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.The first data set, the Input Data Set , contains all of the numeric data to be analyzed. This data set must be in the tall format where each sample corresponds to one row and each column corresponds to a separate experimental condition or array.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 second data set is the Experimental Design Data Set (EDDS) . This required data set tells how the experiment was performed, providing information about the columns in the input data set. Note that one column in the EDDS must be named ColumnName and the values contained in this column must exactly match the column names in the input data set.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.For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets .Refer to the Partial Least Squares Normalization output documentation for detailed descriptions of the output of this process.