Description Usage Arguments Details Value References Examples
We use LRAcluster R package to apply dimension reduction and data integration, then use k-means to cluster the samples and get subtyping results.
1 |
data |
A list of different omics data. Each data in data list should be format as a data matrix with rows representing features and columns representing samples. |
type |
A list of the types of different omics data. Each element of the list can be one of "binary","gaussian","poisson" |
maxdimension |
An integer value means the maximize dimension LRAcluster will tried from one, default 10. |
maxk |
the max number of clustering we will try. |
cores |
An integer value means the number of cores for parallel computing. |
ev is a metric that provided by the author of LRAcluster to determine the dimension number of best low dimension. We determine the best low dimensional data by the metric in LRAcluster. When using LRAcluster R package, the user must decide the best dimension number manually according to the explained variation plot in the dimension reduction step so that LRAcluster cannot automatically get the integrated results. Therefore, we propose a method to choose the number of dimensions automatically.
Return a list with the following elements:
x
is the best low dimension answer
n
is best low dimension data we advise by ev values, the metric provide by author of LRAcluster.
rec
is all the ev values
result
is all of the low dimension data we tried from 1 to maxdimension
Wu,D. et al. (2015) Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC Genomics, 16, 1022.
1 2 3 4 5 6 7 8 9 10 | data(COAD_Methy)
data(COAD_miRNA)
data(COAD_mRNA)
res=runLRA(data=list(COAD_Methy, COAD_miRNA, COAD_mRNA),
type=list("gaussian","gaussian","gaussian"))
res=runLRA(data=list(COAD_Methy, COAD_miRNA, COAD_mRNA),
type=list("gaussian","gaussian","gaussian"),maxdimension=10,maxk=10,iskmeans=TRUE)
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