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In lgl15/cnmtf: Prioritisation of single nucleotide variants using Corrected non-negative matrix tri-factorisation
Description
Usage
Preprocessing functions
Factorisation functions
Scoring functions
Comparing functions
Author(s)
Description
cNMTF: Prioritisation of single nucleotide variants using Corrected non-negative matrix tri-factorisation
A data fusion framework for prioritising reliable associations between single nucleotide variants (SNVs) and traits.
This algorithm allows for studying the effect of SNVs on categorical traits, thanks to its main features : 1) It captures the interrelatedness between variants data, the SNVs deleteriousness effect and the protein-protein interactions (PPIs) that might be disrupted. 2) It simultaneously accounts for the patient's outcome and ancestry by means of kernels functions, minimizing the confounding for population structures.
The cnmtf package provides four categories of functions for
preprocessing data, clustering, scoring SNVs and comparing results.
Usage
1
Preprocessing functions
These functions will help you to create the inputs for the algorithm.
Factorisation functions
Main functions to integrate the input data, generate the low-dimmensional matrices and find consensus clusters.
Scoring functions
A set of functions to score SNVs and prioritise significant SNV-trait associations from the low-dimmensional matrices.
Comparing functions
Auxiliary functions to compare your results across different settings of the algorithm.
Author(s)
Luis G. Leal, lgl15@imperial.ac.uk
lgl15/cnmtf documentation built on May 28, 2019, 6:33 p.m.
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Description Usage Preprocessing functions Factorisation functions Scoring functions Comparing functions Author(s)
Description
cNMTF: Prioritisation of single nucleotide variants using Corrected non-negative matrix tri-factorisation
A data fusion framework for prioritising reliable associations between single nucleotide variants (SNVs) and traits. This algorithm allows for studying the effect of SNVs on categorical traits, thanks to its main features : 1) It captures the interrelatedness between variants data, the SNVs deleteriousness effect and the protein-protein interactions (PPIs) that might be disrupted. 2) It simultaneously accounts for the patient's outcome and ancestry by means of kernels functions, minimizing the confounding for population structures.
The cnmtf package provides four categories of functions for preprocessing data, clustering, scoring SNVs and comparing results.
Usage
1 |
Preprocessing functions
These functions will help you to create the inputs for the algorithm.
Factorisation functions
Main functions to integrate the input data, generate the low-dimmensional matrices and find consensus clusters.
Scoring functions
A set of functions to score SNVs and prioritise significant SNV-trait associations from the low-dimmensional matrices.
Comparing functions
Auxiliary functions to compare your results across different settings of the algorithm.
Author(s)
Luis G. Leal, lgl15@imperial.ac.uk
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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