Median-balanced quantile normalization

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

Quantile normalization of a data matrix where rank invariant (RI)/nearly rank invariant (NRI) rows/features or other user-selected rows are normalized by the mean/median-balanced quantile normalization.

mbqnNRI(
  x,
  FUN = "mean",
  na.rm = TRUE,
  method = NULL,
  low_thr = 0.5,
  index = NULL,
  offsetmatrix = FALSE,
  verbose = TRUE
)

`x`	a data matrix, where rows represent features, e.g. of protein abundance, and columns represent groups or samples, e.g. replicates, treatments, or conditions.
`FUN`	a function like mean, median (default), a user defined function, or a numeric vector of weights with length `nrow(x)` to balance each feature across samples. Functions can be parsed also as characters. If FUN = NULL, features are not balanced, i.e. normal QN is used.
`na.rm`	logical indicating to omit NAs in the computation of feature mean.
`method`	character specifying function for computation of quantile normalization; "limma" (default) for `normalizeQuantiles()` from the limma package or "preprocessCore" for `normalize.quantiles()` from the preprocessCore package.
`low_thr`	a value between [0 1]. Features with RI frequency >=`low_thr` are considered as NRI/RI; default 0.5.
`index`	an integer or a vector integers specifying the indices of selected rows.
`offsetmatrix`	logical indicating if offset matrix should be used instead of offset vector specifying offset for each row
`verbose`	logical indicating to print messages.

Selected rows and/or rows with rank invariance frequency >=threshold are normalized with the mean/median-balanced quantile normalization. Remaining rows are quantile normalized without mean balancing.

Normalized matrix.

Ariane Schad

Brombacher, E., Schad, A., Kreutz, C. (2020). Tail-Robust Quantile Normalization. BioRxiv.

mbqn(), mbqnGetNRIfeatures().

## Quantile normalize a data matrix where
## nearly rank invariant (NRI) features are balanced
X <- matrix(c(5,2,3,NA,4,1,4,2,3,4,6,NA,1,3,1),ncol=3)
mbqnNRI(X, median,low_thr = 0.5) # Balance NRI features selected by threshold
mbqnNRI(X, median, index = c(1,2)) # Balance selected features