Chris Holmes
Contact information
chris.holmes@stats.ox.ac.uk  
Tel  +44 (0)1865 285874 
PA  +44 (0)1865 272875 
Chris Holmes
Professors of Biostatistics in Genomics and Group Head / Principal Investigator
I have a broad interest in the theory, methods and applications of statistics and statistical modelling. My background and beliefs lie in Bayesian statistics which provides a unified framework to stochastic modelling and information processing. I am particularly interested in pattern recognition and nonlinear, nonparametric methods.
I moved to Oxford from Imperial College London in February 2004. At Imperial College I studied for my doctorate in Bayesian statistics, investigating novel nonlinear pattern recognition methods. This was followed by a postdoctoral position and then a lectureship at Imperial. Previous to this I worked in industry for a number of years researching in scientific computing, developing techniques for realtime pattern recognition models in defence and SCADA (Supervisory Control and Data Acquisition) systems. My current research is focussed on applications and statistical methods development in the genomic sciences and genetic epidemiology. I hold a programme leaders grant in Statistical Genomics from the Medical Research Council.

The nature and nurture of cell heterogeneity: accounting for macrophage geneenvironment interactions with singlecell RNASeq.
Wills QF. et al, (2017), BMC Genomics, 18

A note on statistical repeatability and study design for highthroughput assays.
Nicholson G. and Holmes C., (2016), Stat Med

Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence
Watson J. et al, (2016), Statistics, 1  14

Statistical Inference in Hidden Markov Models Using kSegment Constraints
Titsias MK. et al, (2016), Journal of the American Statistical Association, 111, 200  215

Scalable bayesian nonparametric regression via a PlackettLuce model for conditional ranks
GrayDavies T. et al, (2016), Electronic Journal of Statistics, 10, 1807  1828

Rejoinder: Approximate models and robust decisions
Watson J. and Holmes C., (2016), Statistical Science, 31, 516  520

Distinct roles of copy number and lossofheterozygosity in predicting prognosis for breast cancer patients
van Stiphout RGPM. et al, (2015), CANCER RESEARCH, 75

Analysis of mammalian gene function through broadbased phenotypic screens across a consortium of mouse clinics.
Hrabě de Angelis M. et al, (2015), Nat Genet, 47, 969  978

Factors influencing success of clinical genome sequencing across a broad spectrum of disorders.
Taylor JC. et al, (2015), Nat Genet, 47, 717  726

Twosample Bayesian nonparametric hypothesis testing
Holmes CC. et al, (2015), Bayesian Analysis, 10, 297  320

Towards Response Prediction Using Integrated Genomics in Chronic Lymphocytic Leukaemia: Results on 250 FirstLine FCR Treated Patients from UK Clinical Trials
Clifford RM. et al, (2014), BLOOD, 124

The Identification of Further Minimal Regions of Overlap in Chronic Lymphocytic Leukemia Using HighResolution SNP Arrays
Knight SJL. et al, (2014), BLOOD, 124

Distinct developmental profile of lowerbody adipose tissue defines resistance against obesityassociated metabolic complications.
Pinnick KE. et al, (2014), Diabetes, 63, 3785  3797

Erythrocytosis associated with a novel missense mutation in the BPGM gene.
Petousi N. et al, (2014), Haematologica, 99, e201  e204

Wholegenome sequencing of bladder cancers reveals somatic CDKN1A mutations and clinicopathological associations with mutation burden.
Cazier JB. et al, (2014), Nat Commun, 5

Estimation of malaria haplotype and genotype frequencies: a statistical approach to overcome the challenge associated with multiclonal infections.
Taylor AR. et al, (2014), Malar J, 13

Towards scaling up Markov chain Monte Carlo: An adaptive subsampling approach
Bardenet R. et al, (2014), 31st International Conference on Machine Learning, ICML 2014, 1, 630  653

Survival in stage II/III colorectal cancer is independently predicted by chromosomal and microsatellite instability, but not by specific driver mutations.
Mouradov D. et al, (2013), Am J Gastroenterol, 108, 1785  1793

Survival in stage II/III colorectal cancer is independently predicted by chromosomal and microsatellite instability, but not by specific driver mutations
Mouradov D. et al, (2013), American Journal of Gastroenterology, 108, 1785  1793

