inputcheck1

The data. A m by n matrix, where m is the number of samples and n is the number of features.

The factor(s) of interest. A m by p matrix, where m is the number of samples and p is the number of factors of interest. Very often p = 1.

Any additional covariates to include in the model. Either a m by q matrix of covariates, or simply 1 (the default) for an intercept term.

The negative controls. A logical vector of length n.

Whether to check for missing values.

check.na

Performs a basic sanity check on the arguments passed to RUV-2, RUV-4, RUV-inv, and RUV-rinv.

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.

Johann Gagnon-Bartsch

Detect and Remove Unwanted Variation using Negative Controls

inputcheck1 function

<dl> <dt>Y</dt>
<dd>The data. A m by n matrix, where m is the number of samples and n is the number of features.</dd> <dt>X</dt>
<dd>The factor(s) of interest. A m by p matrix, where m is the number of samples and p is the number of factors of interest. Very often p = 1.</dd> <dt>Z</dt>
<dd>Any additional covariates to include in the model. Either a m by q matrix of covariates, or simply 1 (the default) for an intercept term.</dd> <dt>ctl</dt>
<dd>The negative controls. A logical vector of length n.</dd> <dt>check.na</dt>
<dd>Whether to check for missing values.</dd></dl>

Arguments

Input Check One — inputcheck1

<dl>

 <dt>Y</dt>
<dd>The data. A m by n matrix, where m is the number of samples and n is the number of features.</dd>

 <dt>X</dt>
<dd>The factor(s) of interest. A m by p matrix, where m is the number of samples and p is the number of factors of interest. Very often p = 1.</dd>

 <dt>Z</dt>
<dd>Any additional covariates to include in the model. Either a m by q matrix of covariates, or simply 1 (the default) for an intercept term.</dd>

 <dt>ctl</dt>
<dd>The negative controls. A logical vector of length n.</dd>

 <dt>check.na</dt>
<dd>Whether to check for missing values.</dd>

</dl>

inputcheck1: Input Check One

Description

Usage

Value

Arguments

See Also