A general framework for analyzing data from two short time-series microarray experiments.
We propose a general theoretical framework for analyzing differentially expressed genes and behavior patterns from two homogenous short time-course data. The framework generalizes the recently proposed Hilbert-Schmidt Independence Criterion (HSIC)-based framework adapting it to the time-series scenario by utilizing tensor analysis for data transformation. The proposed framework is effective in yielding criteria that can identify both the differentially expressed genes and time-course patterns of interest between two time-series experiments without requiring to explicitly cluster the data. The results, obtained by applying the proposed framework with a linear kernel formulation, on various data sets are found to be both biologically meaningful and consistent with published studies.
Centre for Intelligent Machines, McGill University, McConnell Engineering Building, Room 444, 3480, University Street, Montreal, QC H3A 2A7, Canada. mohak@cim.mcgill.ca
Mohak
M
Shah
Jacques
J
Corbeil
D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D005796:Genes / Q000502:physiology; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D013997:Time Factors