11 Papers
9 Citations
Rujin Wang is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Population & Normalization (statistics). The author has an hindex of 5, co-authored 8 publications.
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Papers
SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing
TL;DR: Evaluated on a diverse set of scDNA-seq data in cancer genomics and it is shown that SCOPE offers accurate copy-number estimates and successfully reconstructs subclonal structure.
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CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing
Yuchao Jiang,Rujin Wang,Eugene Urrutia,Ioannis N. Anastopoulos,Katherine L. Nathanson,Nan Zhang +5 more
TL;DR: CODEX2 is described, as a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples.
SCOPE: a normalization and copy number estimation method for single-cell DNA sequencing
TL;DR: SCOPE is proposed, a normalization and copy number estimation method for scDNA-seq data of cancer cells that can reliably recover 1% cancer cell spike-ins from a background of normal cells and successfully reconstructs cancer subclonal structure from ∼10,000 breast cancer cells.
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CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing
Yuchao Jiang,Rujin Wang,Eugene Urrutia,Ioannis N. Anastopoulos,Katherine L. Nathanson,Nan Zhang +5 more
TL;DR: CODEX2 is described, a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples.
Inferring relevant tissues and cell types for complex traits in genome-wide association studies
TL;DR: EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific omics measurements from single-cell sequencing, is presented and it is shown that EPIC outperforms existing methods.