Rafael A Irizarry

Publications of Rafael A Irizarry

• The Partitioned LASSO-Patternsearch Algorithm with Application to Gene Expression Data.

  Authors: Weiliang Shi, Grace Wahba, Rafael A Irizarry, Hector Corrada Bravo, Stephen J Wright

  BMC bioinformatics. 05/2012; 13(1):98.

  ABSTRACT: BACKGROUND: In systems biology, the task of reverse engineering gene pathways from data has been limited not just by the curse of dimensionality (the interaction space is huge) but also byABSTRACT: BACKGROUND: In systems biology, the task of reverse engineering gene pathways from data has been limited not just by the curse of dimensionality (the interaction space is huge) but also by systematic error in the data. The gene expression barcode reduces spurious association driven by batch effects and probe effects. The binary nature of the resulting expression calls lends itself perfectly for modern regularization approaches that thrive with dimensionality. RESULTS: The Partitioned LASSO-Patternsearch algorithm is proposed to identify patterns of multiple dichotomous risk factors for outcomes of interest in genomic studies. A partitioning scheme is used to identify promising patterns by solving many LASSO-Patternsearch subproblems in parallel. All variables that survive this stage proceed to an aggregation stage where the most significant patterns are identified by solving a reduced LASSO-Patternsearch problem in just these variables. This approach was applied to genetic data sets with expression levels dichotomized by gene expression bar code. Most of the genes and second-order interactions thus selected and are known to be related to the outcomes. CONCLUSIONS: We demonstrate with simulations and data analyses that the proposed method not only selects variables and patterns more accurately, but also provides smaller models with better prediction accuracy, in comparison to several competing methodologies.

• DNA methylation shows genome-wide association of NFIX, RAPGEF2 and MSRB3 with gestational age at birth.

  Authors: Hwajin Lee, Andrew E Jaffe, Jason I Feinberg, Rakel Tryggvadottir, Shannon Brown, Carolina Montano, Martin J Aryee, Rafael A Irizarry, Julie Herbstman, Frank R Witter, Lynn R Goldman, Andrew P Feinberg, M Daniele Fallin

  International journal of epidemiology. 02/2012; 41(1):188-199.

  BACKGROUND: Gestational age at birth strongly predicts neonatal, adolescent and adult morbidity and mortality through mostly unknown mechanisms. Identification of specific genes that are undergoingBACKGROUND: Gestational age at birth strongly predicts neonatal, adolescent and adult morbidity and mortality through mostly unknown mechanisms. Identification of specific genes that are undergoing regulatory change prior to birth, such as through changes in DNA methylation, would increase our understanding of developmental changes occurring during the third trimester and consequences of pre-term birth (PTB). METHODS: We performed a genome-wide analysis of DNA methylation (using microarrays, specifically CHARM 2.0) in 141 newborns collected in Baltimore, MD, using novel statistical methodology to identify genomic regions associated with gestational age at birth. Bisulphite pyrosequencing was used to validate significant differentially methylated regions (DMRs), and real-time PCR was performed to assess functional significance of differential methylation in a subset of newborns. RESULTS: We identified three DMRs at genome-wide significance levels adjacent to the NFIX, RAPGEF2 and MSRB3 genes. All three regions were validated by pyrosequencing, and RAGPEF2 also showed an inverse correlation between DNA methylation levels and gene expression levels. Although the three DMRs appear very dynamic with gestational age in our newborn sample, adult DNA methylation levels at these regions are stable and of equal or greater magnitude than the oldest neonate, directionally consistent with the gestational age results. CONCLUSIONS: We have identified three differentially methylated regions associated with gestational age at birth. All three nearby genes play important roles in the development of several organs, including skeletal muscle, brain and haematopoietic system. Therefore, they may provide initial insight into the basis of PTB's negative health outcomes. The genome-wide custom DNA methylation array technology and novel statistical methods employed in this study could constitute a model for epidemiologic studies of epigenetic variation.

• Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

  Authors: Andrew E Jaffe, Peter Murakami, Hwajin Lee, Jeffrey T Leek, M Daniele Fallin, Andrew P Feinberg, Rafael A Irizarry

  International journal of epidemiology. 02/2012; 41(1):200-9.