Statistical estimation of malaria genotype frequencies: a Bayesian approach
Taylor A. et al, (2013), TROPICAL MEDICINE & INTERNATIONAL HEALTH, 18, 67  67

A decisiontheoretic approach for segmental classification
Yau C. and Holmes C., (2013), Annals of Applied Statistics, 7, 1814  1835

Singlecell gene expression analysis reveals genetic associations masked in wholetissue experiments.
Wills QF. et al, (2013), Nat Biotechnol, 31, 748  752

NucleoFinder: a statistical approach for the detection of nucleosome positions.
Becker J. et al, (2013), Bioinformatics, 29, 711  716

The Presence of Methylation Quantitative Trait Loci Indicates a Direct Genetic Influence on the Level of DNA Methylation in Adipose Tissue
Drong AW. et al, (2013), PLoS ONE, 8

Germline mutations affecting the proofreading domains of POLE and POLD1 predispose to colorectal adenomas and carcinomas.
Palles C. et al, (2013), Nat Genet, 45, 136  144

Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells
Livak KJ. et al, (2013), Methods, 59, 71  79

Integrative networkbased Bayesian analysis of diverse genomics data.
Wang W. et al, (2013), BMC Bioinformatics, 14 Suppl 13

GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples.
Cazier JB. et al, (2012), Bioinformatics, 28, 2981  2982

Accounting for Control Mislabelling in Casecontrol Biomarker Studies
Rantalainen M. and Holmes C., (2012), GENETIC EPIDEMIOLOGY, 36, 746  746

Reprioritizing genetic associations in hit regions using LASSObased resample model averaging.
Valdar W. et al, (2012), Genet Epidemiol, 36, 451  462

Quantification of subclonal distributions of recurrent genomic aberrations in paired pretreatment and relapse samples from patients with Bcell chronic lymphocytic leukemia.
Knight SJ. et al, (2012), Leukemia, 26, 1564  1575

nEASE: a method for gene ontology subclassification of highthroughput gene expression data.
Chittenden TW. et al, (2012), Bioinformatics, 28, 726  728

Robust Statistical Methods For GenomeWide Eqtl Analysis
Rantalainen M. and Holmes C., (2012), GENETIC EPIDEMIOLOGY, 36, 140  140

Optimization Under Unknown Constraints
Gramacy RB. et al, (2012), Bayesian Statistics 9

Bayesian sparsitypathanalysis of genetic association signal using generalized t priors.
Lee A. et al, (2012), Stat Appl Genet Mol Biol, 11

Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes.
Min JL. et al, (2012), PLoS Genet, 8

A novel test for geneancestry interactions in genomewide association data.
Davies JL. et al, (2012), PLoS One, 7

Accounting for control mislabeling in casecontrol biomarker studies.
Rantalainen M. and Holmes CC., (2011), J Proteome Res, 10, 5562  5567

Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array.
Cardin N. et al, (2011), Genet Epidemiol, 35, 536  548

A genomewide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection.
Nicholson G. et al, (2011), PLoS Genet, 7

Human metabolic profiles are stably controlled by genetic and environmental variation.
Nicholson G. et al, (2011), Mol Syst Biol, 7

Stochastic boosting algorithms
Jasra A. and Holmes CC., (2011), Statistics and Computing, 21, 335  347

Response to van der Lans
Holmes C. and Held L., (2011), Bayesian Analysis, 6, 357  358

Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination
Yau C. and Holmes C., (2011), Bayesian Analysis, 6, 329  352

Variance decomposition of protein profiles from antibody arrays using a lonitudial twin model
Kato B. et al, (2011), Journal of Proteome Research, 9

MicroRNA expression in abdominal and gluteal adipose tissue is associated with mRNA expression levels and partly genetically driven.
Rantalainen M. et al, (2011), PLoS One, 6

Bayesian nonparametric hidden Markov models with applications in genommics
Yau C. et al, (2011), Journal of the Royal Statistical Society Series B: Statistical Methodology, 73, 33  57

Bayesian Estimation of the Multinomial Logit Model: A Comment on Holmes and Held, "Bayesian Auxiliary Variable Models for Binary and Multinomial Regression" Response
Holmes C. and Held L., (2011), BAYESIAN ANALYSIS, 6, 357  358

