fredericfleche

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Gene expression data generated by high-throughput approaches, such as microarrays and next generation sequencing, play a central role in biological knowledge discovery. However, the size and complexity of these type of data make their analysis challenging. Often the aim of these experiments is to identify patterns of gene expression and to define sets of co-regulated genes. For these purposes clustering algorithms (eg, hierarchical clustering and k-means clustering) are frequently used. Although these methods have proved a powerful and efficient way to analyse gene expression data, they have limitations. One weakness is that they generally produce sharp delineations between clusters of co-expressed genes and different methods often result in very different classifications; the validity and the logic of any classification are rarely obvious or possible to investigate. A second drawback is that clustering algorithms do not reveal global patterns in the data and it is usually difficult .. - fredericfleche