Package: cape 3.1.2

cape: Combined Analysis of Pleiotropy and Epistasis for Diversity Outbred Mice

Combined Analysis of Pleiotropy and Epistasis infers predictive networks between genetic variants and phenotypes. It can be used with standard two-parent populations as well as multi-parent populations, such as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the multi-parent advanced generation intercross (MAGIC) population of Arabidopsis thaliana. It uses complementary information of pleiotropic gene variants across different phenotypes to resolve models of epistatic interactions between alleles. To do this, cape reparametrizes main effect and interaction coefficients from pairwise variant regressions into directed influence parameters. These parameters describe how alleles influence each other, in terms of suppression and enhancement, as well as how gene variants influence phenotypes. All of the final interactions are reported as directed interactions between pairs of parental alleles. For detailed descriptions of the methods used in this package please see the following references. Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) <doi:10.1371/journal.pgen.1003010>. Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) <doi:10.1371/journal.pcbi.1003270>.

Authors:Anna Tyler [aut, cre], Jake Emerson [aut], Baha El Kassaby [aut], Ann Wells [aut], Georgi Kolishovski [aut], Vivek Philip [aut], Gregory Carter [aut]

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cape/json (API)
NEWS

# Install 'cape' in R:
install.packages('cape', repos = c('https://annaltyler.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

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

47 exports 0.09 score 61 dependencies 37 scripts 448 downloads

Last updated 8 months agofrom:e026435e2d. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024
R-4.5-winOKSep 13 2024
R-4.5-linuxOKSep 13 2024
R-4.4-winOKSep 13 2024
R-4.4-macOKSep 13 2024
R-4.3-winOKSep 13 2024
R-4.3-macOKSep 13 2024

Exports:calc_pCapecape2mppdirect_influenceerror_propget_covarget_eigentraitsget_genoget_marker_locationget_marker_nameget_networkget_pairs_for_pairscanget_phenoget_stats_multiallelehist_phenoimpute_missing_genokinshipload_input_and_run_capemarker2covarnorm_phenopairscanpheno2covarplink2capeplot_effectsplot_full_networkplot_networkplot_pairscanplot_pheno_corplot_singlescanplot_svdplot_variant_influencesqnorm_phenoqtl2_to_caperead_parametersread_populationremove_indremove_kin_indremove_markersremove_missing_genotype_dataremove_unused_markersrun_capeselect_eigentraitsselect_markers_for_pairscanselect_phenosinglescanwrite_populationwrite_variant_influences

Dependencies:abindbitbit64bitopsblobcachemcaToolsclicodetoolscolorspacecorpcorcpp11data.tableDBIdoParallelevdfarverfastmapffforeachgluegmmgtablehereigraphiteratorsjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmemoiseminpack.lmmunsellmvtnormpheatmappkgconfigplogrpracmapropagateqtlqtl2qtl2convertR6RColorBrewerRcppRcppEigenregressrlangrprojrootRSQLitesandwichscalesshapetmvtnormvctrsviridisLiteyamlzoo

cape: A package for the combined analysis of pleiotropy and epistasis

Anna L. Tyler^[The Jackson Laboratory, [email protected]], Jake Emerson^[The Jackson Laboratory, [email protected]], Baha El Kassaby^[The Jackson Laboratory, [email protected]], Ann Wells^[The Jackson Laboratory, [email protected]], Georgi Kolishovski^[The Jackson Laboratory, [email protected]], Vivek M. Philip^[The Jackson Laboratory, [email protected]],and Gregory W. Carter^[The Jackson Laboratory, [email protected]]

Rendered fromcape.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2024-01-10
Started: 2021-02-10

Readme and manuals

Help Manual

Help pageTopics
Error propagationcalc_delta_errors
Calculate empirical p-valuescalc_emp_p
Calculate P Values for Interactions Based on Permutationscalc_p
The CAPE data objectCape Cape-class
Converts a 'read_population' object to a multi-parent objectcape2mpp
Calculate the significance of direct influences of variant pairs on phenotypesdirect_influence
Estimate Errors of Regression Coefficientserror_prop
Get covariate informationget_covar
Calculate eigentraitsget_eigentraits
Gets the geno objectget_geno
Get marker genomic positionget_marker_location
Get marker namesget_marker_name
Convert the final results to an adjacency matrix.get_network
Select marker pairs for pairscanget_pairs_for_pairscan
Get the phenotype matrixget_pheno
Plot trait histogramshist_pheno
Impute missing genotype data using k nearest neighborsimpute_missing_geno
Calculate the kinship matrixkinship
Loads input and run CAPEload_input_and_run_cape
Creates a covariate from a genetic markermarker2covar
Mean-center and normalize phenotypesnorm_pheno
This function performs the pairwise scan on all markers.pairscan
Create a covariate from a traitpheno2covar
Convert plink2 files to cape formatplink2cape
Plot Interaction Effectsplot_effects
Plot the final epistatic network in a traditional network view.plot_full_network
Plots cape results as a circular networkplot_network
Plot the result of the pairwise scanplot_pairscan
Plot trait pairs against each otherplot_pheno_cor
Plot results of single-locus scansplot_singlescan
Plots eigentraitsplot_svd
Plot cape coefficientsplot_variant_influences
Plot trait distributionsqnorm_pheno
Convert qtl2 object to cape formatqtl2_to_cape
Read the parameter file, add missing entriesread_parameters
Reads in data in the R/qtl csv formatread_population
Remove individualsremove_ind
Removes individuals from the kinship object to match the cape.objremove_kin_ind
Removes genetic markersremove_markers
Removes individuals and/or markers with missing dataremove_missing_genotype_data
Take out markers not used in caperemove_unused_markers
Runs CAPErun_cape
Assign selected eigentraits in the Cape objectselect_eigentraits
Select markers for the pairwise scan.select_markers_for_pairscan
This function selects the phenotypes in a Cape objectselect_pheno
Runs marker regression on each individual genetic markersinglescan
Save the cross data in R/qtl CSV formatwrite_population
Write significant cape interactions to a csv filewrite_variant_influences