Application of a novel score test for genetic association incorporating genegene interaction suggests functionality for prostate cancer susceptibility regions.
Ciampa J. et al, (2011), Hum Hered, 72, 182  193

Application of a noval score test for genetic association incorporating genegene interaction suggests functionality for prostate cancer susceptiblity regions
Ciampa J. et al, (2011), Human Heredity, 3, 182  193

Therapeutic implications of GIPC1 silencing in cancer.
Chittenden TW. et al, (2010), PLoS One, 5

On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods
Lee A. et al, (2010), Journal of Computational and Graphical Statistics, 4, 769  789

Quantitative Whole Genome Analysis of Sequential Samples From Patients with B CLL Identifies Novel Recurrent Copy Number Alterations Involving Critical B Cell Transcription Factors
Timbs A. et al, (2010), BLOOD, 116, 1478  1478

Elusive copy number variation in the mouse genome.
Agam A. et al, (2010), PLoS One, 5

Microribonucleic acid expression profiling and expression quantitative trait loci analysis in human gluteal and abdominal adipose tissue
Rantalainen M. et al, (2010), DIABETOLOGIA, 53

A Bayesian approach using covariance of single nucleotide polymorphism data to detect differences in linkage disequilibrium patterns between groups of individuals.
Clark TG. et al, (2010), Bioinformatics (Oxford, England), 26, 1999  2003

Metaanalysis identifies 13 novel loci associated with waisthip ratio and reveals sexual dimorphism in the genetic basis of fat distribution
Heid IM. et al, (2010), Nat.Genet.

Detecting interacting genetic loci with effects on quantitative traits where the nature and order of the interaction are unknown.
Davies JL. et al, (2010), Genet Epidemiol, 34, 299  308

Genomewide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls
Craddock N. et al, (2010), Nature, 464, 713  720

Bayesian Nonparametrics
Holmes C., (2010)

A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data
Yau C. et al, (2010), Genome Biology, 11

Antithetic methods for Gibbs samplers
Holmes C. and Jasra A., (2009), Journal of Computational and Graphical Statistics, 18, 401  414

A boosting approach to structure learning of graphs with and without prior knowledge.
Anjum S. et al, (2009), Bioinformatics, 25, 2929  2936

Analysis of the mouse mutant Clothears shows a role for the voltagegated sodium channel Scn8a in peripheral neural hearing loss.
Mackenzie FE. et al, (2009), Genes Brain Behav, 8, 699  713

Mapping in structured populations by resample model averaging
Valdar W. et al, (2009), Genetics, 182, 1263  1277

Reply to Wirth et al.: In vivo profiles show continuous variation between 2 cellular populations
Lemieux JE. et al, (2009), Proceedings of the National Academy of Sciences of the United States of America, 106

On testing for genetic association in casecontrol studies when population allele frequencies are known.
Antonyuk A. and Holmes C., (2009), Genet Epidemiol, 33, 371  378

Approximate Bayesian feature selection on a large metadataset offers novel insights on factors that effect siRNA potency.
Klingelhoefer JW. et al, (2009), Bioinformatics, 25, 1594  1601

Statistical estimation of cellcycle progression and lineage commitment in Plasmodium falciparum reveals a homogeneous pattern of transcription in ex vivo culture.
Lemieux JE. et al, (2009), Proc Natl Acad Sci U S A, 106, 7559  7564

Phylogenetic inference under recombination using Bayesian stochastic topology selection.
Webb A. et al, (2009), Bioinformatics, 25, 197  203

GenoSNP: a variational Bayes withinsample SNP genotyping algorithm that does not require a reference population.
Giannoulatou E. et al, (2008), Bioinformatics, 24, 2209  2214

Key issues in conducting a metaanalysis of gene expression microarray datasets.
Ramasamy A. et al, (2008), PLoS Med, 5

Interacting sequential Monte Carlo samplers for transdimensional simulation
Jasra A. et al, (2008), Computational Statistics and Data Analysis, 52, 1765  1791

CNV discovery using SNP genotyping arrays.
Yau C. and Holmes CC., (2008), Cytogenet Genome Res, 123, 307  312

Populationbased reversible jump Markov chain Monte Carlo
Jasra A. et al, (2007), Biometrika, 94, 787  807

