{ "_id": "6a1aeac31d7bb097a09f36d1", "Package": "SIAMCAT", "Type": "Package", "Title": "Statistical Inference of Associations between Microbial\nCommunities And host phenoTypes", "Version": "2.17.0", "Authors@R": "c(person(\"Konrad\", \"Zych\", role = c(\"aut\"),\nemail = \"konrad.zych@embl.de\",\ncomment = c(ORCID = \"0000-0001-7426-0516\")),\nperson(\"Jakob\", \"Wirbel\", role = c(\"aut\", \"cre\"),\nemail = \"jakob.wirbel@embl.de\",\ncomment = c(ORCID = \"0000-0002-4073-3562\")),\nperson(\"Georg\", \"Zeller\", role = c(\"aut\"),\nemail = \"zeller@embl.de\",\ncomment = c(ORCID = \"0000-0003-1429-7485\")),\nperson(\"Morgan\", \"Essex\",\nemail = \"morgan.essex@embl.de\", role = c(\"ctb\")),\nperson(\"Nicolai\", \"Karcher\", role = c(\"ctb\")),\nperson(\"Kersten\", \"Breuer\", role = c(\"ctb\")))", "Description": "Pipeline for Statistical Inference of Associations between\nMicrobial Communities And host phenoTypes (SIAMCAT). A primary\ngoal of analyzing microbiome data is to determine changes in\ncommunity composition that are associated with environmental\nfactors. In particular, linking human microbiome composition to\nhost phenotypes such as diseases has become an area of intense\nresearch. For this, robust statistical modeling and biomarker\nextraction toolkits are crucially needed. 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