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.     

Empirical evaluation of data transformations and ranking statistics for microarray analysis

There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics outperform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.     

arrayQCplot: software for checking the quality of microarray data

SUMMARY: arrayQCplot is a software for the exploratory analysis of microarray data. This software focuses on quality control and generates newly developed plots for quality and reproducibility checks. It is developed using R and provides a user-friendly graphical interface for graphics and statistical analysis. Therefore, novice users will find arrayQCplot as an easy-to-use software for checking the quality of their data by a simple mouse click. AVAILABILITY: arrayQCplot software is available from Bioconductor at http://www.bioconductor.org. A more detailed manual is available at http://bibs.snu.ac.kr/software/arrayQCplot CONTACT: tspark@stats.snu.ac.kr.     

AVA: visual analysis of gene expression microarray data

SUMMARY: AVA (Array Visual Analyzer) is a Java program that provides a graphical environment for visualization and analysis of gene expression microarray data. Together with its interactive visualization tools and a variety of built-in data analysis and filtration methods, AVA effectively integrates microarray data normalization, quality assessment, and data mining into one application. AVAILABILITY: The software is freely available for academic users on request from the authors.     

Network Meta-Analysis Using R: A Review of Currently Available Automated Packages

Network meta-analysis (NMA) – a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously – has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA.     

Multiple sclerosis and pregnancy: what does the patient think? a questionnaire study

Background

The first objective of a DNA microarray experiment is typically to generate a list of genes or probes that are found to be differentially expressed or represented (in the case of comparative genomic hybridizations and/or copy number variation) between two conditions or strains. Rank Products analysis comprises a robust algorithm for deriving such lists from microarray experiments that comprise small numbers of replicates, for example, less than the number required for the commonly used t-test. Currently, users wishing to apply Rank Products analysis to their own microarray data sets have been restricted to the use of command line-based software which can limit its usage within the biological community.

Findings

Here we have developed a web interface to existing Rank Products analysis tools allowing users to quickly process their data in an intuitive and step-wise manner to obtain the respective Rank Product or Rank Sum, probability of false prediction and p-values in a downloadable file.

Conclusions

The online interactive Rank Products analysis tool RankProdIt, for analysis of any data set containing measurements for multiple replicated conditions, is available at: http://strep-microarray.sbs.surrey.ac.uk/RankProducts     

The effect of replicate number and image analysis method on sweetpotato [Ipomoea batatas (L.) Lam.] cDNA microarray results

Microarray analysis makes it possible to determine the relative expression of thousands of genes simultaneously. It has gained popularity at a rapid rate, but many caveats remain. In an effort to establish reliable microarray protocols for sweetpotato [Ipomoea batatas (L.) Lam.], we compared the effect of replication number and image analysis software with results obtained by quantitative rela-time PCR (Q-RT-PCR). Sweetpotato storage root development is the most economically important process in sweetpotato. In order to identify genes that may play a role in this process, RNA for microarray analysis was extracted from sweetpotato fibrous and storage roots. Four data sets, Spot4, Spot6, Finder4 and Finder6, were created using 4 or 6 replications, and the image analysis software of UCSF Spot or TIGR Spotfinder were used for spot detection and quantification. The ability of these methods to identify significant differential expression between treatments was investigated. The data sets with 6 replications were better at identifying genes with significant differential expression than the ones of 4 replications. Furthermore when using 6 replicates, UCSF Spot was superior to TIGR Spotfinder in identifying genes differentially expressed (18 out of 19) based on Q-RT-PCR. Our study shows the importance of proper replication number and image analysis for microarray studies.     

Contributions of the EMERALD project to assessing and improving microarray data quality

While minimum information about a microarray experiment (MIAME) standards have helped to increase the value of the microarray data deposited into public databases like ArrayExpress and Gene Expression Omnibus (GEO), limited means have been available to assess the quality of this data or to identify the procedures used to normalize and transform raw data. The EMERALD FP6 Coordination Action was designed to deliver approaches to assess and enhance the overall quality of microarray data and to disseminate these approaches to the microarray community through an extensive series of workshops, tutorials, and symposia. Tools were developed for assessing data quality and used to demonstrate how the removal of poor-quality data could improve the power of statistical analyses and facilitate analysis of multiple joint microarray data sets. These quality metrics tools have been disseminated through publications and through the software package arrayQualityMetrics. Within the framework provided by the Ontology of Biomedical Investigations, ontology was developed to describe data transformations, and software ontology was developed for gene expression analysis software. In addition, the consortium has advocated for the development and use of external reference standards in microarray hybridizations and created the Molecular Methods (MolMeth) database, which provides a central source for methods and protocols focusing on microarray-based technologies.