Selective Measuring

The function adjust_frm() is used to modify existing FastRet models based on changes in chromatographic conditions. It requires a set of molecules with measured retention times on both the original and new column. This function selects a sensible subset of molecules from the original dataset for re-measurement. The selection process includes:

1. Generating chemical descriptors from the SMILES strings of the molecules. These are the features used by train_frm() and adjust_frm().

2. Standardizing chemical descriptors to have zero mean and unit variance.

3. Training a Ridge Regression model with the standardized chemical descriptors as features and the retention times as the target variable.

4. Scaling the chemical descriptors by coefficients of the Ridge Regression model.

5. Clustering the entire dataset, which includes the scaled chemical descriptors and the retention times.

6. Returning the clustering results, which include the cluster assignments, the medoid indicators, and the raw data.

Usage

selective_measuring(raw_data, k_cluster = 25, verbose = 1, seed = NULL)

Arguments

raw_data

The raw data to be processed. Must be a dataframe with columns NAME, RT and SMILES.

k_cluster

The number of clusters for PAM clustering.

verbose

The level of verbosity.

seed

An optional random seed for reproducibility, set at the beginning of the function.

Value

A list containing the following elements:

• clustering: A data frame with columns RT, SMILES, NAME, CLUSTER and IS_MEDOID.

• clobj: The clustering object. The object returned by the clustering function. Depends on the method parameter.

• coefs: The coefficients from the Ridge Regression model.

• model: The Ridge Regression model.

• df: The preprocessed data.

• dfz: The standardized features.

• dfzb: The features scaled by the coefficients (betas) of the Ridge Regression model.

Examples

x <- selective_measuring(RP[1:50, ], k = 5, verbose = 0)
# For the sake of a short runtime, only the first 50 rows of the RP dataset
# were used in this example. In practice, you should always use the entire
# dataset to find the optimal subset for re-measurement.