  During the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studiesDuring the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature. New statistical challenges arise when the measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal, and measurement errors are characterized by spatial correlation along the genome. Perhaps the most challenging complication is that continuous genomic data from large studies are measured throughout long periods, making them susceptible to 'batch effects'. An example that combines all three characteristics is genome-wide DNA methylation measurements. Here, we present a data analysis pipeline that effectively models measurement error, removes batch effects, detects regions of interest and attaches statistical uncertainty to identified regions. We illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with a continuous trait in a well-characterized population of newborns. Additionally, we show that addressing unexplained heterogeneity like batch effects reduces the number of false-positive regions. Our framework offers a comprehensive yet flexible approach for identifying genomic regions of biological interest in large epidemiological studies using quantitative high-throughput methods.

• Removing technical variability in RNA-seq data using conditional quantile normalization.

  Authors: Kasper D Hansen, Rafael A Irizarry, Zhijin Wu

  Biostatistics (Oxford, England). 01/2012;

  The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddledThe ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade's worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show that RNA-seq data demonstrate unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find guanine-cytosine content (GC-content) has a strong sample-specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here, we describe a statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content and quantile normalization to correct for global distortions.

• Genome-wide DNA methylation scan in major depressive disorder.

  Authors: Sarven Sabunciyan, Martin J Aryee, Rafael A Irizarry, Michael Rongione, Maree J Webster, Walter E Kaufman, Peter Murakami, Andree Lessard, Robert H Yolken, Andrew P Feinberg, James B Potash, Genred Consortium

  PloS one. 01/2012; 7(4):e34451.

  While genome-wide association studies are ongoing to identify sequence variation influencing susceptibility to major depressive disorder (MDD), epigenetic marks, such as DNA methylation, which can beWhile genome-wide association studies are ongoing to identify sequence variation influencing susceptibility to major depressive disorder (MDD), epigenetic marks, such as DNA methylation, which can be influenced by environment, might also play a role. Here we present the first genome-wide DNA methylation (DNAm) scan in MDD. We compared 39 postmortem frontal cortex MDD samples to 26 controls. DNA was hybridized to our Comprehensive High-throughput Arrays for Relative Methylation (CHARM) platform, covering 3.5 million CpGs. CHARM identified 224 candidate regions with DNAm differences >10%. These regions are highly enriched for neuronal growth and development genes. Ten of 17 regions for which validation was attempted showed true DNAm differences; the greatest were in PRIMA1, with 12-15% increased DNAm in MDD (p = 0.0002-0.0003), and a concomitant decrease in gene expression. These results must be considered pilot data, however, as we could only test replication in a small number of additional brain samples (n = 16), which showed no significant difference in PRIMA1. Because PRIMA1 anchors acetylcholinesterase in neuronal membranes, decreased expression could result in decreased enzyme function and increased cholinergic transmission, consistent with a role in MDD. We observed decreased immunoreactivity for acetylcholinesterase in MDD brain with increased PRIMA1 DNAm, non-significant at p = 0.08.While we cannot draw firm conclusions about PRIMA1 DNAm in MDD, the involvement of neuronal development genes across the set showing differential methylation suggests a role for epigenetics in the illness. Further studies using limbic system brain regions might shed additional light on this role.

• Thawing Frozen Robust Multi-array Analysis (fRMA).

  Authors: Matthew N McCall, Rafael A Irizarry

  BMC bioinformatics. 09/2011; 12:369.

  A novel method of microarray preprocessing--Frozen Robust Multi-array Analysis (fRMA)--has recently been developed. This algorithm allows the user to preprocess arrays individually while retainingA novel method of microarray preprocessing--Frozen Robust Multi-array Analysis (fRMA)--has recently been developed. This algorithm allows the user to preprocess arrays individually while retaining the advantages of multi-array preprocessing methods. The frozen parameter estimates required by this algorithm are generated using a large database of publicly available arrays. Curation of such a database and creation of the frozen parameter estimates is time-consuming; therefore, fRMA has only been implemented on the most widely used Affymetrix platforms. We present an R package, frmaTools, that allows the user to quickly create his or her own frozen parameter vectors. We describe how this package fits into a preprocessing workflow and explore the size of the training dataset needed to generate reliable frozen parameter estimates. This is followed by a discussion of specific situations in which one might wish to create one's own fRMA implementation. For a few specific scenarios, we demonstrate that fRMA performs well even when a large database of arrays in unavailable. By allowing the user to easily create his or her own fRMA implementation, the frmaTools package greatly increases the applicability of the fRMA algorithm. The frmaTools package is freely available as part of the Bioconductor project.

