Matthew E. Ritchie

limma powers differential expression analyses for RNA-sequencing and microarray studies

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.

A comparison of background correction methods for two-colour microarrays

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. SUPPLEMENTARY INFORMATION Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.

A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data

Illumina BeadArrays are among the most popular and reliable platforms for gene expression profiling. However, little external scrutiny has been given to the design, selection and annotation of BeadArray probes, which is a fundamental issue in data quality and interpretation. Here we present a pipeline for the complete genomic and transcriptomic re-annotation of Illumina probe sequences, also applicable to other platforms, with its output available through a Web interface and incorporated into Bioconductor packages. We have identified several problems with the design of individual probes and we show the benefits of probe re-annotation on the analysis of BeadArray gene expression data sets. We discuss the importance of aspects such as probe coverage of individual transcripts, alternative messenger RNA splicing, single-nucleotide polymorphisms, repeat sequences, RNA degradation biases and probes targeting genomic regions with no known transcription. We conclude that many of the Illumina probes have unreliable original annotation and that our re-annotation allows analyses to focus on the good quality probes, which form the majority, and also to expand the scope of biological information that can be extracted.

Targeting BCL-2 with the BH3 Mimetic ABT-199 in Estrogen Receptor-Positive Breast Cancer

The prosurvival protein BCL-2 is frequently overexpressed in estrogen receptor (ER)-positive breast cancer. We have generated ER-positive primary breast tumor xenografts that recapitulate the primary tumors and demonstrate that the BH3 mimetic ABT-737 markedly improves tumor response to the antiestrogen tamoxifen. Despite abundant BCL-XL expression, similar efficacy was observed with the BCL-2 selective inhibitor ABT-199, revealing that BCL-2 is a crucial target. Unexpectedly, BH3 mimetics were found to counteract the side effect of tamoxifen-induced endometrial hyperplasia. Moreover, BH3 mimetics synergized with phosphatidylinositol 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) inhibitors in eliciting apoptosis. Importantly, these two classes of inhibitor further enhanced tumor response in combination therapy with tamoxifen. Collectively, our findings provide a rationale for the clinical evaluation of BH3 mimetics in therapy for breast cancer.

Sensitization of BCL-2–expressing breast tumors to chemotherapy by the BH3 mimetic ABT-737

Overexpression of the prosurvival protein BCL-2 is common in breast cancer. Here we have explored its role as a potential therapeutic target in this disease. BCL-2, its anti-apoptotic relatives MCL-1 and BCL-XL, and the proapoptotic BH3-only ligand BIM were found to be coexpressed at relatively high levels in a substantial proportion of heterogeneous breast tumors, including clinically aggressive basal-like cancers. To determine whether the BH3 mimetic ABT-737 that neutralizes BCL-2, BCL-XL, and BCL-W had potential efficacy in targeting BCL-2–expressing basal-like triple-negative tumors, we generated a panel of primary breast tumor xenografts in immunocompromised mice and treated recipients with either ABT-737, docetaxel, or a combination. Tumor response and overall survival were significantly improved by combination therapy, but only for tumor xenografts that expressed elevated levels of BCL-2. Treatment with ABT-737 alone was ineffective, suggesting that ABT-737 sensitizes the tumor cells to docetaxel. Combination therapy was accompanied by a marked increase in apoptosis and dissociation of BIM from BCL-2. Notably, BH3 mimetics also appeared effective in BCL-2–expressing xenograft lines that harbored p53 mutations. Our findings provide in vivo evidence that BH3 mimetics can be used to sensitize primary breast tumors to chemotherapy and further suggest that elevated BCL-2 expression constitutes a predictive response marker in breast cancer.

Microarray background correction: maximum likelihood estimation for the normal–exponential convolution

Background correction is an important preprocessing step for microarray data that attempts to adjust the data for the ambient intensity surrounding each feature. The “normexp” method models the observed pixel intensities as the sum of 2 random variables, one normally distributed and the other exponentially distributed, representing background noise and signal, respectively. Using a saddle-point approximation, Ritchie and others (2007) found normexp to be the best background correction method for 2-color microarray data. This article develops the normexp method further by improving the estimation of the parameters. A complete mathematical development is given of the normexp model and the associated saddle-point approximation. Some subtle numerical programming issues are solved which caused the original normexp method to fail occasionally when applied to unusual data sets. A practical and reliable algorithm is developed for exact maximum likelihood estimation (MLE) using high-quality optimization software and using the saddle-point estimates as starting values. “MLE” is shown to outperform heuristic estimators proposed by other authors, both in terms of estimation accuracy and in terms of performance on real data. The saddle-point approximation is an adequate replacement in most practical situations. The performance of normexp for assessing differential expression is improved by adding a small offset to the corrected intensities.

Opposing roles of polycomb repressive complexes in hematopoietic stem and progenitor cells.

