plasmut: Stratifying mutations observed in cell-free DNA and white blood cells as germline, hematopoietic, or somatic

Package: plasmut 1.11.0

Adith Arun

plasmut: Stratifying mutations observed in cell-free DNA and white blood cells as germline, hematopoietic, or somatic

A Bayesian method for quantifying the liklihood that a given plasma mutation arises from clonal hematopoesis or the underlying tumor. It requires sequencing data of the mutation in plasma and white blood cells with the number of distinct and mutant reads in both tissues. We implement a Monte Carlo importance sampling method to assess the likelihood that a mutation arises from the tumor relative to non-tumor origin.

Authors:Adith Arun [aut, cre], Robert Scharpf [aut]

plasmut_1.11.0.tar.gz
plasmut_1.11.0.zip(r-4.7)plasmut_1.11.0.zip(r-4.6)plasmut_1.11.0.zip(r-4.5)
plasmut_1.11.0.tgz(r-4.6-any)plasmut_1.11.0.tgz(r-4.5-any)
plasmut_1.11.0.tar.gz(r-4.7-any)plasmut_1.11.0.tar.gz(r-4.6-any)
plasmut_1.11.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
plasmut/json (API)

# Install 'plasmut' in R:

install.packages('plasmut', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

crcseq - This data is an example dataset to show how to use the package

On BioConductor:plasmut-1.11.0(bioc 3.24)plasmut-1.10.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bayesian somaticmutation germlinemutation sequencing

4.00 score 3 scripts 248 downloads 4 exports 15 dependencies

Last updated from:7b0b25f1fc. Checks:1 WARNING, 9 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	164
linux-devel-x86_64	OK	375
source / vignettes	OK	294
linux-release-x86_64	OK	224
macos-release-arm64	OK	102
macos-oldrel-arm64	OK	253
windows-devel	OK	100
windows-release	OK	161
windows-oldrel	OK	132
wasm-release	OK	152

Exports:importance_sampler model_w plasma_somatic wbc_somatic

Dependencies:cli dplyr generics glue lifecycle magrittr pillar pkgconfig R6 rlang tibble tidyselect utf8 vctrs withr

Modeling the origin of mutations identified in a liquid biopsy: cancer or clonal hematopoiesis?

Rendered fromplasmut.Rmdusingknitr::rmarkdown

Help page	Topics
This data is an example dataset to show how to use the package	crcseq
Importance sampler to estimate marginal likelihoods and Bayes factors	importance_sampler
Estimate the marginal likelihood that mutations in buffy coat and cfDNA reflect CH or correspond to germline mutations. If germline, the allele frequency should be 50 percent. The prior should be diffuse enough to handle CHIP mutations which are potentially way less than 50 percent	model_w
Estimate the marginal likelihood that variants identified in cell-free DNA are derived from tumor cells (ctDNA-derived)	plasma_somatic
Bayesian models for estimating the origin of a sequenced DNA fragment	plasmut
Estimate the marginal likelihood of observing somatic mutations from CTCs present in buffy coat p(y_w \| theta_w, n_w, model_S) x p(theta_w\| Model_S) theta_w \| model_S ~ beta(1, 999) ## sequencing error or CTC	wbc_somatic

Help page

Topics

This data is an example dataset to show how to use the package

crcseq

Importance sampler to estimate marginal likelihoods and Bayes factors

importance_sampler

Estimate the marginal likelihood that mutations in buffy coat and cfDNA reflect CH or correspond to germline mutations. If germline, the allele frequency should be 50 percent. The prior should be diffuse enough to handle CHIP mutations which are potentially way less than 50 percent

model_w

Estimate the marginal likelihood that variants identified in cell-free DNA are derived from tumor cells (ctDNA-derived)

plasma_somatic

Bayesian models for estimating the origin of a sequenced DNA fragment

plasmut