- recipe bioconductor-genesis
GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness
- Homepage:
https://bioconductor.org/packages/3.18/bioc/html/GENESIS.html
- License:
GPL-3
- Recipe:
The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes.
- package bioconductor-genesis¶
- versions:
2.32.0-0,2.30.0-0,2.28.0-1,2.28.0-0,2.24.2-0,2.24.1-0,2.24.0-0,2.22.1-0,2.20.1-0,2.32.0-0,2.30.0-0,2.28.0-1,2.28.0-0,2.24.2-0,2.24.1-0,2.24.0-0,2.22.1-0,2.20.1-0,2.20.0-0,2.18.0-0,2.16.0-0,2.14.3-0,2.12.0-0- depends bioconductor-biobase:
>=2.62.0,<2.63.0- depends bioconductor-biobase:
>=2.62.0,<2.63.0a0- depends bioconductor-biocgenerics:
>=0.48.0,<0.49.0- depends bioconductor-biocgenerics:
>=0.48.1,<0.49.0a0- depends bioconductor-biocparallel:
>=1.36.0,<1.37.0- depends bioconductor-biocparallel:
>=1.36.0,<1.37.0a0- depends bioconductor-gdsfmt:
>=1.38.0,<1.39.0- depends bioconductor-gdsfmt:
>=1.38.0,<1.39.0a0- depends bioconductor-genomicranges:
>=1.54.0,<1.55.0- depends bioconductor-genomicranges:
>=1.54.1,<1.55.0a0- depends bioconductor-gwastools:
>=1.48.0,<1.49.0- depends bioconductor-gwastools:
>=1.48.0,<1.49.0a0- depends bioconductor-iranges:
>=2.36.0,<2.37.0- depends bioconductor-iranges:
>=2.36.0,<2.37.0a0- depends bioconductor-s4vectors:
>=0.40.0,<0.41.0- depends bioconductor-s4vectors:
>=0.40.2,<0.41.0a0- depends bioconductor-seqarray:
>=1.42.0,<1.43.0- depends bioconductor-seqarray:
>=1.42.0,<1.43.0a0- depends bioconductor-seqvartools:
>=1.40.0,<1.41.0- depends bioconductor-seqvartools:
>=1.40.0,<1.41.0a0- depends bioconductor-snprelate:
>=1.36.0,<1.37.0- depends bioconductor-snprelate:
>=1.36.0,<1.37.0a0- depends libblas:
>=3.9.0,<4.0a0- depends libgcc-ng:
>=12- depends liblapack:
>=3.9.0,<4.0a0- depends r-base:
>=4.3,<4.4.0a0- depends r-data.table:
- depends r-igraph:
- depends r-matrix:
- depends r-reshape2:
- requirements:
Installation
You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).
While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.
Given that you already have a conda environment in which you want to have this package, install with:
mamba install bioconductor-genesis and update with:: mamba update bioconductor-genesis
To create a new environment, run:
mamba create --name myenvname bioconductor-genesis
with
myenvnamebeing a reasonable name for the environment (see e.g. the mamba docs for details and further options).Alternatively, use the docker container:
docker pull quay.io/biocontainers/bioconductor-genesis:<tag> (see `bioconductor-genesis/tags`_ for valid values for ``<tag>``)
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
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