• Increased methylation variation in epigenetic domains across cancer types.

  Authors: Kasper Daniel Hansen, Winston Timp, Héctor Corrada Bravo, Sarven Sabunciyan, Benjamin Langmead, Oliver G McDonald, Bo Wen, Hao Wu, Yun Liu, Dinh Diep, Eirikur Briem, Kun Zhang, Rafael A Irizarry, Andrew P Feinberg

  Nature genetics. 06/2011; 43(8):768-75.

  Tumor heterogeneity is a major barrier to effective cancer diagnosis and treatment. We recently identified cancer-specific differentially DNA-methylated regions (cDMRs) in colon cancer, which alsoTumor heterogeneity is a major barrier to effective cancer diagnosis and treatment. We recently identified cancer-specific differentially DNA-methylated regions (cDMRs) in colon cancer, which also distinguish normal tissue types from each other, suggesting that these cDMRs might be generalized across cancer types. Here we show stochastic methylation variation of the same cDMRs, distinguishing cancer from normal tissue, in colon, lung, breast, thyroid and Wilms' tumors, with intermediate variation in adenomas. Whole-genome bisulfite sequencing shows these variable cDMRs are related to loss of sharply delimited methylation boundaries at CpG islands. Furthermore, we find hypomethylation of discrete blocks encompassing half the genome, with extreme gene expression variability. Genes associated with the cDMRs and large blocks are involved in mitosis and matrix remodeling, respectively. We suggest a model for cancer involving loss of epigenetic stability of well-defined genomic domains that underlies increased methylation variability in cancer that may contribute to tumor heterogeneity.

• Significance analysis and statistical dissection of variably methylated regions.

  Authors: Andrew E Jaffe, Andrew P Feinberg, Rafael A Irizarry, Jeffrey T Leek

  Biostatistics (Oxford, England). 06/2011; 13(1):166-78.

  It has recently been proposed that variation in DNA methylation at specific genomic locations may play an important role in the development of complex diseases such as cancer. Here, we develop 1- andIt has recently been proposed that variation in DNA methylation at specific genomic locations may play an important role in the development of complex diseases such as cancer. Here, we develop 1- and 2-group multiple testing procedures for identifying and quantifying regions of DNA methylation variability. Our method is the first genome-wide statistical significance calculation for increased or differential variability, as opposed to the traditional approach of testing for mean changes. We apply these procedures to genome-wide methylation data obtained from biological and technical replicates and provide the first statistical proof that variably methylated regions exist and are due to interindividual variation. We also show that differentially variable regions in colon tumor and normal tissue show enrichment of genes regulating gene expression, cell morphogenesis, and development, supporting a biological role for DNA methylation variability in cancer.

• Using the R Package crlmm for Genotyping and Copy Number Estimation.

  Authors: Robert B Scharpf, Rafael A Irizarry, Matthew E Ritchie, Benilton Carvalho, Ingo Ruczinski

  Journal of statistical software. 05/2011; 40(12):1-32.

  Genotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largelyGenotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largely concordant when assessed on HapMap samples, tools to assess copy number changes are more variable and often discordant. One explanation for the discordance is that copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. Analysis algorithms that do not adjust for batch effects are prone to spurious measures of association. The R package crlmm implements a multilevel model that adjusts for batch effects and provides allele-specific estimates of copy number. This paper illustrates a workflow for the estimation of allele-specific copy number and integration of the marker-level estimates with complimentary Bioconductor software for inferring regions of copy number gain or loss. All analyses are performed in the statistical environment R.

• Performance assessment of copy number microarray platforms using a spike-in experiment.

  Authors: Eitan Halper-Stromberg, Laurence Frelin, Ingo Ruczinski, Robert Scharpf, Chunfa Jie, Benilton Carvalho, Haiping Hao, Kurt Hetrick, Anne Jedlicka, Amanda Dziedzic, Kim Doheny, Alan F Scott, Steve Baylin, Jonathan Pevsner, Forrest Spencer, Rafael A Irizarry

  Bioinformatics (Oxford, England). 04/2011; 27(8):1052-60.

