Motivation
Single-cell sequencing (SCS) data provide unprecedented insights into intratumoral heterogeneity. With SCS, we can better characterize clonal genotypes and reconstruct phylogenetic relationships of tumor cells/clones. However, SCS data are often error-prone, making their computational analysis challenging.
Results
To infer the clonal evolution in tumor from the error-prone SCS data, we developed an efficient computational framework, termed RobustClone. It recovers the true genotypes of subclones based on the extended robust principal component analysis, a low-rank matrix decomposition method, and reconstructs the subclonal evolutionary tree. RobustClone is a model-free method, which can be applied to both single-cell single nucleotide variation (scSNV) and single-cell copy-number variation (scCNV) data. It is efficient and scalable to large-scale datasets. We conducted a set of systematic evaluations on simulated datasets and demonstrated that RobustClone outperforms state-of-the-art methods in large-scale data both in accuracy and efficiency. We further validated RobustClone on two scSNV and two scCNV datasets and demonstrated that RobustClone could recover genotype matrix and infer the subclonal evolution tree accurately under various scenarios. In particular, RobustClone revealed the spatial progression patterns of subclonal evolution on the large-scale 10X Genomics scCNV breast cancer dataset.
Availability and implementation
RobustClone software is available at https://github.com/ucasdp/RobustClone.
Contact
lwan@amss.ac.cn or maliang@ioz.ac.cn
Supplementary information
Supplementary data are available at Bioinformatics online.
PTMiner post-processes the coarse and error-prone results of an open search of MS/MS spectra. It confidently filters and localizes the modifications (mass shifts) using the transfer FDR and an empirical Bayesian method. Evaluated on simulated and synthetic peptide data, PT-Miner showed much higher accuracy than two open search engines and the Ascore algorithm. PTMiner was used to comprehensively characterize the PTMs in a draft map of human proteome, resulting in over 1.7 million modifications confidently identified and localized.
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