csdR: Perform differential co-expression analysis
In AlmaasLab/csdR: Differential gene co-expression
csdR R Documentation
Perform differential co-expression analysis
Description
This package provides functionality
to perform differential co-expression
analysis by the CSD algorithm presented by Voigt et al. 2017.
The main function of the
package is run_csd which compares gene expression for two
conditions and returns values for C, S and D for each gene pair.
Also, the underlying
function run_cor_bootstrap is provided
for doing correlation analysis
within a condition,
but is mostly for experimental and expect use only.
The function partial_argsort is
aimed specifically at the task of
selecting the highest values of C, S and D
of the results with minimal computational overhead,
but is written also to be useable for other problems of the same type.
For more details on how to approach, please read the
vignette for the package or the individual manpages for the functions.
Author(s)
Maintainer: Jakob Peder Pettersen jakobpeder.pettersen@gmail.com (ORCID)
References
Voigt A, Nowick K and Almaas E
'A composite network of conserved and tissue specific
gene interactions reveals possible genetic interactions in glioma'
In: PLOS Computational Biology 13(9): e1005739.
(doi: https://doi.org/10.1371/journal.pcbi.1005739)
See Also
Useful links:
AlmaasLab/csdR documentation built on Nov. 9, 2024, 3:09 p.m.
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| csdR | R Documentation |
Perform differential co-expression analysis
Description
This package provides functionality
to perform differential co-expression
analysis by the CSD algorithm presented by Voigt et al. 2017.
The main function of the
package is run_csd which compares gene expression for two
conditions and returns values for C, S and D for each gene pair.
Also, the underlying
function run_cor_bootstrap is provided
for doing correlation analysis
within a condition,
but is mostly for experimental and expect use only.
The function partial_argsort is
aimed specifically at the task of
selecting the highest values of C, S and D
of the results with minimal computational overhead,
but is written also to be useable for other problems of the same type.
For more details on how to approach, please read the vignette for the package or the individual manpages for the functions.
Author(s)
Maintainer: Jakob Peder Pettersen jakobpeder.pettersen@gmail.com (ORCID)
References
Voigt A, Nowick K and Almaas E 'A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma' In: PLOS Computational Biology 13(9): e1005739. (doi: https://doi.org/10.1371/journal.pcbi.1005739)
See Also
Useful links:
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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