School of Mathematics and Statistics - Research Publications

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    Predicting qualitative phenotypes from microarray data - the Eadgene pig data set.
    Robert-Granié, C ; Lê Cao, K-A ; Sancristobal, M (Springer Science and Business Media LLC, 2009-07-16)
    BACKGROUND: The aim of this work was to study the performances of 2 predictive statistical tools on a data set that was given to all participants of the Eadgene-SABRE Post Analyses Working Group, namely the Pig data set of Hazard et al. (2008). The data consisted of 3686 gene expressions measured on 24 animals partitioned in 2 genotypes and 2 treatments. The objective was to find biomarkers that characterized the genotypes and the treatments in the whole set of genes. METHODS: We first considered the Random Forest approach that enables the selection of predictive variables. We then compared the classical Partial Least Squares regression (PLS) with a novel approach called sparse PLS, a variant of PLS that adapts lasso penalization and allows for the selection of a subset of variables. RESULTS: All methods performed well on this data set. The sparse PLS outperformed the PLS in terms of prediction performance and improved the interpretability of the results. CONCLUSION: We recommend the use of machine learning methods such as Random Forest and multivariate methods such as sparse PLS for prediction purposes. Both approaches are well adapted to transcriptomic data where the number of features is much greater than the number of individuals.
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    Feasibility, design and conduct of a pragmatic randomized controlled trial to reduce overweight and obesity in children: The electronic games to aid motivation to exercise (eGAME) study.
    Maddison, R ; Foley, L ; Mhurchu, CN ; Jull, A ; Jiang, Y ; Prapavessis, H ; Rodgers, A ; Vander Hoorn, S ; Hohepa, M ; Schaaf, D (Springer Science and Business Media LLC, 2009-05-19)
    BACKGROUND: Childhood obesity has reached epidemic proportions in developed countries. Sedentary screen-based activities such as video gaming are thought to displace active behaviors and are independently associated with obesity. Active video games, where players physically interact with images onscreen, may have utility as a novel intervention to increase physical activity and improve body composition in children. The aim of the Electronic Games to Aid Motivation to Exercise (eGAME) study is to determine the effects of an active video game intervention over 6 months on: body mass index (BMI), percent body fat, waist circumference, cardio-respiratory fitness, and physical activity levels in overweight children. METHODS/DESIGN: Three hundred and thirty participants aged 10-14 years will be randomized to receive either an active video game upgrade package or to a control group (no intervention). DISCUSSION: An overview of the eGAME study is presented, providing an example of a large, pragmatic randomized controlled trial in a community setting. Reflection is offered on key issues encountered during the course of the study. In particular, investigation into the feasibility of the proposed intervention, as well as robust testing of proposed study procedures is a critical step prior to implementation of a large-scale trial.
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    Improved Methodology for Assessment of mRNA Levels in Blood of Patients with FMR1 Related Disorders
    Godler, DE ; Loesch, DZ ; Huggins, R ; Gordon, L ; Slater, HR ; Gehling, F ; Burgess, T ; Choo, KHA (BIOMED CENTRAL LTD, 2009-01-01)
    BACKGROUND: Elevated levels of FMR1 mRNA in blood have been implicated in RNA toxicity associated with a number of clinical conditions. Due to the extensive inter-sample variation in the time lapse between the blood collection and RNA extraction in clinical practice, the resulting variation in mRNA quality significantly confounds mRNA analysis by real-time PCR. METHODS: Here, we developed an improved method to normalize for mRNA degradation in a sample set with large variation in rRNA quality, without sample omission. Initially, RNA samples were artificially degraded, and analyzed using capillary electrophoresis and real-time PCR standard curve method, with the aim of defining the best predictors of total RNA and mRNA degradation. RESULTS: We found that: (i) the 28S:18S ratio and RNA quality indicator (RQI) were good predictors of severe total RNA degradation, however, the greatest changes in the quantity of different mRNAs (FMR1, DNMT1, GUS, B2M and GAPDH) occurred during the early to moderate stages of degradation; (ii) chromatographic features for the 18S, 28S and the inter-peak region were the most reliable predictors of total RNA degradation, however their use for target gene normalization was inferior to internal control genes, of which GUS was the most appropriate. Using GUS for normalization, we examined in the whole blood the relationship between the FMR1 mRNA and CGG expansion in a non-coding portion of this gene, in a sample set (n = 30) with the large variation in rRNA quality. By combining FMR1 3' and 5' mRNA analyses the confounding impact of mRNA degradation on the correlation between FMR1 expression and CGG size was minimized, and the biological significance increased from p = 0.046 for the 5' FMR1 assay, to p = 0.018 for the combined FMR1 3' and 5' mRNA analysis. CONCLUSION: Our observations demonstrate that, through the use of an appropriate internal control and the direct analysis of multiple sites of target mRNA, samples that do not conform to the conventional rRNA criteria can still be utilized to obtain biologically/clinically relevant data. Although, this strategy clearly has application for improved assessment of FMR1 mRNA toxicity in blood, it may also have more general implications for gene expression studies in fresh and archival tissues.
