Description Details Slots Accessors Author(s) References See Also Examples

`Eigensystem`

is a list-based class for storing the results of applying Singular Value Decomposition (SVD) to a feature by assay data set. Objects are normally created by `compute,Eigensystem-method`

.

The `Eigensystem`

class stores the original data and all SVD-derived information obtained with `compute`

.
Data in the `Eigensystem`

are organized into different slots,
‘matrix’, ‘signMatrix’, ‘assayMatrix’, ‘featureMatrix’,
‘eigenassays’, ‘eigenexpressions’, ‘eigenfeatures’,
‘assaycorrelations’, ‘featurecorrelations’, ‘fractions’,
‘entropy’, ‘apply’, ‘excludeEigenfeatures’, and ‘colorIdFeatures’.
Brief descriptions of these slots are provided below.

`Eigensystem`

objects contain the following slots

`matrix`

:`matrix`

containing the feature by assay data without missing values`signMatrix`

:`matrix`

containing the sign of each element in matrix`assayMatrix`

:`matrix`

containing additional information about assays, with rows as assays and columns as additional variables`featureMatrix`

:`matrix`

containing additional information about features, with rows as features and columns as additional variables`eigenassays`

:`matrix`

containing the feature by eigenassay data, with each column in eigenassays corresponding to a left singular vector, representing genome-wide expression, proteome-wide abundance or metabolome-wide intensity in the corresponding eigenassay`eigenexpressions`

:`numeric`

vector containing the eigenexpression fraction of each eigenfeature, eigenassay-pair, constituting the diagonal elements of the diagonal matrix connecting the left and right singular values; the diagonal matrix reflects the decoupling and decorrelation of the data, with expression of each eigenfeature restricted to the corresponding eigenassay`eigenfeatures`

:`matrix`

containing the eigenfeatures by assay data, with each row corresponding to a right singular vector, representing the expression, abundance or intensity of the corresponding eigenfeature across all assays`assaycorrelations`

:`matrix`

containing the correlation between the eigenassays as rows and the assays as columns`featurecorrelations`

:`matrix`

containing the correlation between the eigenfeatures as rows and features as columns`fractions`

:`numeric`

vector containing the eigenexpression fraction for each eigenfeature, eigenassay-pair, defined as the relative fraction of overall expression that each eigenfeature and eigenassay capture`entropy`

:`numeric`

value between 0 and 1 giving the Shannon entropy as measure for data complexity, with an entropy of 0 corresponding to an ordered and redundant data set with all expression captured by a single eigenfeature, eigenassay-pair, and an entropy of 1 corresponding to a disordered and random data set with all eigenfeature, eigenassay-pairs equally expressed`apply`

:`character`

containing whether the eigensystem should be computed for the actual data or the variance in the data`excludeEigenfeatures`

:`numeric`

vector containing eigenfeature 1 and 2 in case they capture >85% of the data with eigenfeature 2 capturing at least 15%, otherwise numeric value containing eigenfeature 1`colorIdFeatures`

:`numeric`

vector or`factor`

containing annotation information on the features

`matrix(x)`

,`matrix(x) <- value`

`signMatrix(x)`

,`signMatrix(x) <- value`

`assayMatrix(x)`

,`assayMatrix(x) <- value`

`featureMatrix(x)`

,`featureMatrix(x) <- value`

`eigenassays(x)`

,`eigenassays(x) <- value`

`eigenexpressions(x)`

,`eigenexpressions(x) <- value`

`eigenfeatures(x)`

,`eigenfeatures(x) <- value`

`assaycorrelations(x)`

,`assaycorrelations(x) <- value`

`featurecorrelations(x)`

,`featurecorrelations(x) <- value`

`fractions(x)`

,`fractions(x) <- value`

`entropy(x)`

,`entropy(x) <- value`

`apply(x)`

,`apply(x) <- value`

`excludeEigenfeatures(x)`

,`excludeEigenfeatures(x) <- value`

`colorIdFeatures(x)`

,`colorIdFeatures(x) <- value`

Anneleen Daemen [email protected], Matthew Brauer [email protected]

Alter O, Brown PO and Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U.S.A. 97(18), 10101-10106 (2000).

compute,Eigensystem-method

1 2 3 4 5 6 7 8 | ```
## Metabolomics starvation data obtained from http://genomics-pubs.princeton.edu/StarvationMetabolomics/Download.shtml
data(StarvationData)
## An object from class Eigensystem is obtained with the compute method
eigensystem <- compute(StarvationData)
## Obtain entropy
entropy(eigensystem)
``` |

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