ruv_svdplot: RUV SVD Plot
In ruv: Detect and Remove Unwanted Variation using Negative Controls

Description Usage Arguments Details Value Author(s)

A generalization of a PC (principal component) plot.

1	ruv_svdplot(Y.data, Y.space = NULL, info = NULL, k = c(1, 2), Z = 1, left = TRUE)

`Y.data`	The data matrix. Rows are observations and columns are features (e.g. genes).
`Y.space`	Either a data matrix of the same dimension as `Y.data`, or the SVD of such a matrix, as returned by the `svd` function. The singular vectors of this matrix define the space in which `Y.data` will be plotted. If NULL, `Y.data` itself is used.
`info`	Additional data to be included in the ggplot, which can be used for setting aesthetics such as color. Converted to a dataframe, which should have a number of rows equal to the number of rows of `Y.data` (if `left=TRUE`) or the number of columns of `Y.data` (if `left=FALSE`).
`k`	A numeric vector of length 2. The singular vectors to be plotted. Typically integers, but fractional values can also be specified. For example, a value of 2.5 corresponds to the linear combination (singular vector 2) + (singular vector 3), rescaled to have unit length. Similarly, a value of 2.2 corresponds to the (rescaled) linear combination 8(singular vector 2) + 2(singular vector 3), and -2.2 corresponds to the (rescaled) linear combination 8(singular vector 2) - 2(singular vector 3). Note that the vectors defined by 2.2 and -2.8 are orthogonal to each other, as are those defined by 2.3 and -2.7, etc.
`Z`	Any variables to regress out of `Y.data` as a preprocessing step. May simply be 1 (the default) for an intercept term, i.e. the columns of Y are mean centered. May also be `NULL`. Similarly for `Y.space`, unless `Y.space` is already an SVD.
`left`	Plot the left singular vectors (if TRUE) or the right singular vectors (if FALSE).