Showing 8 of total 8 results (show query)
lcrawlab
mvMAPIT:Multivariate Genome Wide Marginal Epistasis Test
Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this package, we present the 'multivariate MArginal ePIstasis Test' ('mvMAPIT') – a multi-outcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact – thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search based methods. Our proposed 'mvMAPIT' builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate 'mvMAPIT' as a multivariate linear mixed model and develop a multi-trait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. Crawford et al. (2017) <doi:10.1371/journal.pgen.1006869>. Stamp et al. (2023) <doi:10.1093/g3journal/jkad118>.
Maintained by Julian Stamp. Last updated 5 months ago.
cppepistasisepistasis-analysisgwasgwas-toolslinear-mixed-modelsmapitmvmapitvariance-componentsopenblascppopenmp
11 stars 6.90 score 17 scripts 1 dependents
bioc
metaseqR2:An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms
Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.
Maintained by Panagiotis Moulos. Last updated 19 days ago.
softwaregeneexpressiondifferentialexpressionworkflowsteppreprocessingqualitycontrolnormalizationreportwritingrnaseqtranscriptionsequencingtranscriptomicsbayesianclusteringcellbiologybiomedicalinformaticsfunctionalgenomicssystemsbiologyimmunooncologyalternativesplicingdifferentialsplicingmultiplecomparisontimecoursedataimportatacseqepigeneticsregressionproprietaryplatformsgenesetenrichmentbatcheffectchipseq
7 stars 6.05 score 3 scriptslcrawlab
smer:Sparse Marginal Epistasis Test
The Sparse Marginal Epistasis Test is a computationally efficient genetics method which detects statistical epistasis in complex traits; see Stamp et al. (2025, <doi:10.1101/2025.01.11.632557>) for details.
Maintained by Julian Stamp. Last updated 2 months ago.
genomewideassociationepistasisgeneticssnplinearmixedmodelcppepistasis-analysisepistatisgwasgwas-toolsmapitzlibcppopenmp
1 stars 4.95 score 8 scriptsbioc
motifTestR:Perform key tests for binding motifs in sequence data
Taking a set of sequence motifs as PWMs, test a set of sequences for over-representation of these motifs, as well as any positional features within the set of motifs. Enrichment analysis can be undertaken using multiple statistical approaches. The package also contains core functions to prepare data for analysis, and to visualise results.
Maintained by Stevie Pederson. Last updated 23 days ago.
motifannotationchipseqchiponchipsequencematchingsoftware
1 stars 4.90 score 2 scriptsbioc
dce:Pathway Enrichment Based on Differential Causal Effects
Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG.
Maintained by Kim Philipp Jablonski. Last updated 3 months ago.
softwarestatisticalmethodgraphandnetworkregressiongeneexpressiondifferentialexpressionnetworkenrichmentnetworkkeggbioconductorcausality
13 stars 4.59 score 4 scriptsbioc
HERON:Hierarchical Epitope pROtein biNding
HERON is a software package for analyzing peptide binding array data. In addition to identifying significant binding probes, HERON also provides functions for finding epitopes (string of consecutive peptides within a protein). HERON also calculates significance on the probe, epitope, and protein level by employing meta p-value methods. HERON is designed for obtaining calls on the sample level and calculates fractions of hits for different conditions.
Maintained by Sean McIlwain. Last updated 5 months ago.
1 stars 4.18 score 6 scripts
codingmylife
PLStests:Model Checking for High-Dimensional GLMs via Random Projections
Provides methods for testing the goodness-of-fit of generalized linear models (GLMs) using random projections. It is specifically designed for high-dimensional scenarios where the number of predictors substantially exceeds the sample size. The statistical methodologies implemented in this package are detailed in the paper by Wen Chen and Falong Tan (2024, <doi:10.48550/arXiv.2412.10721>).
Maintained by Wen Chen. Last updated 23 days ago.
1.30 score