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Andrew J. Holloway

Bio: Andrew J. Holloway is an academic researcher from Peter MacCallum Cancer Centre. The author has contributed to research in topics: Gene expression profiling & Ubiquitin ligase. The author has an hindex of 18, co-authored 24 publications receiving 3285 citations. Previous affiliations of Andrew J. Holloway include Walter and Eliza Hall Institute of Medical Research & Foundation Center.

Papers
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Journal ArticleDOI
TL;DR: The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates, and methods which stabilize the variances of the log-ratios along the intensity range perform the best.
Abstract: Motivation: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. Results: Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data. Availability: The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. Contact: smyth@wehi.edu.au Supplementary information: Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.

946 citations

Journal ArticleDOI
TL;DR: The ability of HDACi to target multiple apoptotic and cell proliferation pathways may provide a competitive advantage over other chemotherapeutic agents because suppression/loss of a single pathway may not confer resistance to these agents.
Abstract: Histone deacetylase inhibitors (HDACis) inhibit tumor cell growth and survival, possibly through their ability to regulate the expression of specific proliferative and/or apoptotic genes. However, the HDACi-regulated genes necessary and/or sufficient for their biological effects remain undefined. We demonstrate that the HDACis suberoylanilide hydroxamic acid (SAHA) and depsipeptide regulate a highly overlapping gene set with at least 22% of genes showing altered expression over a 16-h culture period. SAHA and depsipeptide coordinately regulated the expression of several genes within distinct apoptosis and cell cycle pathways. Multiple genes within the Myc, type β TGF, cyclin/cyclin-dependent kinase, TNF, Bcl-2, and caspase pathways were regulated in a manner that favored induction of apoptosis and decreased cellular proliferation. APAF-1, a gene central to the intrinsic apoptotic pathway, was induced by SAHA and depsipeptide and shown to be important, but not essential, for HDACi-induced cell death. Overexpression of p16INK4A and arrest of cells in G1 can suppress HDACi-mediated apoptosis. Although p16INK4A did not affect the genome-wide transcription changes mediated by SAHA, a small number of apoptotic genes, including BCLXL and B-MYB, were differentially regulated in a manner consistent with attenuated HDACi-mediated apoptosis in arrested cells. We demonstrate that different HDACi alter transcription of a large and common set of genes that control diverse molecular pathways important for cell survival and proliferation. The ability of HDACi to target multiple apoptotic and cell proliferation pathways may provide a competitive advantage over other chemotherapeutic agents because suppression/loss of a single pathway may not confer resistance to these agents.

529 citations

Journal ArticleDOI
TL;DR: A graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis.
Abstract: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment. This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication.

296 citations

Journal ArticleDOI
TL;DR: The most recent equipment, reagents and protocols available to the researcher are discussed, as well as describing data analysis and storage options available from the evolving field of microarray informatics.
Abstract: Microarray technology has undergone a rapid evolution. With widespread interest in large-scale genomic research, an abundance of equipment and reagents have now become available and affordable to a large cross section of the scientific community. As protocols become more refined, careful investigators are able to obtain good quality microarray data quickly. In most recent times, however, perhaps one of the biggest obstacles researchers face is not the manufacture and use of microarrays at the bench, but storage and analysis of the array data. This review discusses the most recent equipment, reagents and protocols available to the researcher, as well as describing data analysis and storage options available from the evolving field of microarray informatics.

271 citations

Journal ArticleDOI
TL;DR: This work builds a clinically robust site of origin classifier with the ultimate aim of applying it to determine the origin of cancer of unknown primary (CUP), and shows that the microarray SVM classifier was capable of making high confidence predictions in 11 of 13 cases.
Abstract: Gene expression profiling offers a promising new technique for the diagnosis and prognosis of cancer. We have applied this technology to build a clinically robust site of origin classifier with the ultimate aim of applying it to determine the origin of cancer of unknown primary (CUP). A single cDNA microarray platform was used to profile 229 primary and metastatic tumors representing 14 tumor types and multiple histologic subtypes. This data set was subsequently used for training and validation of a support vector machine (SVM) classifier, demonstrating 89% accuracy using a 13-class model. Further, we show the translation of a five-class classifier to a quantitative PCR-based platform. Selecting 79 optimal gene markers, we generated a quantitative-PCR low-density array, allowing the assay of both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue. Data generated using both quantitative PCR and microarray were subsequently used to train and validate a cross-platform SVM model with high prediction accuracy. Finally, we applied our SVM classifiers to 13 cases of CUP. We show that the microarray SVM classifier was capable of making high confidence predictions in 11 of 13 cases. These predictions were supported by comprehensive review of the patients' clinical histories.

224 citations


Cited by
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Journal ArticleDOI
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

22,147 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Book ChapterDOI
01 Jan 2005
TL;DR: This chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments with technical as well as biological replication.
Abstract: A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data.

5,920 citations

Journal ArticleDOI
Adam J. Bass1, Vesteinn Thorsson2, Ilya Shmulevich2, Sheila Reynolds2  +254 moreInstitutions (32)
11 Sep 2014-Nature
TL;DR: A comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project is described and a molecular classification dividing gastric cancer into four subtypes is proposed.
Abstract: Gastric cancer was the world’s third leading cause of cancer mortality in 2012, responsible for 723,000 deaths1. The vast majority of gastric cancers are adenocarcinomas, which can be further subdivided into intestinal and diffuse types according to the Lauren classification2. An alternative system, proposed by the World Health Organization, divides gastric cancer into papillary, tubular, mucinous (colloid) and poorly cohesive carcinomas3. These classification systems have little clinical utility, making the development of robust classifiers that can guide patient therapy an urgent priority. The majority of gastric cancers are associated with infectious agents, including the bacterium Helicobacter pylori4 and Epstein–Barr virus (EBV). The distribution of histological subtypes of gastric cancer and the frequencies of H. pylori and EBV associated gastric cancer vary across the globe5. A small minority of gastric cancer cases are associated with germline mutation in E-cadherin (CDH1)6 or mismatch repair genes7 (Lynch syndrome), whereas sporadic mismatch repair-deficient gastric cancers have epigenetic silencing of MLH1 in the context of a CpG island methylator phenotype (CIMP)8. Molecular profiling of gastric cancer has been performed using gene expression or DNA sequencing9–12, but has not led to a clear biologic classification scheme. The goals of this study by The Cancer Genome Atlas (TCGA) were to develop a robust molecular classification of gastric cancer and to identify dysregulated pathways and candidate drivers of distinct classes of gastric cancer.

4,583 citations

Journal ArticleDOI
TL;DR: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments, and the voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline.
Abstract: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

4,475 citations