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The other one of the two views simulated to mimic datasets from a real study, in which genes are characterized with expression patterns.
● Data Source:
CranContrib
● Keywords:
● Alias: view2
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0 images
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phe
(Package: mvcluster) :
Phenotype data
Phenotype data of 1003 subjects on 10 simulated phenotypic variables.
● Data Source:
CranContrib
● Keywords:
● Alias: phe
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0 images
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mvlrrl0
(Package: mvcluster) :
Multi-view bi-clustering via L0-norm enforced sparse LRR
Identify consistent sample cluster among all views and simultaneously associated feature clusters per view. Clusters are obtained via finding sparse low rank representation (LRR) of input data matrices, where the sparsity is enforced using L0-norm. One sample cluster and its associated feature clusters are identified and returned each time this function is used. If multiple clusters are desired, call this function repeatedly with samples left unclustered.
● Data Source:
CranContrib
● Keywords:
● Alias: mvlrrl0
●
0 images
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One of the two views simulated to mimic datasets from a real study, in which genes are characterized with expression patterns.
● Data Source:
CranContrib
● Keywords:
● Alias: view1
●
0 images
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gen
(Package: mvcluster) :
Genotype data
Genotype data of 1003 subjects on 1000 simulated biallelic genetic variants.
● Data Source:
CranContrib
● Keywords:
● Alias: gen
●
0 images
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mvsvdl1
(Package: mvcluster) :
Multi-view bi-clustering via SSVD
Identify consistent sample cluster among all views and simultaneously associated feature clusters per view. Clusters are obtained via multi-view sparse singular value decomposition (SSVD). One sample cluster and its associated feature clusters are identified and returned through each call of this function. If multiple clusters are desired, call this function repeatedly with samples left unclustered.
● Data Source:
CranContrib
● Keywords:
● Alias: mvsvdl1
●
0 images
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mvlrrl1
(Package: mvcluster) :
Multi-view bi-clustering via L1-norm enforced sparse LRR
Identify consistent sample cluster among all views and simultaneously associated feature clusters per view. Clusters are obtained via finding sparse low rank representation (LRR) of input data matrices, where the sparsity is enforced using L1-norm. One sample cluster and its associated feature clusters are identified and returned each time this function is used. If multiple clusters are desired, call this function repeatedly with samples left unclustered.
● Data Source:
CranContrib
● Keywords:
● Alias: mvlrrl1
●
0 images
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providing 
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