOR: 1.104
CiteScore 2017: 0.86
SCImago Journal Rank (SJR) 2017: 0.456
Source Normalized Impact per Paper (SNIP) 2017: 0.527
Mathematical Citation Quotient (MCQ) 2017: 0.04
Integration of data from multiple omics platforms has become a major challenge in studying complex systems and traits. For integrating data from multiple platforms, the underlying spaces from which the top ranked elements come from are likely to be different. Thus, taking the underlying spaces into consideration explicitly is important, as failure to do so would lead to inefficient use of data and might render biases and/or sub-optimal results. We propose two space oriented classes of heuristic algorithms for integrating ranked lists from omic scale data. These algorithms are either Borda inspired or Markov chain based that take the underlying spaces of the individual ranked lists into account explicitly. We applied this set of algorithms to a number of problems, including one that aims at aggregating results from three cDNA and two Affymetrix gene expression studies in which the underlying spaces between Affymetrix and cDNA platforms are clearly different.
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