Turbo Genomic Control
Astle LJ. et al, (2007), GENETIC EPIDEMIOLOGY, 31, 605  605

On populationbased simulation for static inference
Jasra A. et al, (2007), Statistics and Computing, 17, 263  279

Turbo Genomic Control
Astle W. et al, (2007), ANNALS OF HUMAN GENETICS, 71, 553  554

Flexible threshold models for modelling interest rate volatility
Dellaportas P. et al, (2007), Econometric Reviews, 26, 419  437

QuantiSNP: an Objective Bayes HiddenMarkov Model to detect and accurately map copy number variation using SNP genotyping data.
Colella S. et al, (2007), Nucleic Acids Res, 35, 2013  2025

A Bayesian approach to calibrating apatite fission track annealing models for laboratory and geological timescales
Stephenson J. et al, (2006), Geochimica et Cosmochimica Acta, 70, 5183  5200

Modulation of the BK channel by estrogens: examination at single channel level.
De Wet H. et al, (2006), Mol Membr Biol, 23, 420  429

Bayesian mixture modelling in geochronology via Markov chain Monte Carlo
Jasra A. et al, (2006), Mathematical Geology, 38, 269  300

Spatially adaptive smoothing splines
Pintore A. et al, (2006), Biometrika, 93, 113  125

A quantitative study of gene regulation involved in the immune response of Anopheline mosquitoes: An application of Bayesian hierarchical clustering of curves
Heard NA. et al, (2006), Journal of the American Statistical Association, 101, 18  29

Low temperature thermochronology and strategies for multiple samples 2: Partition modelling for 2D/3D distributions with discontinuities
Stephenson J. et al, (2006), EARTH AND PLANETARY SCIENCE LETTERS, 241, 557  570

Putting the data to work  strategies for modelling multiple samples zin multiple dimensions
Gallagher K. et al, (2006), GEOCHIMICA ET COSMOCHIMICA ACTA, 70, A190  A190

Bayesian Auxiliary Variable Models for Binary and Multinomial Regression
Holmes CC. and Held L., (2006), BAYESIAN ANALYSIS, 1, 145  168

A new approach to mixture modelling for geochronology
Gallagher K. et al, (2006), GEOCHIMICA ET COSMOCHIMICA ACTA, 70, A190  A190

Exploiting 3D spatial sampling in inverse modeling of thermochronological data
Gallagher K. et al, (2005), Reviews in Mineralogy and Geochemistry, 58, 375  387

Bayesian prediction via partitioning
Holmes CC. et al, (2005), Journal of Computational and Graphical Statistics, 14, 811  830

Bayesian coclustering of Anopheles gene expression time series: study of immune defense response to multiple experimental challenges.
Heard NA. et al, (2005), Proc Natl Acad Sci U S A, 102, 16939  16944

Low temperature thermochronology and modeling strategies for multiple samples 1: Vertical profiles
Gallagher K. et al, (2005), Earth and Planetary Science Letters, 237, 193  208

Analyzing nonstationary spatial data using piecewise Gaussian processes
Kim HM. et al, (2005), Journal of the American Statistical Association, 100, 653  668

Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
Jasra A. et al, (2005), Statistical Science, 20, 50  67

A statistical technique for modelling nonstationary spatial processes
Stephenson J. et al, (2005), Geostatistics Banff 2004, Vols 1 and 2, 14, 125  134

A dimensionreduction approach for spectral tempering using empirical orthogonal functions
Pintore A. and Holmes CC., (2005), GEOSTATISTICS BANFF 2004, VOLS 1 AND 2, 14, 1007  1015

Beyond kriging: Dealing with discontinuous spatial data fields using adaptive prior information and Bayesian partition modelling
Stephenson J. et al, (2004), Geological Society Special Publication, 239, 195  209

All systems GO for understanding mouse gene function.
Holmes C. and Brown SD., (2004), J Biol, 3

Generalized nonlinear modeling with multivariate freeknot regression splines
Holmes CC. and Mallick BK., (2003), Journal of the American Statistical Association, 98, 352  368

Likelihood inference in nearestneighbour classification models
Holmes CC. and Adams NM., (2003), Biometrika, 90, 99  112

Generalized monotonic regression using random change points.
Holmes CC. and Heard NA., (2003), Stat Med, 22, 623  638

