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Illustration of MINT analysis in mixOmics.

By Florian Rohart (3574298), Benoît Gautier (4571203), Amrit Singh (429768) and Kim-Anh Lê Cao (159730)


<p><b>A</b>: Parameter tuning of a MINT sPLS-DA model with two components using Leave-One-Group-Out cross-validation and maximum distance, BER (y-axis) with respect to number of selected features (x-axis). Full diamond represents the optimal number of features to select on each component, <b>B</b>: Performance of the final MINT sPLS-DA model including selected features based on BER and classification error rate per class, <b>C</b>: Global sample plot with confidence ellipse plots, <b>D</b>: Study specific sample plot, <b>E</b>: Clustered Image Map (Euclidean Distance, Complete linkage). Samples are represented in rows, selected features on the first component in columns. <b>F</b>: Loading plot of each feature selected on the first component in each study, with color indicating the class with a maximal mean expression value for each gene.</p

Topics: Biochemistry, Genetics, Cancer, Plant Biology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, R package, data sets, PLS, systems biology approach, omic, data integration
Year: 2017
DOI identifier: 10.1371/journal.pcbi.1005752.g005
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Provided by: FigShare
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