Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. We benchmarked three widely used methods-Ginkgo, HMMcopy, and CopyNumber-on simulated as well as real datasets. To facilitate this, we developed a novel simulator of single-cell genome evolution in the presence of CNAs. Furthermore, to assess performance on empirical data where the ground truth is unknown, we introduce a phylogeny-based measure for identifying potentially erroneous inferences. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, our findings show that even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.

Single-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker.

UNLABELLED: PROBER is an oligonucleotide primer design software application that designs multiple primer pairs for generating PCR probes useful for fluorescence in situ hybridization (FISH). PROBER generates Tiling Oligonucleotide Probes (TOPs) by masking repetitive genomic sequences and delineating essentially unique regions that can be amplified to yield small (100-2000 bp) DNA probes that in aggregate will generate a single, strong fluorescent signal for regions as small as a single gene. TOPs are an alternative to bacterial artificial chromosomes (BACs) that are commonly used for FISH but may be unstable, unavailable, chimeric, or non-specific to small (10-100 kb) genomic regions. PROBER can be applied to any genomic locus, with the limitation that the locus must contain at least 10 kb of essentially unique blocks. To test the software, we designed a number of probes for genomic amplifications and hemizygous deletions that were initially detected by Representational Oligonucleotide Microarray Analysis of breast cancer tumors. AVAILABILITY: http://prober.cshl.edu