CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression

Detection of DNA copy number alteration in cancer cells is critical to understanding cancer initiation and progression. Widely used methods, such as DNA arrays and genomic DNA sequencing, are relatively expensive and require DNA samples at a microgram level, which are not avaiblable in certain situations like clinical biopsies or single-cell genomes. Here, we developed an alternative method-CNAPE to computationally infer copy number alterations from gene expression data. A prior knowledge-aided machine learning model was proposed, trained and tested on 9,740 cancer samples from The Cancer Genome Atlas. We then applied CNAPE to study gliomas, the most common and aggressive brain cancer in adult. Particularly, using RNA sequencing data, CNAPE respectively predicted DNA copy number of chromosomes, chromosomal arms, and 12 commonly altered genes, and achieved over 80 percent accuracy in almost all broad regions and some focal regions. CNAPE was developed as an easy-to-use tool at https://github.com/WangLabHKUST/CNAPE.