Calculate normalization parameters for the data via via z-score transformation.
zscore_transform(
e_data,
edata_id,
subset_fn,
feature_subset,
backtransform = FALSE,
apply_norm = FALSE,
check.names = NULL
)a \(p \times n + 1\) data.frame, where \(p\) is the number of peptides, lipids, or metabolites and \(n\) is the number of samples. Each row corresponds to data for a peptide, protein, lipid, or metabolite, with one column giving the biomolecule identifier name.
character string indicating the name of the peptide, protein,
lipid, or metabolite identifier. Usually obtained by calling
attr(omicsData, "cnames")$edata_cname.
character string indicating the subset function to use for normalization
character vector containing the feature names in the subset to be used for normalization
logical argument. If TRUE, the data will be back transformed after normalization so that the values are on a scale similar to their raw values. See details for more information. Defaults to FALSE.
logical argument. If TRUE, the normalization will be applied to the data. Defaults to FALSE.
deprecated
List containing two elements: norm_params is list with two
elements:
| scale | numeric vector of length n standard deviations for each sample |
| location | numeric vector of length n means for each sample |
backtransform_params is a list with two elements:
| scale | numeric value giving the pooled standard deviation across all samples |
| location | numeric value giving global mean across all samples |
If backtransform is set to TRUE then each list item under
backtransform_params will be NULL.
If apply_norm is TRUE, the transformed data is returned as a third
list item.
Each feature is scaled by subtracting the mean of the feature subset specified for normalization and then dividing the result by the standard deviation (SD) of the feature subset specified for normalization to get the normalized data. The location estimates are the subset means for each sample. The scale estimates are the subset SDs for each sample. If backtransform is TRUE, the normalized feature values are multiplied by a pooled standard deviation (estimated across all samples) and a global mean of the subset data (across all samples) is added back to the normalized values. Means are taken ignoring any NA values.