Center-median normalise the expression matrix in an MSnSet using medians from a reference dataset

Center-median normalisation is a simple normalisation method that is appropriate for relative abundance proteomics such as isobaric tagging. This can be achieved with MSnbase::normalise(method='diff.median'). However, for some experimental designs, the normalisation should be against the medians in another dataset. For example, for PTM studies, one may wish to isobaric tag samples, pool, and then PTM-enriched, with the enriched sample quantified in a separate run to the non-enriched (total) sample. In this case, it may make more sense to center-median normalise the PTM-enriched samples using the median from the total samaples.

center_normalise_to_ref(
  obj,
  medians,
  center_to_zero = FALSE,
  on_log_scale = FALSE
)

Arguments

obj

MSnSet. Contains PSMs.

medians

vector, numeric. Sample medians from reference dataset

center_to_zero

logical. Centre the data range on zero. If FALSE, normalisation retains original data range.

on_log_scale

logical. Input data is log-transformed

Value

Returns an MSnSet with the expression matrix column center-median normalised