Clustering and Prediction
Description
The methods predict
for NMF models return the
cluster membership of each sample or each feature.
Currently the classification/prediction of new data is
not implemented.
Usage
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Arguments
object 
an NMF model 
what 
a character string that indicates the type of cluster membership should be returned: ‘columns’ or ‘rows’ for clustering the colmuns or the rows of the target matrix respectively. The values ‘samples’ and ‘features’ are aliases for ‘colmuns’ and ‘rows’ respectively. 
prob 
logical that indicates if the relative contributions of/to the dominant basis component should be computed and returned. See Details. 
dmatrix 
logical that indicates if a dissimiliarity matrix should be attached to the result. This is notably used internally when computing NMF clustering silhouettes. 
... 
additional arguments affecting the predictions produced. 
Details
The cluster membership is computed as the index of the
dominant basis component for each sample
(what='samples' or 'columns'
) or each feature
(what='features' or 'rows'
), based on their
corresponding entries in the coefficient matrix or basis
matrix respectively.
For example, if what='samples'
, then the dominant
basis component is computed for each column of the
coefficient matrix as the row index of the maximum within
the column.
If argument prob=FALSE
(default), the result is a
factor
. Otherwise a list with two elements is
returned: element predict
contains the cluster
membership index (as a factor
) and element
prob
contains the relative contribution of the
dominant component to each sample (resp. the relative
contribution of each feature to the dominant basis
component):
Samples:
p(j) = x(k0) / sum_k x(k)
, for each sample 1≤q j ≤q p, where x(k) is the contribution of the kth basis component to jth sample (i.e.
H[k ,j]
), and x(k0) is the maximum of these contributions.Features:
p(i) = y(k0) / sum_k y(k)
, for each feature 1≤q i ≤q p, where y(k) is the contribution of the kth basis component to ith feature (i.e.
W[i, k]
), and y(k0) is the maximum of these contributions.
Methods
 predict
signature(object = "NMF")
: Default method for NMF models predict
signature(object = "NMFfitX")
: Returns the cluster membership index from an NMF model fitted with multiple runs.Besides the type of clustering available for any NMF models (
'columns', 'rows', 'samples', 'features'
), this method can return the cluster membership index based on the consensus matrix, computed from the multiple NMF runs.Argument
what
accepts the following extra types:'chc'
returns the cluster membership based on the hierarchical clustering of the consensus matrix, as performed by
consensushc
.'consensus'

same as
'chc'
but the levels of the membership index are relabeled to match the order of the clusters as they would be displayed on the associated dendrogram, as reordered on the default annotation track in consensus heatmap produced byconsensusmap
.
References
Brunet J, Tamayo P, Golub TR and Mesirov JP (2004). "Metagenes and molecular pattern discovery using matrix factorization." _Proceedings of the National Academy of Sciences of the United States of America_, *101*(12), pp. 41649. ISSN 00278424, <URL: http://dx.doi.org/10.1073/pnas.0308531101>, <URL: http://www.ncbi.nlm.nih.gov/pubmed/15016911>.
PascualMontano A, Carazo JM, Kochi K, Lehmann D and Pascualmarqui RD (2006). "Nonsmooth nonnegative matrix factorization (nsNMF)." _IEEE Trans. Pattern Anal. Mach. Intell_, *28*, pp. 403415.
Examples
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