Here, we propose a new method (CLARITY; Clustering with Local shApe-based similaRITY) for the analysis of microarray time course experiments that uses a local shape-based similarity measure based on Spearman rank correlation. This measure does not require a normalization of the expression data and is comparably robust towards noise. It is also able to detect similar and even time-shifted sub-profiles. To this end, we implemented an approach motivated by the BLAST algorithm for sequence alignment. We used CLARITY to cluster the times series of gene expression data during the mitotic cell cycle of the yeast Saccharomyces cerevisiae. The obtained clusters were related to the MIPS functional classification to assess their biological significance. We found that several clusters were significantly enriched with genes that share similar or related functions.

No abstract

Introduction of fluorescence-based Real-Time PCR (RT-PCR) also called qPCR is increasingly used to detect multiple pathogens simultaneously and rapidly by gene expression analysis of PCR amplification data. Real-time PCR data are analyzed after setting an arbitrary threshold that must intersect the signal curve in its exponential phase. The point at which the curve crosses the threshold is called CT (Cycle Threshold). This simple and arbitrary value however is not an elegant definition of CT value sometimes leads to conclusions that are either false positive or negative. Therefore, the purpose of this paper is to present a stable and consistent alternative approach for the definition and determination of CT value that leads to near zero false positives and negatives.