  Changes in the copy number of chromosomal DNA segments [copy number variants (CNVs)] have been implicated in human variation, heritable diseases and cancers. Microarray-based platforms are theChanges in the copy number of chromosomal DNA segments [copy number variants (CNVs)] have been implicated in human variation, heritable diseases and cancers. Microarray-based platforms are the current established technology of choice for studies reporting these discoveries and constitute the benchmark against which emergent sequence-based approaches will be evaluated. Research that depends on CNV analysis is rapidly increasing, and systematic platform assessments that distinguish strengths and weaknesses are needed to guide informed choice. We evaluated the sensitivity and specificity of six platforms, provided by four leading vendors, using a spike-in experiment. NimbleGen and Agilent platforms outperformed Illumina and Affymetrix in accuracy and precision of copy number dosage estimates. However, Illumina and Affymetrix algorithms that leverage single nucleotide polymorphism (SNP) information make up for this disadvantage and perform well at variant detection. Overall, the NimbleGen 2.1M platform outperformed others, but only with the use of an alternative data analysis pipeline to the one offered by the manufacturer. The data is available from http://rafalab.jhsph.edu/cnvcomp/. pevsner@jhmi.edu; fspencer@jhmi.edu; rafa@jhu.edu Supplementary data are available at Bioinformatics online.

• Accurate genome-scale percentage DNA methylation estimates from microarray data.

  Authors: Martin J Aryee, Zhijin Wu, Christine Ladd-Acosta, Brian Herb, Andrew P Feinberg, Srinivasan Yegnasubramanian, Rafael A Irizarry

  Biostatistics (Oxford, England). 04/2011; 12(2):197-210.

  DNA methylation is a key regulator of gene function in a multitude of both normal and abnormal biological processes, but tools to elucidate its roles on a genome-wide scale are still in theirDNA methylation is a key regulator of gene function in a multitude of both normal and abnormal biological processes, but tools to elucidate its roles on a genome-wide scale are still in their infancy. Methylation sensitive restriction enzymes and microarrays provide a potential high-throughput, low-cost platform to allow methylation profiling. However, accurate absolute methylation estimates have been elusive due to systematic errors and unwanted variability. Previous microarray preprocessing procedures, mostly developed for expression arrays, fail to adequately normalize methylation-related data since they rely on key assumptions that are violated in the case of DNA methylation. We develop a normalization strategy tailored to DNA methylation data and an empirical Bayes percentage methylation estimator that together yield accurate absolute methylation estimates that can be compared across samples. We illustrate the method on data generated to detect methylation differences between tissues and between normal and tumor colon samples.

• Comparing genotyping algorithms for Illumina's Infinium whole-genome SNP BeadChips.

  Authors: Matthew E Ritchie, Ruijie Liu, Benilton S Carvalho, Rafael A Irizarry

  BMC bioinformatics. 03/2011; 12:68.

  Illumina's Infinium SNP BeadChips are extensively used in both small and large-scale genetic studies. A fundamental step in any analysis is the processing of raw allele A and allele B intensitiesIllumina's Infinium SNP BeadChips are extensively used in both small and large-scale genetic studies. A fundamental step in any analysis is the processing of raw allele A and allele B intensities from each SNP into genotype calls (AA, AB, BB). Various algorithms which make use of different statistical models are available for this task. We compare four methods (GenCall, Illuminus, GenoSNP and CRLMM) on data where the true genotypes are known in advance and data from a recently published genome-wide association study. In general, differences in accuracy are relatively small between the methods evaluated, although CRLMM and GenoSNP were found to consistently outperform GenCall. The performance of Illuminus is heavily dependent on sample size, with lower no call rates and improved accuracy as the number of samples available increases. For X chromosome SNPs, methods with sex-dependent models (Illuminus, CRLMM) perform better than methods which ignore gender information (GenCall, GenoSNP). We observe that CRLMM and GenoSNP are more accurate at calling SNPs with low minor allele frequency than GenCall or Illuminus. The sample quality metrics from each of the four methods were found to have a high level of agreement at flagging samples with unusual signal characteristics. CRLMM, GenoSNP and GenCall can be applied with confidence in studies of any size, as their performance was shown to be invariant to the number of samples available. Illuminus on the other hand requires a larger number of samples to achieve comparable levels of accuracy and its use in smaller studies (50 or fewer individuals) is not recommended.

