This PR adds functions that allow to perform a linear-model based normalization, which includes:
adjustment for injection index dependent signal drifts in LC-MS data
adjustment for batch effects
The functions perform row-wise adjustments, thus, a linear model if fitted for each "feature" separately, and the data will be adjusted (per feature) for this. Also, estimation of the bias can be performed on a subset of samples (e.g. QC samples) and applied in a second step to the full data set.
These functions are core/basic functions, more user convenient functions that base on these might be implemented elsewhere.
This PR adds functions that allow to perform a linear-model based normalization, which includes:
The functions perform row-wise adjustments, thus, a linear model if fitted for each "feature" separately, and the data will be adjusted (per feature) for this. Also, estimation of the bias can be performed on a subset of samples (e.g. QC samples) and applied in a second step to the full data set.
These functions are core/basic functions, more user convenient functions that base on these might be implemented elsewhere.