ruv_summary: RUV Summary
In ruv: Detect and Remove Unwanted Variation using Negative Controls

Description Usage Arguments Details Value Author(s) See Also

Post-process and summarize the results of call to RUV2, RUV4, RUVinv, or RUVrinv.

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ruv_summary(Y, fit, rowinfo=NULL, colinfo=NULL, colsubset=NULL, sort.by="F.p", 
            var.type=c("ebayes", "standard", "pooled"),
            p.type=c("standard", "rsvar", "evar"), min.p.cutoff=10e-25)

`Y`	The original data matrix used in the call to RUV2/4/inv/rinv
`fit`	A RUV model fit (a list), as returned by RUV2 / RUV4 / RUVinv / RUVrinv
`rowinfo`	A matrix or dataframe containing information about the rows (samples). This information is included in the summary that is returned.
`colinfo`	A matrix or dataframe containing information about the columns (features, e.g. genes). This information is included in the summary that is returned.
`colsubset`	A vector indexing the features of interest. Only only data on these features will be returned.
`sort.by`	An index variable; which column of `C` (see below) should be used to sort the features. The default is "F.p", meaning that features will be sorted by the F-test p-value. If `NULL`, features will note be sorted.
`var.type`	Which type of estimate for sigma2 should be used from the call to variance_adjust? The options are "ebayes", "standard", or "pooled." See variance_adjust for details.
`p.type`	Which type of p-values should be used from the call to variance_adjust? The options are "standard", "rsvar", or "evar".
`min.p.cutoff`	p-values below this value will be changed and set equal to this value. Useful for plotting p-values on a log scale.

This function post-processes the results of a call to RUV2/4/inv/rinv and then nicely summarizes the output. The post-processing step primarily consists of a call to variance_adjust, which computes various adjustments to variances, t-statistics, and and p-values. See variance_adjust for details. The var.type and p.type options determine which of these adjustments are used. An additional post-processing step is that the column means of the Y matrix are computed, both before and after the call to RUV1 (if eta was specified).

After post-processing, the results are summarized into a list containing 4 objects: 1) the data matrix Y; 2) a dataframe R containing information about the rows (samples); 3) a dataframe C containing information about the columns (features, e.g. genes), and 4) a list misc of other information returned by RUV2/4/inv/rinv.

Finally, if colsubset is specified, then C is subset to include only the features of interest (as are the relevant entries of misc that are used to compute projection plots). If sort.by is specified, the features will also be sorted.

A list containing:

`Y`	The original data matrix.
`R`	A dataframe of row-wise information, including `X`, `Z`, and any other data passed in with `rowinfo`
`C`	A dataframe of column-wise information, including p-values, estimated regression coefficients, estimated variances, column means, an index of the negative controls, and any other data passed in with `colinfo`.
`misc`	A list of additional information returned by RUV2/4/inv/rinv