Bioconductor/R package gprege: Gaussian Process Ranking and Estimation of Gene Expression time-series. The gprege package implements the methodology described in Kalaitzis & Lawrence (2011) ”A simple approach to ranking differentially expressed gene expression time-courses through Gaussian process regression”. The software fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS (Angelini et.al, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (http://www.biomedcentral.com/1471-2105/12/180).
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Valeria Policastro, Dario Righelli, Annamaria Carissimo, Luisa Cutillo, Italia De Feis: ROBustness In Network (robin): an R package for Comparison and Validation of communities (2021) arXiv
- Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
- Mohammadi, Hossein; Challenor, Peter; Goodfellow, Marc: Emulating dynamic non-linear simulators using Gaussian processes (2019)
- Carissimo, Annamaria; Cutillo, Luisa; Feis, Italia De: Validation of community robustness (2018)
- Garbuno-Inigo, A.; DiazDelaO, F. A.; Zuev, K. M.: Gaussian process hyper-parameter estimation using parallel asymptotically independent Markov sampling (2016)
- Butler, A.; Haynes, R. D.; Humphries, T. D.; Ranjan, P.: Efficient optimization of the likelihood function in Gaussian process modelling (2014)
- Kalaitzis, Alfredo A.; Lawrence, Neil D.: A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression (2011) ioport