Motivation: Several cancer types consist of multiple genetically and phenotypically distinct subpopulations. The underlying mechanism for this intra-tumoral heterogeneity can be explained by the clonal evolution model, whereby growth advantageous mutations cause the expansion of cancer cell subclones. The recurrent phenotype of many cancers may be a consequence of these coexisting subpopulations responding unequally to therapies. Methods to computationally infer tumor evolution and subpopulation diversity are emerging and they hold the promise to improve the understanding of genetic and molecular determinants of recurrence. Results: To address cellular subpopulation dynamics within human tumors, we developed a bioinformatic method, EXPANDS. It estimates the proportion of cells harboring specific mutations in a tumor. By modeling cellular frequencies as probability distributions, EXPANDS predicts mutations that accumulate in a cell before its clonal expansion. We assessed the performance of EXPANDS on one whole genome sequenced breast cancer and performed SP analyses on 118 glioblastoma multiforme samples obtained from TCGA. Our results inform about the extent of subclonal diversity in primary glioblastoma, subpopulation dynamics during recurrence and provide a set of candidate genes mutated in the most well-adapted subpopulations. In summary, EXPANDS predicts tumor purity and subclonal composition from sequencing data.