Abstract: A method of detecting mutational signatures of a sample and their exposures in a collection of samples, each being characterized by nucleic acid sequencing information describing at least one mutation, comprises: clustering the samples to provide clusters and respective exposure vectors, where each exposure vector describes prior probabilities for a plurality of signatures to emit a mutation. An optimization procedure is applied to dynamically re-cluster the samples and to dynamically update the signatures and exposure vectors. Optionally, the mutational signatures in the sample are determined using an exposure vector of one or more clusters associated with the sample.

Abstract: A method of classifying a plurality of elements, such as genes, an associated system and an associated computer-read-able storage medium. Similarity values for pairs of elements are measured. For example, in the case of genes, gene expression fingerprints are measured, and the similarity values are computed from the fingerprints. A graph is constructed such that each vertex of the graph corresponds to a respective element. Each edge of the graph is assigned a superbinary weight that is based on the corresponding similarity value. The graph is partitioned into kernels, and the kernels are merged into clusters. Preferably, the superbinary weights are based on the similarity values according to a probabilistic model. The system of the present invention includes an apparatus for measuring the similarity values, a memory for storing the similarity values, and a proccesor for implementing the method of the present invention.