This function will automatically scale your data based on the normalization method you choose. It will also calculate the CVs for each sample and each metabolite.
Usage
auto_scale(
is_to_use,
pref_to_use,
prefs_to_remove,
normalization,
data,
sample_information,
pool_missing_p,
fill_method,
smooth_method = "lowess"
)Arguments
- is_to_use
A vector of the internal standards to use for normalization. If you do not want to use internal standards, leave this blank.
- pref_to_use
A string of the preferred reference to use for normalization. If you do not want to use a preferred reference, leave this blank.
- prefs_to_remove
A vector of the preferred references to remove from the data. If you do not want to remove any preferred references, leave this blank.
- normalization
A string of the normalization method to use. This can be "IS", "NN", or "SMOOTH".
- data
A data frame of the data to be normalized. This should be the output of the
read_datafunction.- sample_information
A data frame of the sample information. This should be the output of the
read_sample_informationfunction.- pool_missing_p
A boolean indicating whether or not to pool missing values. If TRUE, missing values will be pooled. If FALSE, missing values will be filled with the half-minimum.
- fill_method
A string indicating how to fill missing values. This can be "half-min" or "mean". If "half-min", missing values will be filled with the half-minimum. If "mean", missing values will be filled with the mean.
- smooth_method
A string indicating the smoothing method to use. This can be "lowess" or "loess". If "lowess", a local polynomial regression will be used. If "loess", a local polynomial regression will be used.