README.md

HTSet

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Quantitative high through-put experiments often generates data in very similar structures, including an expression/abundance matrix, a table for feature variables, and a table for sample metadata. The feature data often holds feature characteristics such as gene ID, protein name, metabolite annotation, or pathway ID. The sample metadata contains variables for each sample, such as genotype, phenotype, or geographic information. Thus in the HTSet package, the core S4 class HTSet was created as a general data container for any quantitative high through-put experiment data, including RNA-seq, microbiome, proteomics, and metabolomics study.

Installation

devtools::install_github("zhuchcn/HTSet")


zhuchcn/HTSet documentation built on April 10, 2020, 4:51 p.m.