Convergence of alternating optimization
Neural, Parallel & Scientific Computations
Analyzing time series gene expression data
Bioinformatics
Clustering short time series gene expression data
Bioinformatics
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Modern high-throughput technologies like microarray, mass spectrometry or next generation sequencing enable biologists to measure cell products like metabolites, peptides, proteins or mRNA. With the advance of the technologies there are more and more experiments that do not only compare the cell products under two or more specific conditions, but also track them over time. These experiments usually yield short time series for a large number of cell products, but with only a few replicates. The noise in the measurements, but also the often strong biological variation of the replicates makes a coherent analysis of such data difficult. In this paper, we focus on methods to correct measurement errors or deviations caused by biological variation in terms of a time shift, different reaction speed and different reaction intensity for replicates. We propose a regression model that can estimate corresponding parameters that can be used to correct the data and to obtain better results in the further analysis.