• Assessments of Affymetrix GeneChip Microarray Quality for Laboratories and Single Samples.

  Authors: Matthew N. McCall, Peter N. Murakami, Margus Lukk, Wolfgang Huber, Rafael A. Irizarry

  BMC Bioinformatics. 01/2011; 12:137.

• Sequencing technology does not eliminate biological variability.

  Authors: Kasper D Hansen, Zhijin Wu, Rafael A Irizarry, Jeffrey T Leek

  Nature biotechnology. 01/2011; 29(7):572-3.

• Comparing genotyping algorithms for Illumina's Infinium whole-genome SNP BeadChips.

  Authors: Matthew E. Ritchie, Ruijie Liu, Benilton Carvalho, Rafael A. Irizarry

  BMC Bioinformatics. 01/2011; 12:68.

• Assessing affymetrix GeneChip microarray quality.

  Authors: Matthew N McCall, Peter N Murakami, Margus Lukk, Wolfgang Huber, Rafael A Irizarry

  BMC bioinformatics. 01/2011; 12:137.

  Microarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondaryMicroarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyses rapidly increasing, we expect this rate to continue. Various initiatives such as the External RNA Control Consortium (ERCC) and the MicroArray Quality Control (MAQC) project have explored ways to provide standards for the technology. For microarrays to become generally accepted as a reliable technology, statistical methods for assessing quality will be an indispensable component; however, there remains a lack of consensus in both defining and measuring microarray quality. We begin by providing a precise definition of microarray quality and reviewing existing Affymetrix GeneChip quality metrics in light of this definition. We show that the best-performing metrics require multiple arrays to be assessed simultaneously. While such multi-array quality metrics are adequate for bench science, as microarrays begin to be used in clinical settings, single-array quality metrics will be indispensable. To this end, we define a single-array version of one of the best multi-array quality metrics and show that this metric performs as well as the best multi-array metrics. We then use this new quality metric to assess the quality of microarry data available via the Gene Expression Omnibus (GEO) using more than 22,000 Affymetrix HGU133a and HGU133plus2 arrays from 809 studies. We find that approximately 10 percent of these publicly available arrays are of poor quality. Moreover, the quality of microarray measurements varies greatly from hybridization to hybridization, study to study, and lab to lab, with some experiments producing unusable data. Many of the concepts described here are applicable to other high-throughput technologies.

• The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes.

  Authors: Matthew N McCall, Karan Uppal, Harris A Jaffee, Michael J Zilliox, Rafael A Irizarry

  Nucleic acids research. 01/2011; 39(Database issue):D1011-5.

  Various databases have harnessed the wealth of publicly available microarray data to address biological questions ranging from across-tissue differential expression to homologous gene expression.Various databases have harnessed the wealth of publicly available microarray data to address biological questions ranging from across-tissue differential expression to homologous gene expression. Despite their practical value, these databases rely on relative measures of expression and are unable to address the most fundamental question--which genes are expressed in a given cell type. The Gene Expression Barcode is the first database to provide reliable absolute measures of expression for most annotated genes for 131 human and 89 mouse tissue types, including diseased tissue. This is made possible by a novel algorithm that leverages information from the GEO and ArrayExpress public repositories to build statistical models that permit converting data from a single microarray into expressed/unexpressed calls for each gene. For selected platforms, users may upload data and obtain results in a matter of seconds. The raw data, curated annotation, and code used to create our resource are also available at http://rafalab.jhsph.edu/barcode.

• Chromosome-wide mapping of DNA methylation patterns in normal and malignant prostate cells reveals pervasive methylation of gene-associated and conserved intergenic sequences.

  Authors: Srinivasan Yegnasubramanian, Zhijin Wu, Michael C Haffner, David Esopi, Martin J Aryee, Raghav Badrinath, Tony L He, James D Morgan, Benilton Carvalho, Qizhi Zheng, Angelo M De Marzo, Rafael A Irizarry, William G Nelson

  BMC genomics. 01/2011; 12:313.