Polycomb group (PcG) proteins are transcriptional repressors with a central role in the establishment and maintenance of gene expression patterns during development. We have investigated the role of polycomb repressive complexes (PRCs) in hematopoietic stem cells (HSCs) and progenitor populations. We show that mice with loss of function mutations in PRC2 components display enhanced HSC/progenitor population activity, whereas mutations that disrupt PRC1 or pleiohomeotic repressive complex are associated with HSC/progenitor cell defects. Because the hierarchical model of PRC action would predict synergistic effects of PRC1 and PRC2 mutation, these opposing effects suggest this model does not hold true in HSC/progenitor cells. To investigate the molecular targets of each complex in HSC/progenitor cells, we measured genome-wide expression changes associated with PRC deficiency, and identified transcriptional networks that are differentially regulated by PRC1 and PRC2. These studies provide new insights into the mechanistic interplay between distinct PRCs and have important implications for approaching PcG proteins as therapeutic targets.

Swift: primary data analysis for the Illumina Solexa sequencing platform

Motivation: Primary data analysis methods are of critical importance in second generation DNA sequencing. Improved methods have the potential to increase yield and reduce the error rates. Openly documented analysis tools enable the user to understand the primary data, this is important for the optimization and validity of their scientific work. Results: In this article, we describe Swift, a new tool for performing primary data analysis on the Illumina Solexa Sequencing Platform. Swift is the first tool, outside of the vendors own software, which completes the full analysis process, from raw images through to base calls. As such it provides an alternative to, and independent validation of, the vendor supplied tool. Our results show that Swift is able to increase yield by 13.8%, at comparable error rate. Availability and Implementation: Swift is implemented in C++and supported under Linux. It is supplied under an open source license (LGPL3), allowing researchers to build upon the platform. Swift is available from http://swiftng.sourceforge.net. Contact: new@sgenomics.org; nava.whiteford@nanoporetech.com Supplementary information: Supplementary data are available at Bioinformatics online.

Statistical issues in the analysis of Illumina data

BackgroundIllumina bead-based arrays are becoming increasingly popular due to their high degree of replication and reported high data quality. However, little attention has been paid to the pre-processing of Illumina data. In this paper, we present our experience of analysing the raw data from an Illumina spike-in experiment and offer guidelines for those wishing to analyse expression data or develop new methodologies for this technology.ResultsWe find that the local background estimated by Illumina is consistently low, and subtracting this background is beneficial for detecting differential expression (DE). Illuminas summary method performs well at removing outliers, producing estimates which are less biased and are less variable than other robust summary methods. However, quality assessment on summarised data may miss spatial artefacts present in the raw data. Also, we find that the background normalisation method used in Illuminas proprietary software (BeadStudio) can cause problems with a standard DE analysis. We demonstrate that variances calculated from the raw data can be used as inverse weights in the DE analysis to improve power. Finally, variability in both expression levels and DE statistics can be attributed to differences in probe composition. These differences are not accounted for by current analysis methods and require further investigation.ConclusionAnalysing Illumina expression data using BeadStudio is reasonable because of the conservative estimates of summary values produced by the software. Improvements can however be made by not using background normalisation. Access to the raw data allows for a more detailed quality assessment and flexible analyses. In the case of a gene expression study, data can be analysed on an appropriate scale using established tools. Similar improvements can be expected for other Illumina assays.

Integrative analysis of RUNX1 downstream pathways and target genes

BackgroundThe RUNX1 transcription factor gene is frequently mutated in sporadic myeloid and lymphoid leukemia through translocation, point mutation or amplification. It is also responsible for a familial platelet disorder with predisposition to acute myeloid leukemia (FPD-AML). The disruption of the largely unknown biological pathways controlled by RUNX1 is likely to be responsible for the development of leukemia. We have used multiple microarray platforms and bioinformatic techniques to help identify these biological pathways to aid in the understanding of why RUNX1 mutations lead to leukemia.ResultsHere we report genes regulated either directly or indirectly by RUNX1 based on the study of gene expression profiles generated from 3 different human and mouse platforms. The platforms used were global gene expression profiling of: 1) cell lines with RUNX1 mutations from FPD-AML patients, 2) over-expression of RUNX1 and CBFβ, and 3) Runx1 knockout mouse embryos using either cDNA or Affymetrix microarrays. We observe that our datasets (lists of differentially expressed genes) significantly correlate with published microarray data from sporadic AML patients with mutations in either RUNX1 or its cofactor, CBFβ. A number of biological processes were identified among the differentially expressed genes and functional assays suggest that heterozygous RUNX1 point mutations in patients with FPD-AML impair cell proliferation, microtubule dynamics and possibly genetic stability. In addition, analysis of the regulatory regions of the differentially expressed genes has for the first time systematically identified numerous potential novel RUNX1 target genes.ConclusionThis work is the first large-scale study attempting to identify the genetic networks regulated by RUNX1, a master regulator in the development of the hematopoietic system and leukemia. The biological pathways and target genes controlled by RUNX1 will have considerable importance in disease progression in both familial and sporadic leukemia as well as therapeutic implications.