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    The EADGENE microarray data analysis workshop (open access publication)
    De Koning, D-J ; Jaffrezic, F ; Lund, MS ; Watson, M ; Channing, C ; Hulsegge, I ; Pool, MH ; Buitenhuis, B ; Hedegaard, J ; Hornshoj, H ; Jiang, L ; Sorensen, P ; Marot, G ; Delmas, C ; Le Cao, K-A ; Cristobal, MS ; Baron, MD ; Malinverni, R ; Stella, A ; Brunner, RM ; Seyfert, H-M ; Jensen, K ; Mouzaki, D ; Waddington, D ; Jimenez-Marin, A ; Perez-Alegre, M ; Perez-Reinado, E ; Closset, R ; Detilleux, JC ; Dovc, P ; Lavric, M ; Nie, H ; Janss, L (EDP SCIENCES S A, 2007-11-01)
    Microarray analyses have become an important tool in animal genomics. While their use is becoming widespread, there is still a lot of ongoing research regarding the analysis of microarray data. In the context of a European Network of Excellence, 31 researchers representing 14 research groups from 10 countries performed and discussed the statistical analyses of real and simulated 2-colour microarray data that were distributed among participants. The real data consisted of 48 microarrays from a disease challenge experiment in dairy cattle, while the simulated data consisted of 10 microarrays from a direct comparison of two treatments (dye-balanced). While there was broader agreement with regards to methods of microarray normalisation and significance testing, there were major differences with regards to quality control. The quality control approaches varied from none, through using statistical weights, to omitting a large number of spots or omitting entire slides. Surprisingly, these very different approaches gave quite similar results when applied to the simulated data, although not all participating groups analysed both real and simulated data. The workshop was very successful in facilitating interaction between scientists with a diverse background but a common interest in microarray analyses.
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    Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication)
    Watson, M ; Alegre, MP ; Baron, MD ; Delmas, C ; Dovc, P ; Duval, M ; Foulley, JL ; Pavon, JJG ; Hulsegge, I ; Jaffrezic, F ; Marin, AJ ; Lavric, M ; Le Cao, KA ; Marot, G ; Mouzaki, D ; Pool, MH ; Granie, CR ; Cristobal, MS ; Klopp, GT ; Waddington, D ; De Koning, DJ (EDP SCIENCES S A, 2007-11-01)
    Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set.
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    Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication)
    Sorensen, P ; Bonnet, A ; Buitenhuis, B ; Closset, R ; Dejean, S ; Delmas, C ; Duval, M ; Glass, L ; Hedegaard, J ; Hornshoj, H ; Hulsegge, I ; Jaffrezic, F ; Jensen, K ; Jiang, L ; De Koning, D-J ; Le Cao, K-A ; Nie, H ; Petzl, W ; Pool, MH ; Robert-Granie, C ; Cristobal, MS ; Lund, MS ; Van Schothorst, EM ; Schuberth, H-J ; Seyfert, H-M ; Tosser-Klopp, G ; Waddington, D ; Watson, M ; Yang, W ; Zerbe, H (BMC, 2007-11-01)
    The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.
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    Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of diffrentially expressed genes (Open Access publication)
    Jaffrezic, F ; De Koning, D-J ; Boettcher, PJ ; Bonnet, A ; Buitenhuis, B ; Closset, R ; Dejean, S ; Delmas, C ; Detilleux, JC ; Dovc, P ; Duval, M ; Foulley, J-L ; Hedegaard, J ; Hornshoj, H ; Hulsegge, I ; Janss, L ; Jensen, K ; Jiang, L ; Lavric, M ; Le Cao, K-A ; Lund, MS ; Malinverni, R ; Marot, G ; Nie, H ; Petzl, W ; Pool, MH ; Granie, CR ; Cristobal, MS ; Van Schothorst, EM ; Schuberth, H-J ; Sorensen, P ; Stella, A ; Tosser-Klopp, G ; Waddington, D ; Watson, M ; Yang, W ; Zerbe, H ; Seyfert, H-M (EDP SCIENCES S A, 2007-11-01)
    A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.
