{ "_id": "6a1ae5c01d7bb097a09f007b", "Package": "MPRAnalyze", "Type": "Package", "Title": "Statistical Analysis of MPRA data", "Version": "1.31.0", "Authors@R": "c(\nperson('Tal', 'Ashuach', NULL, 'tal_ashuach@berkeley.edu', c('aut', 'cre')),\nperson('David S', 'Fischer', NULL, 'david.fischer@helmholtz-muenchen.de', 'aut'),\nperson('Anat', 'Kriemer', NULL, 'anat.kreimer@gmail.com', 'ctb'),\nperson('Fabian', 'Theis', NULL, 'fabian.theis@helmholtz-muenchen.de', 'ctb'),\nperson('Nir', 'Yosef', NULL, 'niryosef@berkeley.edu', 'ctb'))", "Author": "Tal Ashuach [aut, cre], David S Fischer [aut], Anat Kriemer\n[ctb], Fabian J Theis [ctb], Nir Yosef [ctb],", "Maintainer": "Tal Ashuach ", "Description": "MPRAnalyze provides statistical framework for the analysis\nof data generated by Massively Parallel Reporter Assays\n(MPRAs), used to directly measure enhancer activity. MPRAnalyze\ncan be used for quantification of enhancer activity,\nclassification of active enhancers and comparative analyses of\nenhancer activity between conditions. 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This analysis aims to determine which sequences have a regulatory function, when no condition is being tested.", "topics": [ "analyzeQuantification" ] }, { "page": "ChrEpi", "title": "Sample MPRA data", "topics": [ "ce.colAnnot", "ce.control", "ce.dnaCounts", "ce.rnaCounts", "ChrEpi" ] }, { "page": "estimateDepthFactors", "title": "estimate library size correction factors", "topics": [ "estimateDepthFactors" ] }, { "page": "getAlpha", "title": "return the fitted value for the transcription rate.", "topics": [ "getAlpha" ] }, { "page": "getDistrParam", "title": "Get model distribution parameters from an MpraObject of a given candidate enhancer", "topics": [ "getDistrParam_DNA", "getDistrParam_RNA" ] }, { "page": "getFits_DNA", "title": "Get DNA model-based estimates from an MpraObject (the expected values based on the model). These can be compared with the observed counts to assess goodness of fit.", "topics": [ "getFits_DNA" ] }, { "page": "getFits_RNA", "title": "Get RNA model-based estimates from an MpraObject (the expected values based on the model). These can be compared with the observed counts to assess goodness of fit.", "topics": [ "getFits_RNA" ] }, { "page": "extractModelParameters", "title": "extract the DNA model parameters", "topics": [ "extractModelParameters_DNA", "extractModelParameters_RNA", "getModelParameters_DNA", "getModelParameters_RNA" ] }, { "page": "MpraObject", "title": "MpraObject", "topics": [ "controls", "controls,MpraObject-method", "dnaAnnot", "dnaAnnot,MpraObject-method", "dnaCounts", "dnaCounts,MpraObject-method", "dnaDepth", "dnaDepth,MpraObject-method", "model", "model,MpraObject-method", "MpraObject", "MpraObject,matrix-method", "MpraObject,SummarizedExperiment-method", "rnaAnnot", "rnaAnnot,MpraObject-method", "rnaCounts", "rnaCounts,MpraObject-method", "rnaDepth", "rnaDepth,MpraObject-method", "rowAnnot", "rowAnnot,MpraObject-method" ] }, { "page": "setDepthFactors", "title": "Manually set library depth correction factors", "topics": [ "setDepthFactors" ] }, { "page": "setModel", "title": "Set the distributional model used. Default is gamma.pois, and is recommended. Other supoprted models are ln.nb in which the DNA follows a log-normal distribution and the RNA follows a negative binomial, and ln.ln in which both follow log-normal distributions. 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