slurmR - A Lightweight Wrapper for 'Slurm'

'Slurm', Simple Linux Utility for Resource Management <https://slurm.schedmd.com/>, is a popular 'Linux' based software used to schedule jobs in 'HPC' (High Performance Computing) clusters. This R package provides a specialized lightweight wrapper of 'Slurm' with a syntax similar to that found in the 'parallel' R package. The package also includes a method for creating socket cluster objects spanning multiple nodes that can be used with the 'parallel' package.

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bioinformaticshpcslurm

7.63 score 61 stars 232 scripts 277 downloads

partition - Agglomerative Partitioning Framework for Dimension Reduction

A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>.

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data-reductiondimensionality-reductionpartitional-clusteringopenblascpp

7.29 score 37 stars 1 dependents 29 scripts 216 downloads

fmcmc - A friendly MCMC framework

Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.

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adaptivebayesian-inferencemarkov-chain-monte-carlomcmcmetropolis-hastingsparallel-computing

7.22 score 16 stars 1 dependents 58 scripts 319 downloads

aphylo - Statistical Inference and Prediction of Annotations in Phylogenetic Trees

Implements a parsimonious evolutionary model to analyze and predict gene-functional annotations in phylogenetic trees as described in Vega Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. Focusing on computational efficiency, 'aphylo' makes it possible to estimate pooled phylogenetic models, including thousands (hundreds) of annotations (trees) in the same run. The package also provides the tools for visualization of annotated phylogenies, calculation of posterior probabilities (prediction) and goodness-of-fit assessment featured in Vega Yon et al. (2021).

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annotationsinferencephylogeneticsrcpparmadillocpp

6.49 score 6 stars 104 scripts 646 downloads

HiLDA - Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation

A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization.

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softwaresomaticmutationsequencingstatisticalmethodbayesianmutational-signaturesrjagssomatic-mutationscppjags

5.56 score 3 stars 1 dependents 7 scripts 370 downloads

LUCIDus - Latent Unknown Clustering Integrating Multi-View Data

An implementation of the LUCID model (Peng (2019) <doi:10.1093/bioinformatics/btz667>). LUCID conducts integrated clustering using exposures, omics data (and outcome as an option). An EM algorithm is implemented to estimate MLE of the LUCID model. 'LUCIDus' features integrated variable selection, incorporation of missing omics data, bootstrap inference, prediction and visualization of the model.

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5.35 score 7 stars 32 scripts 609 downloads

hJAM - Hierarchical Joint Analysis of Marginal Summary Statistics

Provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." <bioRxiv><doi:10.1101/2020.02.03.924241>.

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5.18 score 10 stars 5 scripts 202 downloads

xrnet - Hierarchical Regularized Regression

Fits hierarchical regularized regression models to incorporate potentially informative external data, Weaver and Lewinger (2019) <doi:10.21105/joss.01761>. Utilizes coordinate descent to efficiently fit regularized regression models both with and without external information with the most common penalties used in practice (i.e. ridge, lasso, elastic net). Support for standard R matrices, sparse matrices and big.matrix objects.

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cpp

4.65 score 10 stars 1 dependents 10 scripts 197 downloads

selectKSigs - Selecting the number of mutational signatures using a perplexity-based measure and cross-validation

A package to suggest the number of mutational signatures in a collection of somatic mutations using calculating the cross-validated perplexity score.

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softwaresomaticmutationsequencingstatisticalmethodclusteringmutational-signaturesrjagssomatic-mutationscppjags

4.08 score 3 stars 7 scripts 308 downloads

barry - Your Go-to Motif Accountant

Provides the 'C++' header-only library 'barry' for use in R packages. 'barry' is a 'C++' template library for counting sufficient statistics on binary arrays and building discrete exponential-family models. It provides tools for sparse arrays, user-defined count statistics, support set constraints, power set generation, and includes modules for Discrete Exponential Family Models (DEFMs) and network statistics. By placing these headers in this package, we offer an efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. This package follows the same approach as the 'BH' package which provides 'Boost' headers for R packages.

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motifsr-libraryr-programmingstatistics

3.95 score 2 dependents 274 downloads

cit - Causal Inference Test

A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), <DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.

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gslcpp

3.86 score 2 stars 36 scripts 289 downloads

methcon5 - Identify and Rank CpG DNA Methylation Conservation Along the Human Genome

Identify and rank CpG DNA methylation conservation along the human genome. Specifically it includes bootstrapping methods to provide ranking which should adjust for the differences in length as without it short regions tend to get higher conservation scores.

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2.70 score 6 scripts 179 downloads

modACDC - Association of Covariance for Detecting Differential Co-Expression

A series of functions to implement association of covariance for detecting differential co-expression (ACDC), a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Users can use the default method which identifies modules by Partition or may supply their own modules. Also included are functions to choose an information loss criterion (ILC) for Partition using OmicS-data-based Complex trait Analysis (OSCA) and Genome-wide Complex trait Analysis (GCTA). The manuscript describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. "ACDC: a general approach for detecting phenotype or exposure associated co-expression" (2023) <doi:10.3389/fmed.2023.1118824>.

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2.70 score 1 stars 241 downloads