RUV4: Remove Unwanted Variation, 4-step
In ruv: Detect and Remove Unwanted Variation using Negative Controls

Description Usage Arguments Details Value Note Author(s) References See Also Examples

The RUV-4 algorithm. Estimates and adjusts for unwanted variation using negative controls.

1 2	RUV4(Y, X, ctl, k, Z = 1, eta = NULL, include.intercept=TRUE, fullW0=NULL, inputcheck=TRUE)

`Y`	The data. A m by n matrix, where m is the number of samples and n is the number of features.
`X`	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. Factors and dataframes are also permissible, and converted to a matrix by `design.matrix`.
`ctl`	An index vector to specify the negative controls. Either a logical vector of length n or a vector of integers.
`k`	The number of unwanted factors to use. Can be 0.
`Z`	Any additional covariates to include in the model, typically a m by q matrix. Factors and dataframes are also permissible, and converted to a matrix by `design.matrix`. Alternatively, may simply be 1 (the default) for an intercept term. May also be `NULL`.
`eta`	Gene-wise (as opposed to sample-wise) covariates. These covariates are adjusted for by RUV-1 before any further analysis proceeds. Can be either (1) a matrix with n columns, (2) a matrix with n rows, (3) a dataframe with n rows, (4) a vector or factor of length n, or (5) simply 1, for an intercept term.
`include.intercept`	Applies to both `Z` and `eta`. When `Z` or `eta` (or both) is specified (not `NULL`) but does not already include an intercept term, this will automatically include one. If only one of `Z` or `eta` should include an intercept, this variable should be set to `FALSE`, and the intercept term should be included manually where desired.
`fullW0`	Can be included to speed up execution. Is returned by previous calls of `RUV4`, `RUVinv`, or `RUVrinv` (see below).
`inputcheck`	Perform a basic sanity check on the inputs, and issue a warning if there is a problem.

Implements the RUV-4 algorithm as described in Gagnon-Bartsch, Jacob, and Speed (2013), using the SVD as the factor analysis routine. Unwanted factors W are estimated using control genes. Y is then regressed on the variables X, Z, and W.

A list containing

`betahat`	The estimated coefficients of the factor(s) of interest. A p by n matrix.
`sigma2`	Estimates of the features' variances. A vector of length n.
`t`	t statistics for the factor(s) of interest. A p by n matrix.
`p`	P-values for the factor(s) of interest. A p by n matrix.
`Fstats`	F statistics for testing all of the factors in `X` simultaneously.
`Fpvals`	P-values for testing all of the factors in `X` simultaneously.
`multiplier`	The constant by which `sigma2` must be multiplied in order get an estimate of the variance of `betahat`
`df`	The number of residual degrees of freedom.
`W`	The estimated unwanted factors.
`alpha`	The estimated coefficients of W.
`byx`	The coefficients in a regression of Y on X (after both Y and X have been "adjusted" for Z). Useful for projection plots.
`bwx`	The coefficients in a regression of W on X (after X has been "adjusted" for Z). Useful for projection plots.
`X`	`X`. Included for reference.
`k`	`k`. Included for reference.
`ctl`	`ctl`. Included for reference.
`Z`	`Z`. Included for reference.
`eta`	`eta`. Included for reference.
`fullW0`	Can be used to speed up future calls of RUV4.
`include.intercept`	`include.intercept`. Included for reference.
`method`	Character variable with value "RUV4". Included for reference.

Additional resources can be found at http://www-personal.umich.edu/~johanngb/ruv/.

Johann Gagnon-Bartsch johanngb@umich.edu

Using control genes to correct for unwanted variation in microarray data. Gagnon-Bartsch and Speed, 2012. Available at: http://biostatistics.oxfordjournals.org/content/13/3/539.full.

Removing Unwanted Variation from High Dimensional Data with Negative Controls. Gagnon-Bartsch, Jacob, and Speed, 2013. Available at: http://statistics.berkeley.edu/tech-reports/820.

RUV2, RUVinv, RUVrinv, variance_adjust

## Create some simulated data
m = 50
n = 10000
nc = 1000
p = 1
k = 20
ctl = rep(FALSE, n)
ctl[1:nc] = TRUE
X = matrix(c(rep(0,floor(m/2)), rep(1,ceiling(m/2))), m, p)
beta = matrix(rnorm(p*n), p, n)
beta[,ctl] = 0
W = matrix(rnorm(m*k),m,k)
alpha = matrix(rnorm(k*n),k,n)
epsilon = matrix(rnorm(m*n),m,n)
Y = X%*%beta + W%*%alpha + epsilon

## Run RUV-4
fit = RUV4(Y, X, ctl, k)

## Get adjusted variances and p-values
fit = variance_adjust(fit)