ABBA: Approximate Bayesian Bisulphite sequencing Analysis
Rackham O, Langley S, Oates T, Vradi E, Harmston N, Srivastava P, et al. (2017) A Bayesian Approach for Analysis of Whole-Genome Bisulphite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation. Genetics.
causalSignalingNetworkCode : Code to reproduce the causal signaling network analysis in Hill, Nesser et al. (2017).
Hill SM, Nesser NK, et al. (2017) Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling,. Cell Systems 4(1), 73–83.e10.
PCHIC-specificityScoreCode: Script to reproduce the specificity score analysis in the paper “Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters” by Javierre, Burren, Wilder, Kreuzhuber, Hill et al. (2016).
Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, et al. (2016) Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters,. Cell 167(5), 1369–1384.e19.
retroCode: Code for reproducing analyses from the paper “Retroviruses integrate into a shared, non-palindromic DNA motif” by Kirk et al. (2016).
Kirk PDW, Huvet M, Melamed A, Maertens GN & Bangham, CRM. (2016) Retroviruses integrate into a shared, non-palindromic DNA motif,. Nature Microbiology 2, 16212.
gene-cocite: Web application for extracting, visualising and assessing the cocitations of a list of genes.
MDI-GPU: GPU accelerated Multiple Dataset Integration.
Mason S, Sayyid F, Kirk PDW, Starr C, Wild DL (2016). MDI-GPU: Accelerating integrative modelling for genomic-scale data using GP-GPU computing. Stat. Appl. Genet. Mol. Biol. 15(1).
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data.
Vallejos CA, Marioni JC & Richardson S. (2015) BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. PLoS computational biology. 11(6):e1004333.
Vallejos, CA, Richardson S, & Marioni JC. (2016). Beyond comparisons of means: understanding changes in gene expression at the single-cell level. Genome Biology, 17, 70.
METAMATCHED: Database of predicted gene regulatory relationships inferred from a meta-analysis of 31 matched aCGH and transcriptomics datasets.
Newton R, Wernisch L (2015) Investigating inter-chromosomal regulatory relationships through a comprehensive meta-analysis of matched copy number and transcriptomics data sets. BMC Genomics 16:967.
PReMiuM: Profile Regression Mixture Models Using Dirichlet Processes.
Liverani S, Hastie DI, Azizi L, Papathomas M, Richardson S. (2015) PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes. Journal of Statistical Software, 64 (7) 1-30.
R2BGLiMS: Weibull regression with Bayesian variable selection.
Newcombe, P. J., Raza Ali, H., Blows, F. M., Provenzano, E., Pharoah, P. D., Caldas, C., & Richardson, S. (2014). Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival. Statistical Methods in Medical Research.
R2GUESS: GPU-enabled sparse Bayesian variable selection method for linear regression based analysis of possibly multivariate/correlated outcomes.
Bottolo L, Chadeau-Hyam M, Hastie DI, Zeller T, Liquet B, et al. (2013) GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm. PLoS Genet 9(8): e1003657.
R2HESS: Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues.
Lewin, A., Saadi, H., Peters, J. E., Moreno-Moral, A., Lee, J. C., Smith, K. G. C., et al. (2015). MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues. Bioinformatics (Oxford, England).
Please click here to download the package: R2HESS_1.0.1.tar.gz
Rwui: Used to create web interfaces for running R scripts.
Newton R, Wernisch L (2007) Rwui: A web application to create user friendly web interfaces for R scripts. R News, 7(2):32–35, October.
ShrinkNet: Gene network reconstruction using global-local shrinkage priors.
Preprint: Gwenaël G. R. Leday, Mathisca C. M. de Gunst, Gino B. Kpogbezan, Aad W. Van der Vaart, Wessel N. Van Wieringen, Mark A. Van de Wiel (2015) Gene network reconstruction using global-local shrinkage priors. arXiv:1510.03771
STEME: EM to find motifs in large data sets.
Reid JE, Wernisch L (2011) STEME: efficient EM to find motifs in large data sets. Nucl. Acids Res. 39(18).
infpy: A Python library which implements variational Bayes inference techniques, in particular nonlinear regression and classification using Gaussian processes.
pybool: A Python library for inferring Boolean regulatory networks from temporal expression constraints.
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- Lecture 2, Slides, Practical 2 and Solution to Practical 2. (Modelling Correlated Non-normal Data). Accompanying Data-sets: Epileptic Seizures and Respiratory Illness
- Lecture 3, Slides, Practical 3 and Solution to Practical 3. (Survival Data Analysis). Accompanying Data-set:
- Lecture 4, Slides, Practical 4 and Solution to Practical 4. (Multi-state Markov Modelling of Incomplete Event History Data).