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The Normalize to baseline option is available at in the Normalization and Scaling section in of the context-sensitive menu (Figure 1) upon selection of any counts data node containing matrix of measurements of observations (e.g. samples) on features (e.g genes).

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There are three options to choose baseline samples:

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

Use all samples to create baseline

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

Use a group of sample to create baseline

When there are is a subset of samples that serve as the baseline in the experiment, chooseselect use group as  for Choose baseline samples.  The specific group needs should be specified from the using sample attributes (Figure 3).

Choose this option, select the attribute containing the baseline group information, e.g. Treatment in this example, the samples labeled as Control will be used as baseline. After normalization, the control sample The control samples can be filtered out in the report after normalization by selecting the Remove baseline samples after normalization check button.

Use matched pairs

In a paired experiment design, when normalized one sample to its own control – typically there is only one sample serve as a control to its pair (Figure 4), an attribute on the pair information should be specified in addition to baseline group attribute.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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After normalization, all values for the control sample

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will be either 0 or 1 depending

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on the normalization method chosen, so we

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recommend

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removing baseline samples when using matched pairs.