ruv_shiny

The data matrix. Rows are observations and columns are features (e.g. genes).

A dataframe of information about the observations. Should have the same number of rows as Y. Should contain at least one column that can be used as either a factor of interest or to define replicates.

rowinfo

A dataframe of information about the observations. Should have the same number of rows as Y. Should contain at least one column that is a logical vector that can be used to define negative controls.

colinfo

A list of options to pass to the shinyApp function.

options

A Shiny App that allows quick exploration of a dataset using RUV methods.

Implements the 'RUV' (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.

ruv_shiny: RUV Shiny App

Description

Usage

Arguments

Value