The paper introduces a computational method called ABSOLUTE, which infers tumor purity and malignant cell ploidy directly from somatic DNA alterations. ABSOLUTE can detect subclonal heterogeneity and somatic homozygosity, and calculate statistical sensitivity for detecting specific aberrations. The method was applied to analyze exome sequencing data from 214 ovarian carcinoma tumor-normal pairs, identifying pervasive subclonal somatic point mutations and a subset of predominantly clonal and homozygous mutations in tumor suppressor genes *TP53* and *NF1*. ABSOLUTE was also used to infer absolute allelic copy-number profiles from 3,155 diverse cancer specimens, revealing that genome-doubling events are common in human cancer and influence tumor progression. The authors validated the purity and ploidy predictions of ABSOLUTE using Affymetrix SNP microarray data and DNA-mixing experiments, demonstrating its accuracy and applicability to various cancer types. The method provides a foundation for integrative genomic analysis of cancer genome alterations and facilitates the design of clinical sequencing studies and studies of cancer genome evolution and intra-tumor heterogeneity.The paper introduces a computational method called ABSOLUTE, which infers tumor purity and malignant cell ploidy directly from somatic DNA alterations. ABSOLUTE can detect subclonal heterogeneity and somatic homozygosity, and calculate statistical sensitivity for detecting specific aberrations. The method was applied to analyze exome sequencing data from 214 ovarian carcinoma tumor-normal pairs, identifying pervasive subclonal somatic point mutations and a subset of predominantly clonal and homozygous mutations in tumor suppressor genes *TP53* and *NF1*. ABSOLUTE was also used to infer absolute allelic copy-number profiles from 3,155 diverse cancer specimens, revealing that genome-doubling events are common in human cancer and influence tumor progression. The authors validated the purity and ploidy predictions of ABSOLUTE using Affymetrix SNP microarray data and DNA-mixing experiments, demonstrating its accuracy and applicability to various cancer types. The method provides a foundation for integrative genomic analysis of cancer genome alterations and facilitates the design of clinical sequencing studies and studies of cancer genome evolution and intra-tumor heterogeneity.