Processes | Expression | Partial Least Squares Normalization

Partial Least Squares Normalization
The Partial Least Squares Normalization process normalizes data by fitting a partial least squares (PLS) model to class variables and subtracting the predicted fit. This method is appropriate when the class variables represent unwanted effects in the data, for example a batch or day effect, and you want to remove this effect from the data before proceeding with other analyses.
Caution : If the class variables are confounded with other effects, the differences in the data due to these other effects can also be removed.
What do I need?
Two data sets are required to run this process.
The drosophilaaging.sas7bdat data set, shown below, is a normalized data set derived from the Drosophila Aging experiment described in Sample Case Studies . It has 49 columns and 100 rows corresponding to 49 arrays and 100 individual probes , respectively.
The drosophilaaging_exp.sas7bdat EDDS is shown below. Note that the ColumnName column lists the column names in the input data set. The Array column corresponds to an index variable . Note the variables describing experimental conditions.
The drosophilaaging.sas7bdat and drosophilaaging_exp.sas7bdat data sets are included in the Sample Data folder.
Refer to the Partial Least Squares Normalization output documentation for detailed descriptions of the output of this process.