  DNA methylation has been linked to genome regulation and dysregulation in health and disease respectively, and methods for characterizing genomic DNA methylation patterns are rapidly emerging. WeDNA methylation has been linked to genome regulation and dysregulation in health and disease respectively, and methods for characterizing genomic DNA methylation patterns are rapidly emerging. We have developed/refined methods for enrichment of methylated genomic fragments using the methyl-binding domain of the human MBD2 protein (MBD2-MBD) followed by analysis with high-density tiling microarrays. This MBD-chip approach was used to characterize DNA methylation patterns across all non-repetitive sequences of human chromosomes 21 and 22 at high-resolution in normal and malignant prostate cells. Examining this data using computational methods that were designed specifically for DNA methylation tiling array data revealed widespread methylation of both gene promoter and non-promoter regions in cancer and normal cells. In addition to identifying several novel cancer hypermethylated 5' gene upstream regions that mediated epigenetic gene silencing, we also found several hypermethylated 3' gene downstream, intragenic and intergenic regions. The hypermethylated intragenic regions were highly enriched for overlap with intron-exon boundaries, suggesting a possible role in regulation of alternative transcriptional start sites, exon usage and/or splicing. The hypermethylated intergenic regions showed significant enrichment for conservation across vertebrate species. A sampling of these newly identified promoter (ADAMTS1 and SCARF2 genes) and non-promoter (downstream or within DSCR9, C21orf57 and HLCS genes) hypermethylated regions were effective in distinguishing malignant from normal prostate tissues and/or cell lines. Comparison of chromosome-wide DNA methylation patterns in normal and malignant prostate cells revealed significant methylation of gene-proximal and conserved intergenic sequences. Such analyses can be easily extended for genome-wide methylation analysis in health and disease.

• A multilevel model to address batch effects in copy number estimation using SNP arrays.

  Authors: Robert B Scharpf, Ingo Ruczinski, Benilton Carvalho, Betty Doan, Aravinda Chakravarti, Rafael A Irizarry

  Biostatistics (Oxford, England). 01/2011; 12(1):33-50.

  Submicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution thatSubmicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution that permits the detection of segmental changes in DNA copy number that span thousands of base pairs in the genome. Genomewide association studies (GWAS) may simultaneously screen for copy number phenotype and single nucleotide polymorphism (SNP) phenotype associations as part of the analytic strategy. However, genomewide array analyses are particularly susceptible to batch effects as the logistics of preparing DNA and processing thousands of arrays often involves multiple laboratories and technicians, or changes over calendar time to the reagents and laboratory equipment. Failure to adjust for batch effects can lead to incorrect inference and requires inefficient post hoc quality control procedures to exclude regions that are associated with batch. Our work extends previous model-based approaches for copy number estimation by explicitly modeling batch and using shrinkage to improve locus-specific estimates of copy number uncertainty. Key features of this approach include the use of biallelic genotype calls from experimental data to estimate batch-specific and locus-specific parameters of background and signal without the requirement of training data. We illustrate these ideas using a study of bipolar disease and a study of chromosome 21 trisomy. The former has batch effects that dominate much of the observed variation in the quantile-normalized intensities, while the latter illustrates the robustness of our approach to a data set in which approximately 27% of the samples have altered copy number. Locus-specific estimates of copy number can be plotted on the copy number scale to investigate mosaicism and guide the choice of appropriate downstream approaches for smoothing the copy number as a function of physical position. The software is open source and implemented in the R package crlmm at Bioconductor (http:www.bioconductor.org).

• A framework for oligonucleotide microarray preprocessing.

  Authors: Benilton S Carvalho, Rafael A Irizarry

  Bioinformatics (Oxford, England). 10/2010; 26(19):2363-7.

  The availability of flexible open source software for the analysis of gene expression raw level data has greatly facilitated the development of widely used preprocessing methods for theseThe availability of flexible open source software for the analysis of gene expression raw level data has greatly facilitated the development of widely used preprocessing methods for these technologies. However, the expansion of microarray applications has exposed the limitation of existing tools. We developed the oligo package to provide a more general solution that supports a wide range of applications. The package is based on the BioConductor principles of transparency, reproducibility and efficiency of development. It extends the existing tools and leverages existing code for visualization, accessing data and widely used preprocessing routines. The oligo package implements a unified paradigm for preprocessing data and interfaces with other BioConductor tools for downstream analysis. Our infrastructure is general and can be used by other BioConductor packages. The oligo package is freely available through BioConductor, http://www.bioconductor.org.