Processes | Expression | Effect Removal via PLS Normalization

Effect Removal via PLS Normalization
The Effect Removal via PLS Normalization process normalizes data by fitting a partial least squares model to class variables and subtracting the predicted fit. This method is appropriate when the class variables represent unwanted effects in the data. These effects, which can include such things as batch effects, laboratory effects, and day/time effects for example, should be removed from the data before proceeding with other analyses.
Note : You should exercise caution when using this method. If the normalized 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 for this process.
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 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.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets .
The output of the Effect Removal via PLS Normalization process includes one data set (with the suffix _pnm appended) containing the normalized data.