{ "_id": "6a1a792d1d7bb097a09d32fc", "Package": "scECODA", "Title": "Single-Cell Exploratory Compositional Data Analysis", "Version": "1.1.5", "Authors@R": "c(\nperson(\"Christian\", \"Halter\",\nemail = \"scecoda.dev@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = '0009-0009-5479-2246')),\nperson(\"Massimo\", \"Andreatta\",\nemail = \"Massimo.Andreatta@unige.ch\",\nrole = c(\"aut\"),\ncomment = c(ORCID = '0000-0002-8036-2647')),\nperson(\"Santiago\", \"Carmona\",\nemail = \"Santiago.Carmona@unige.ch\",\nrole = c(\"aut\"),\ncomment = c(ORCID = '0000-0002-2495-0671')),\nperson(\"Swiss Cancer Research Foundation\", role = c(\"fnd\"))\n)", "Description": "The scECODA R package provides a complete workflow for the\nanalysis and visualization of compositional data, primarily\nfocusing on cell type proportions derived from single-cell\ndata. It implements specialized methods, such as the Centered\nLog-Ratio (CLR) transformation, to properly analyze\nproportional data while avoiding the biases introduced by the\ncompositional constraint. The package encapsulates data\nmanagement, transformation, and analysis into a single\nSummarizedExperiment object, offering downstream tools for\ndimensionality reduction via PCA, calculating critical metrics\nlike the Adjusted Rand Index (ARI) and Modularity to quantify\nsample grouping quality, and generating high-quality\nvisualizations like heatmaps and scatter plots.", "biocViews": "Software, SingleCell, Transcriptomics, CellBasedAssays,\nNormalization, Preprocessing, Visualization, Clustering,\nDimensionReduction, FeatureExtraction, PrincipalComponent", "BugReports": "https://github.com/carmonalab/scECODA/issues", "License": "GPL-3 + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "URL": "https://github.com/carmonalab/scECODA", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libssl-dev\nzlib1g-dev", "Repository": "https://carmonalab.r-universe.dev", 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