GPA - GPA (Genetic analysis incorporating Pleiotropy and Annotation)
This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture.
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softwarestatisticalmethodclassificationgenomewideassociationsnpgeneticsclusteringmultiplecomparisonpreprocessinggeneexpressiondifferentialexpressioncpp
6.43 score 16 stars 17 scripts 292 downloadsgpaExample - Example data for the GPA package (Genetic analysis incorporating Pleiotropy and Annotation)
Example data for the GPA package, consisting of the p-values of 1,219,805 SNPs for five psychiatric disorder GWAS from the psychiatric GWAS consortium (PGC), with the annotation data using genes preferentially expressed in the central nervous system (CNS).
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experimentdatahomo_sapiens_datasnpdatadatapkg
4.20 score 16 stars 8 scriptsGGPA - graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture
Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph.
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softwarestatisticalmethodclassificationgenomewideassociationsnpgeneticsclusteringmultiplecomparisonpreprocessinggeneexpressiondifferentialexpressionopenblascpp
4.00 score 1 stars 2 scripts 352 downloadsmosaics - MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq)
This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification.
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chipseqsequencingtranscriptiongeneticsbioinformaticscpp
3.30 score 10 scripts 709 downloads