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Package: numbat | ||
Title: Haplotype-Aware CNV Analysis from scRNA-Seq | ||
URL: https://github.com/kharchenkolab/numbat/, https://kharchenkolab.github.io/numbat/ | ||
Version: 1.3.4 | ||
Version: 1.4.0 | ||
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 | ||
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@@ -21,6 +21,7 @@ Imports: | |
ggraph, | ||
ggtree, | ||
glue, | ||
hahmmr, | ||
igraph, | ||
IRanges, | ||
logger, | ||
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# Generated by using Rcpp::compileAttributes() -> do not edit by hand | ||
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 | ||
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cppdbbinom <- function(x, size, alpha, beta, log_prob = FALSE) { | ||
.Call('_numbat_cppdbbinom', PACKAGE = 'numbat', x, size, alpha, beta, log_prob) | ||
} | ||
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cpp_dgpois <- function(x, alpha, beta, log_prob = FALSE) { | ||
.Call('_numbat_cpp_dgpois', PACKAGE = 'numbat', x, alpha, beta, log_prob) | ||
} | ||
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logSumExp <- function(x) { | ||
.Call('_numbat_logSumExp', PACKAGE = 'numbat', x) | ||
} | ||
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likelihood_compute <- function(logphi, logprob, logPi, n, m) { | ||
.Call('_numbat_likelihood_compute', PACKAGE = 'numbat', logphi, logprob, logPi, n, m) | ||
} | ||
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forward_backward_compute <- function(logphi, logprob, logPi, n, m) { | ||
.Call('_numbat_forward_backward_compute', PACKAGE = 'numbat', logphi, logprob, logPi, n, m) | ||
} | ||
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viterbi_compute <- function(log_delta, logprob, logPi, n, m, nu, z) { | ||
.Call('_numbat_viterbi_compute', PACKAGE = 'numbat', log_delta, logprob, logPi, n, m, nu, z) | ||
} | ||
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roman2int_internal <- function(letters, nchar) { | ||
.Call('_numbat_roman2int_internal', PACKAGE = 'numbat', letters, nchar) | ||
} | ||
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fit_lnpois_cpp <- function(Y_obs, lambda_ref, d) { | ||
.Call('_numbat_fit_lnpois_cpp', PACKAGE = 'numbat', Y_obs, lambda_ref, d) | ||
} | ||
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poilog1 <- function(x, my, sig) { | ||
.Call('_numbat_poilog1', PACKAGE = 'numbat', x, my, sig) | ||
} | ||
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l_lnpois_cpp <- function(Y_obs, lambda_ref, d, mu, sig, phi = 1.0) { | ||
.Call('_numbat_l_lnpois_cpp', PACKAGE = 'numbat', Y_obs, lambda_ref, d, mu, sig, phi) | ||
.Call(`_numbat_roman2int_internal`, letters, nchar) | ||
} | ||
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