Package: mashr 0.2.79

Peter Carbonetto

mashr: Multivariate Adaptive Shrinkage

Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <doi:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.

Authors:Matthew Stephens [aut], Sarah Urbut [aut], Gao Wang [aut], Yuxin Zou [aut], Yunqi Yang [ctb], Sam Roweis [cph], David Hogg [cph], Jo Bovy [cph], Peter Carbonetto [aut, cre]

mashr_0.2.79.tar.gz
mashr_0.2.79.tar.gz(r-4.5-noble)mashr_0.2.79.tar.gz(r-4.4-noble)
mashr_0.2.79.tgz(r-4.4-emscripten)mashr_0.2.79.tgz(r-4.3-emscripten)
mashr.pdf |mashr.html✨
mashr/json (API)

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

Peer review:

Bug tracker:https://github.com/stephenslab/mashr/issues

Uses libs:
  • openblas– Optimized BLAS
  • gsl– GNU Scientific Library (GSL)
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

7.12 score 3 packages 616 scripts 492 downloads 6 mentions 28 exports 18 dependencies

Last updated 1 years agofrom:938c074868. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-linux-x86_64NOTENov 10 2024

Exports:contrast_matrixcov_canonicalcov_edcov_flashcov_pcacov_udiestimate_null_correlation_simpleextreme_deconvolutionget_estimated_piget_log10bfget_n_significant_conditionsget_pairwise_sharingget_pairwise_sharing_from_samplesget_samplesget_significant_resultsmashmash_1by1mash_compute_loglikmash_compute_posterior_matricesmash_compute_vloglikmash_estimate_corr_emmash_plot_metamash_set_datamash_update_datasim_contrast1sim_contrast2simple_simssimple_sims2

Dependencies:abindashrassertthatetrunctinvgammairlbalatticeMatrixmixsqpmvtnormplyrRcppRcppArmadilloRcppGSLrmetasoftImputeSQUAREMtruncnorm

mashr with common baseline

Yuxin Zou

Rendered fromintro_mashcommonbaseline.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2021-05-23
Started: 2020-06-09

mashr with common baseline at mean

Yuxin Zou

Rendered fromintro_mashbaselinemean.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2021-05-23
Started: 2021-05-23

Introduction to mashr

Matthew Stephens

Rendered fromintro_mash.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2022-12-07
Started: 2020-06-09

Accounting for correlations among measurements

Matthew Stephens

Rendered fromintro_correlations.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2022-12-07
Started: 2020-06-09

Introduction to mash: data-driven covariances

Matthew Stephens

Rendered fromintro_mash_dd.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2020-06-09
Started: 2020-06-09

Simulation with non-canonical matrices

Matthew Stephens

Rendered fromsimulate_noncanon.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2020-06-09
Started: 2020-06-09

Sample from mash posteriors

Yuxin Zou

Rendered frommash_sampling.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2022-01-25
Started: 2020-06-09

eQTL analysis outline

Matthew Stephens

Rendered fromeQTL_outline.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2022-12-07
Started: 2020-06-09

Readme and manuals

Help Manual

Help pageTopics
Create contrast matrixcontrast_matrix
Compute a list of canonical covariance matricescov_canonical
Perform "extreme deconvolution" (Bovy et al) on a subset of the datacov_ed
Perform Empirical Bayes Matrix Factorization using flashier, and return a list of candidate covariance matricescov_flash
Perform PCA on data and return list of candidate covariance matricescov_pca
Compute a list of covariance matrices corresponding to the "Unassociated", "Directly associated" and "Indirectly associated" modelscov_udi
Estimate null correlations (simple)estimate_null_correlation_simple
Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete dataextreme_deconvolution
Return the estimated mixture proportionsget_estimated_pi
Return the Bayes Factor for each effectget_log10bf
Count number of conditions each effect is significant inget_n_significant_conditions
Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior meanget_pairwise_sharing
Compute the proportion of (significant) signals shared by magnitude in each pair of conditionsget_pairwise_sharing_from_samples
Return samples from a mash objectget_samples
Find effects that are significant in at least one conditionget_significant_results
Apply mash method to datamash
Perform condition-by-condition analysesmash_1by1
Compute loglikelihood for fitted mash object on new data.mash_compute_loglik
Compute posterior matrices for fitted mash object on new datamash_compute_posterior_matrices
Compute vector of loglikelihood for fitted mash object on new datamash_compute_vloglik
Fit mash model and estimate residual correlations using EM algorithmmash_estimate_corr_em
Plot metaplot for an effect based on posterior from mashmash_plot_meta
Create a data object for mash analysis.mash_set_data
Update the data object for mash analysis.mash_update_data
Create simplest simulation, cj = mu 1 data used for contrast analysissim_contrast1
Create simulation with signal data used for contrast analysis.sim_contrast2
Create some simple simulated data for testing purposessimple_sims
Create some more simple simulated data for testing purposessimple_sims2