This normalization is performed on observations (samples) . It uses the average geometric mean from the selected features(e.g. genes) to scale the data to have the same geometric mean across all samples. The computation is as follows: it first compute the geometric mean on the selected features for each observation, and then get the arithmetic mean on the geometric means across all  observations, this is the scaling factor. Every value on the matrix will be divided by geometric mean of its sample, all the sample geometric mean will be the same and then the values will multiple by the scaling factor. using internal control features (genes). The internal control features, usually housekeeping genes, should not vary among samples. The implementation 

Note: The input data node must contain all positive values to compute geometric mean.

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• use all samples
• use a group
• use matched pairs

Use all samples to create baseline

To normalize data to all the samples, choose to calculate the baseline using the mean or median of all samples for each feature, and choose to subtract baseline or ratio to baseline for the normalization method (Figure 2), and click Finish.

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Choose use group, select the attribute containing the baseline group information, e.g. Treatment in this example, with the samples with the group Control for the Treatment attribute used as the baseline. The control samples can be filtered out after normalization by selecting the Remove baseline samples after normalization check box.

Use matched pairs

When using matched pairs, one sample from each pair serves as the control. An attribute specifying the pairs must be selected in addition to an attribute designating which sample in each pair is the baseline sample (Figure 4). 

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