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teng-gao committed Sep 19, 2024
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Title: Haplotype-Aware CNV Analysis from scRNA-Seq
URL: https://github.com/kharchenkolab/numbat/, https://kharchenkolab.github.io/numbat/
Version: 1.4.2
Authors@R: c(person("Teng","Gao", email="[email protected]", role=c("cre", "aut")), person("Ruslan", "Soldatov", email="[email protected]", role="aut"), person("Hirak", "Sarkar", email="[email protected]", role="aut"), person("Evan", "Biederstedt", email="[email protected]", role="aut"), person("Peter", "Kharchenko", email = "[email protected]", role = "aut"))
Authors@R: c(person("Teng","Gao", email="[email protected]", role=c("cre", "aut")), person("Ruslan", "Soldatov", email="[email protected]", role="aut"), person("Hirak", "Sarkar", email="[email protected]", role="aut"), person("Evan", "Biederstedt", email="[email protected]", role="aut"), person("Peter", "Kharchenko", email = "[email protected]", role = "aut"))
Description: A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at <https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) <doi:10.1038/s41587-022-01468-y>.
License: MIT + file LICENSE
Encoding: UTF-8
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