A fast variational algorithm to detect the clonal copy number substructure of tumors from single-cell data
- 1. Department of Electrical Engineering and Information Technology (DIETI) University of Naples "Federico II", 80128 Naples, Italy and BIOGEM Institute of Molecular Biology and Genetics, 83031 Ariano Irpino, Italy
- 2. Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, 5 Yiheyuan Rd, Haidian District, 100871 Beijing, China
- 3. Institute for Cancer Genetics, Columbia University, 1130 St Nicholas Ave, New York, NY 10032, USA
Description
Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast variational algorithm for the deconvolution of the clonal substructure of tumors from single cell data. It uses a multichannel segmentation algorithm exploiting the assumption that all the cells in a given copy number clone share the same breakpoints. Thus, the smoothed expression profile of every individual cell constitutes part of the evidence of the copy number profile in each subclone. SCEVAN can automatically and accurately discriminate between malignant and non-malignant cells, resulting in a practical framework to analyze tumors and their microenvironment. We apply SCEVAN to several datasets encompassing 106 samples and 93,322 cells from different tumors types and technologies. We demonstrate its application to characterize the intratumor heterogeneity and geographic evolution of malignant brain tumors.