Genomic data integration is a key goal to be achieved towards large-scale genomic data analysis. This process is very challenging due to the diverse sources of information resulting from genomics experiments. In this work, we review methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments. It has been acknowledged that the main source of variation between different MAGE datasets is due to the so-called 'batch effects'. The methods reviewed here perform data integration by removing (or more precisely attempting to remove) the unwanted variation associated with batch effects. They are presented in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process. We provide a systematic description of the MAGE data integration methodology together with some basic recommendation to help the users in choosing the appropriate tools to integrate MAGE data for large-scale analysis; and also how to evaluate them from different perspectives in order to quantify their efficiency. All genomic data used in this study for illustration purposes were retrieved from InSilicoDB http://insilico.ulb.ac.be.

A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. We present them in a unified framework, using standardized notations in order to reveal their technical details and to highlight their common characteristics as well as their particularities.

BackgroundWith an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck.ResultsWe present the newly released R/Bioconductor package which, together with the earlier released R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the package a set of five visual and six quantitative validation measures are available as well.ConclusionsBy providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [https://insilicodb.org/app/].

Genomics datasets are increasingly useful for gaining biomedical insights, with adoption in the clinic underway. However, multiple hurdles related to data management stand in the way of their efficient large-scale utilization. The solution proposed is a web-based data storage hub. Having clear focus, flexibility and adaptability, InSilico DB seamlessly connects genomics dataset repositories to state-of-the-art and free GUI and command-line data analysis tools. The InSilico DB platform is a powerful collaborative environment, with advanced capabilities for biocuration, dataset sharing, and dataset subsetting and combination. InSilico DB is available from https://insilicodb.org.

Microarray technology has become an integral part of biomedical research and increasing amounts of datasets become available through public repositories. However, re-use of these datasets is severely hindered by unstructured, missing or incorrect biological samples information; as well as the wide variety of preprocessing methods in use. The inSilicoDb R/Bioconductor package is a command-line front-end to the InSilico DB, a web-based database currently containing 86 104 expert-curated human Affymetrix expression profiles compiled from 1937 GEO repository series. The use of this package builds on the Bioconductor project's focus on reproducibility by enabling a clear workflow in which not only analysis, but also the retrieval of verified data is supported.