diffuStats: diffuStats: an R package to compute and benchmark diffusion...
In b2slab/diffusion: Diffusion scores on biological networks
Description
Author(s)
References
Description
The diffuStats package consists of (i) functions to compute
graph kernels, see kernels, (ii)
the function diffuse to compute the
diffusion scores and (iii) the function
perf_eval and its wrapper
perf to compute performance measures.
The user can find two vignettes in browseVignettes("diffuStats"):
(1) a quick start with concise examples and (2)
a detailed explanation of the
implemented methods with a practical case study
using a yeast protein dataset.
Author(s)
Sergio Picart-Armada <sergi.picart@upc.edu>, Alexandre Perera-Lluna
References
General references:
Most of the graph kernels can be found in:
Smola, A. J., & Kondor, R. (2003, August).
Kernels and regularization on graphs.
In COLT (Vol. 2777, pp. 144-158).
The statistical normalisation of the diffusion scores,
which has interest per se, has been introduced in:
Bersanelli, M., Mosca, E., Remondini, D.,
Castellani, G., & Milanesi, L. (2016).
Network diffusion-based analysis of high-throughput data
for the detection of differentially enriched modules.
Scientific reports, 6.
b2slab/diffusion documentation built on March 6, 2021, 2:38 a.m.
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Description Author(s) References
Description
The diffuStats package consists of (i) functions to compute
graph kernels, see kernels, (ii)
the function diffuse to compute the
diffusion scores and (iii) the function
perf_eval and its wrapper
perf to compute performance measures.
The user can find two vignettes in browseVignettes("diffuStats"):
(1) a quick start with concise examples and (2)
a detailed explanation of the
implemented methods with a practical case study
using a yeast protein dataset.
Author(s)
Sergio Picart-Armada <sergi.picart@upc.edu>, Alexandre Perera-Lluna
References
General references:
Most of the graph kernels can be found in: Smola, A. J., & Kondor, R. (2003, August). Kernels and regularization on graphs. In COLT (Vol. 2777, pp. 144-158).
The statistical normalisation of the diffusion scores, which has interest per se, has been introduced in: Bersanelli, M., Mosca, E., Remondini, D., Castellani, G., & Milanesi, L. (2016). Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules. Scientific reports, 6.
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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