Classification with Bayesian MARS
Holmes CC. and Denison DGT., (2003), Machine Learning, 50, 159  173

A probabilistic nearest neighbour method for statistical pattern recognition
Holmes CC. and Adams NM., (2002), Journal of the Royal Statistical Society. Series B: Statistical Methodology, 64, 295  306

Accounting for model uncertainty in seemingly unrelated regressions
Holmes CC. et al, (2002), Journal of Computational and Graphical Statistics, 11, 533  551

Perfect simulation involving functionals of a Dirichlet process
Guglielmi A. et al, (2002), JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 11, 306  310

Bayesian partition modelling
Denison DGT. et al, (2002), Computational Statistics and Data Analysis, 38, 475  485

Spline adaptation in extended linear models  Comments and rejoinder
Chipman HA. et al, (2002), STATISTICAL SCIENCE, 17, 20  51

Perfect sampling for the wavelet reconstruction of signals
Holmes C. and Denison DGT., (2002), IEEE Transactions on Signal Processing, 50, 337  344

Probabilistic freeknots splines. Invited discussant on gSpline adaptation in extended linear models by Hansen M and Kooperberg C
Holmes C., (2002), 17, 22  24

Bayesian partitioning for estimating disease risk.
Denison DG. and Holmes CC., (2001), Biometrics, 57, 143  149

Minimumentropy data partitioning using reversible jump Markov chain Monte Carlo
Roberts SJ. et al, (2001), Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23, 909  914

Minimumentropy data clustering using reversible jump Markov chain Monte Carlo
Roberts SJ. et al, (2001), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2130, 103  110

Minimumentropy data clustering using reversible jump Markov chain Monte Carlo
Roberts S. et al, (2001), Artificial Neural Networks—ICANN 2001, 103  110

Bayesian regression with multivariate linear splines
Holmes CC. and Mallick BK., (2001), JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES BSTATISTICAL METHODOLOGY, 63, 3  17

Bayesian wavelet networks for nonparametric regression.
Holmes CC. and Mallick BK., (2000), IEEE Trans Neural Netw, 11, 27  35

Bayesian wavelet analysis with a model complexity prior
Holmes CC. and Denison DGT., (1999), BAYESIAN STATISTICS 6, 769  776

Bayesian Radial Basis Functions of Variable Dimension
Holmes CC. and Mallick BK., (1998), Neural Computation, 10, 1217  1233

Modelbased geostatistics  Discussion
Webster R. et al, (1998), JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES CAPPLIED STATISTICS, 47, 326  350

Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures
Filippi SL. et al, Electronic Journal of Statistics

On Markov chain Monte Carlo Methods for Tall Data
Doucet A. et al, Journal of Machine Learning Research

Invited discussion on Regression shrinkage and selection via the lasso: a retrospective by R Tibsharani
Holmes CC., Journal of Royal Statistical Society Series B, 73, 279  280

HodgkinHuxley revisited: reparameterization and identifiability analysis of the classic action potential model with approximate Bayesian methods
Daly AC. et al, Royal Society Open Science

Bayesian wavelet networks for nonparametric regression
Holmes C. and Mallick B., IEEE Transactions on Neural Networks, 11, 27  35

Approximate models and robust decisions
Holmes CC. and Watson J., Statistical Science: a review journal

A General Framework for Updating Belief Distributions
Holmes CC., Journal of the Royal Statistical Society Series B: Statistical Methodology

A Bayesian nonparametric approach to testing for dependence between random variables
Filippi SL. and Holmes C., Bayesian Analysis
Recent Publications
5
The nature and nurture of cell heterogeneity: accounting for macrophage geneenvironment interactions with singlecell RNASeq.
Wills QF. et al, (2017), BMC Genomics, 18

A note on statistical repeatability and study design for highthroughput assays.
Nicholson G. and Holmes C., (2016), Stat Med

Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence
Watson J. et al, (2016), Statistics, 1  14

Statistical Inference in Hidden Markov Models Using kSegment Constraints
Titsias MK. et al, (2016), Journal of the American Statistical Association, 111, 200  215

Scalable bayesian nonparametric regression via a PlackettLuce model for conditional ranks
GrayDavies T. et al, (2016), Electronic Journal of Statistics, 10, 1807  1828