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We briefly list major public efforts in creating resources (datasets, methods and workshops) for data integration.
Digital records describing bioresources (e.g., sample collections), or born-digital resources (datasets associated with a resource or generated by its use) can be assigned data DOIs [ 23].
In addition, sample variations in the resource dataset that defines explanatory variables are ignored.
To speed-up the tracking procedure, SNPTracker uses a compiled resource dataset from the original dbSNP resource data.
This is done only once by an iterative algorithm in SNPTracker when there is a new dbSNP resource dataset.
The Th dataset was used as a training data (a resource dataset for CCA), and WT data from the Gata3 dataset was used as a test data.
Next, CCA is applied to the transcriptomic data X, using the dataset for D (Z, or resource dataset) as explanatory variables.
We addressed how efficiently those classification methods identify Th17 differentiated T cells in the Stat3 dataset, based on the resource dataset.
In this last section, the classification ability of CCA was compared with other classification methods using Th dataset as a training data (a resource dataset for CCA), and the Stat3 dataset was used as a test data.
Using a single lane of an Illumina HiSeqTM 2000 sequencing run we have generated an extensive genomic resource dataset, providing a broad characterisation of expressed genes in the leaves of D. viscosa, as well as identifying a large set of genetic SNP markers for future population genetic analysis of this species.
The measured data was then compared statistically to several available solar resource datasets for the geographic area under consideration.
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