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Using a reference standard, we measured precision, recall, and F-scores for the outputs of all summarization methods.
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The resulting p-values are based on a summarization of all these 6 datasets, which represent a more confident evaluation of periodical expression than a single dataset.
Reference standards including recommendations from all three products may be more comprehensive, and shed better light on all three summarization methodologies' recall and precision performance.
All three summarization methods were applied to three widely used data sets from the UCI Repository.
Gene expression values (i.e., gene intensity value) were obtained from a summarization of intensity values of all corresponding probes using the RMA (Robust Multi-array Average) method.
Shown in Figure 4 is a summarization of selected features for all four data types, where the regression/classification weights are color-mapped for each feature and each task.
A value of 100 indicated that the gene was detected as significantly differentially expressed for all three summarizations.
Visualization output was not included; it was considered irrelevant, since it automatically includes all output from summarization.
For all four GeneChip summarization methods, PCC rank and MR showed higher prediction efficiency than PCC values.
When package summarization is enabled, all the objects and classes from a given package are represented as a single node.
While offering a basic summarized signal comparison, we recognize the need for further improvements in summarization routines for all-exon arrays.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com