Description Usage Arguments Details Value Examples

View source: R/RUVIII_C_Varying.R

Apply RUV-III-C, a variation of RUV-III that only uses non-missing values

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`k` |
The number of factors of unwanted variation to remove |

`Y` |
The input data matrix. Must be a matrix, not a data.frame. It should contain missing (NA) values, rather than zeros. |

`M` |
The design matrix containing information about technical replicates. It should not contain an intercept term! |

`toCorrect` |
The names of the variables to correct using RUV-III-C |

`potentialControls` |
The names of the control variables which are known to be constant across the observations |

`withExtra` |
Should we generate extra information? |

`withW` |
Should we generate the matrices W giving information about the unwanted factors, for every peptide? |

`withAlpha` |
Should we generate, per-peptide, the matrix alpha giving the effects of the unwanted factors? |

`version` |
The version of the underlying code to use. Must be either "CPP" or "R" |

`progress` |
Should a progress bar be displayed? |

`...` |
Other arguments for the prototype R code. Supported values are |

See the documentation of RUVIII_C for more information about the RUV-III-C method. This function is identical, except in this case the set of negative control variables actually used varies depending on the target variable to be normalized. Instead of putting in a list of negative control variables, the user specifies a list of potential negatve control variables.

When normalizing variable X, the algorithm begins by selecting the rows of the data matrix for which X is non-missing. Out of the potential negative control peptides, it selects those that are always non-missing across the selected subset. The standard version of RUV-III is then applied to the subset, similar to RUVIII_C.

There are two implementations of this function, one in C++ and one in R. Select which version using the `version`

argument, which must be either "CPP" or "R"

If withExtra = FALSE, returns a matrix. If withExtra = TRUE, returns a list with entries named `newY`

, `residualDimensions`

and `W`

.

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data(crossLab)
#Design matrix containing information about which runs are technical replicates of each other.
#In this case, random pairings of mass-spec runs analysing the same sample, at different sites.
#Note that we specify no intercept term!
M <- model.matrix(~ grouping - 1, data = peptideData)
#Get out the list of peptides, both HEK (control) and peptides of interest.
peptides <- setdiff(colnames(peptideData), c("filename", "site", "mixture", "Date", "grouping"))
#Reduce the data matrix to only the peptide data
onlyPeptideData <- data.matrix(peptideData[, peptides])
#All the human peptides are potential controls. That is, everything that's not an SIS peptides.
potentialControls <- setdiff(peptides, sisPeptides)
#But we want to use controls that are *often* found
potentialControlsOftenFound <- names(which(apply(onlyPeptideData[, potentialControls], 2,
function(x) sum(is.na(x))) <= 10))
#Set number of threads for CRAN
try(RUVIIIC::omp_set_num_threads(2L), silent=TRUE)
#Actually run correction
results <- RUVIII_C_Varying(k = 11, Y = log10(onlyPeptideData), M = M, toCorrect =
colnames(onlyPeptideData), potentialControls = potentialControlsOftenFound)
``` |

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