2018
DOI: 10.1101/426122
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CaSpER: Identification, visualization and integrative analysis of CNV events in multiscale resolution using single-cell or bulk RNA sequencing data

Abstract: RNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNV). Here, we propose CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER performs smoothing of… Show more

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Cited by 2 publications
(3 citation statements)
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“…Ginkgo [30] uses Circular Binary Segmentation (CBS) [7] to segment the genome, followed by inferring the integer value of the absolute copy number. It is worth noting that some methods designed for aCGH and NGS data have also been extensively used on single-cell data [31][32][33][34]. One such method is HMMcopy [35].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Ginkgo [30] uses Circular Binary Segmentation (CBS) [7] to segment the genome, followed by inferring the integer value of the absolute copy number. It is worth noting that some methods designed for aCGH and NGS data have also been extensively used on single-cell data [31][32][33][34]. One such method is HMMcopy [35].…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, HMMcopy was designed for aCGH data originally, and thus does not take into account the specific error profiles that characterize single-cell sequencing data, such as low and uneven coverage, or the computational challenges that arise due to biological processes such as aneuploidy in a tumor single cell. While these three methods have been widely applied to analyze single-cell data [27,[31][32][33][34][37][38][39][40][41][42][43][44][45][46][47][48], a comprehensive study of their performance is currently lacking. While Knouse et al [32] assessed the performance of CBS and HMM-based methods on single-cell DNA sequencing data, their evaluation is limited to CNVs in brain and skin cells.…”
Section: Introductionmentioning
confidence: 99%
“…Although many tools identify CNVs from exome sequencing data, there is a lack of methods for detecting CNVs solely from RNA sequencing data [7,20]. We developed one of the first methods that identifies, visualizes and integrates CNV events using scRNA-Seq data [21]. In addition to CNVs, we also need to estimate SNPs and indels from scRNA-Seq data for understanding the clonal architecture of the tumor.…”
mentioning
confidence: 99%