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    Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data
    Dai, MH ; Wang, PL ; Boyd, AD ; Kostov, G ; Athey, B ; Jones, EG ; Bunney, WE ; Myers, RM ; Speed, TP ; Akil, H ; Watson, SJ ; Meng, F (OXFORD UNIV PRESS, 2005-01-01)
    Genome-wide expression profiling is a powerful tool for implicating novel gene ensembles in cellular mechanisms of health and disease. The most popular platform for genome-wide expression profiling is the Affymetrix GeneChip. However, its selection of probes relied on earlier genome and transcriptome annotation which is significantly different from current knowledge. The resultant informatics problems have a profound impact on analysis and interpretation the data. Here, we address these critical issues and offer a solution. We identified several classes of problems at the individual probe level in the existing annotation, under the assumption that current genome and transcriptome databases are more accurate than those used for GeneChip design. We then reorganized probes on more than a dozen popular GeneChips into gene-, transcript- and exon-specific probe sets in light of up-to-date genome, cDNA/EST clustering and single nucleotide polymorphism information. Comparing analysis results between the original and the redefined probe sets reveals approximately 30-50% discrepancy in the genes previously identified as differentially expressed, regardless of analysis method. Our results demonstrate that the original Affymetrix probe set definitions are inaccurate, and many conclusions derived from past GeneChip analyses may be significantly flawed. It will be beneficial to re-analyze existing GeneChip data with updated probe set definitions.
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    A chain multinomial model for estimating the real-time fatality rate of a disease, with an application to severe acute respiratory syndrome
    Yip, PSF ; Lau, EHY ; Lam, KF ; Huggins, RM (OXFORD UNIV PRESS INC, 2005-04-01)
    It is well known that statistics using cumulative data are insensitive to changes. World Health Organization (WHO) estimates of fatality rates are of the above type, which may not be able to reflect the latest changes in fatality due to treatment or government policy in a timely fashion. Here, the authors propose an estimate of a real-time fatality rate based on a chain multinomial model with a kernel function. It is more accurate than the WHO estimate in describing fatality, especially earlier in the course of an epidemic. The estimator provides useful information for public health policy makers for understanding the severity of the disease or evaluating the effects of treatments or policies within a shorter time period, which is critical in disease control during an outbreak. Simulation results showed that the performance of the proposed estimator is superior to that of the WHO estimator in terms of its sensitivity to changes and its timeliness in reflecting the severity of the disease.
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    Polycomb repressive complex 2 (PRC2) restricts hematopoietic stem cell activity
    Majewski, IJ ; Blewitt, ME ; de Graaf, CA ; McManus, EJ ; Bahlo, M ; Hilton, AA ; Hyland, CD ; Smyth, GK ; Corbin, JE ; Metcalf, D ; Alexander, WS ; Hilton, DJ ; Goodell, MA (PUBLIC LIBRARY SCIENCE, 2008-04-01)
    Polycomb group proteins are transcriptional repressors that play a central role in the establishment and maintenance of gene expression patterns during development. Using mice with an N-ethyl-N-nitrosourea (ENU)-induced mutation in Suppressor of Zeste 12 (Suz12), a core component of Polycomb Repressive Complex 2 (PRC2), we show here that loss of Suz12 function enhances hematopoietic stem cell (HSC) activity. In addition to these effects on a wild-type genetic background, mutations in Suz12 are sufficient to ameliorate the stem cell defect and thrombocytopenia present in mice that lack the thrombopoietin receptor (c-Mpl). To investigate the molecular targets of the PRC2 complex in the HSC compartment, we examined changes in global patterns of gene expression in cells deficient in Suz12. We identified a distinct set of genes that are regulated by Suz12 in hematopoietic cells, including eight genes that appear to be highly responsive to PRC2 function within this compartment. These data suggest that PRC2 is required to maintain a specific gene expression pattern in hematopoiesis that is indispensable to normal stem cell function.