Author: Jun Peng [aut, cre], Bo Li [aut], Jorge More [ctb], Burton Garbow [ctb], Kenneth Hillstrom [ctb], John Burkardt [ctb], Paul Gilbert [ctb], Ravi Varadhan [ctb]
fitting, decay curve decomposition, statistical age model optimization, and statistical

dating, such as: equivalent dose calculation, dose rate determination, growth curve fitting,
decay curve decomposition, statistical age model optimization, and statistical plot visualization.

Title: Haplotype-Aware CNV Analysis from scRNA-Seq
Authors@R: c(person("Teng","Gao", email="tgao@g.harvard.edu", role=c("cre", "aut")), person("Hirak", "Sarkar", email="hirak_sarkar@hms.harvard.edu", role="aut"), person("Evan", "Biederstedt", email="evan.biederstedt@gmail.com", role="aut"), person("Peter", "Kharchenko", email = "peter_kharchenko@hms.harvard.edu", 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. (2022) <doi:10.1101/2022.02.07.479314>.
Imports: ape, argparse, caTools, data.table, dendextend, dplyr,
GenomicRanges, ggplot2, ggraph, ggtree, glue, igraph, IRanges,
logger, magrittr, methods, parallel, parallelDist, patchwork,
pryr, purrr, Rcpp, RcppParallel, RhpcBLASctl, R.utils,
reshape2, scales, stats4, stringr, tibble, tidygraph, tidyr,
Suggests: ggrastr, ggrepel, knitr, matrixStats, testthat (>= 3.0.0),
LinkingTo: Rcpp, RcppArmadillo, RcppParallel, roptim