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In CrossICC: An Interactive Consensus Clustering Framework for Multi-platform Data Analysis
Allocate new Samples
CrossICC provide predict() function allowing users to obtaind the cluster number of the unclassified samples based on the result from the CrossICC object.
In this section ,users could perform prediction with the CrossICC.object. Note that, for new sample, it should contains all the features that used for prediction.
that is, the feature from the CrossICC result. Otherwise, it will throw errors.
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CrossICC documentation built on April 29, 2020, 4:40 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Allocate new Samples
CrossICC provide predict() function allowing users to obtaind the cluster number of the unclassified samples based on the result from the CrossICC object.
In this section ,users could perform prediction with the CrossICC.object. Note that, for new sample, it should contains all the features that used for prediction.
that is, the feature from the CrossICC result. Otherwise, it will throw errors.
Try the CrossICC package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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