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/].

The rodent hippocampus is known to exhibit two very distinctive patterns of activity: theta with place selective cells firing during exploratory behavior and sharp waves (SPWs) associated with collective discharges in the CA3 during slow wave sleep (SWS), inactivity while awake and consummatory behavior. A great deal of evidence has demonstrated that the cells activated during SPWs events are representative of previous behavioral activity, which suggests an important functional role of off-line learning and consolidation for these SPWs events. Supporting this view, forward, and more recently, reverse replay of linear track behavioral sequences have been reported in rodent's hippocampal place cells during SPWs. We demonstrate here that these patterns of reactivation can be successfully reproduced by relying on a computational model of the hippocampus with theta phase precession and synaptic plasticity during theta rhythm. Two mechanisms are proposed to initiate SPWs events: random reactivation in the presence of rapid, irregular subthreshold inputs and place selective cell activations. In 2D navigation computational experiments, rather than observing the perfect replay of experienced pathways, new pathways "experienced during immobility" emerge. This suggests a neural mechanism for shortcut navigation.

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.

Oscillatory patterns of activity in various frequency ranges are ubiquitously expressed in cortical circuits. While recent studies in humans emphasized rhythmic modulations of neuronal oscillations ("second-order" rhythms), their potential involvement in information coding remains an open question. Here, we show that a rhythmic (~0.7 Hz) modulation of hippocampal theta power, unraveled by second-order spectral analysis, supports encoding of spatial and behavioral information. The phase preference of neuronal discharge within this slow rhythm significantly increases the amount of information carried by action potentials in various motor/cognitive behaviors by (1) distinguishing between the spikes fired within versus outside the place field of hippocampal place cells, (2) disambiguating place firing of neurons having multiple place fields, and (3) predicting between alternative future spatial trajectories. This finding demonstrates the relevance of second-order spectral components of brain rhythms for decoding neuronal information.

It was recently shown that perisomatic GABAergic inhibitory postsynaptic potentials (IPSPs) originating from basket and chandelier cells can be recorded as population IPSPs from the hippocampal pyramidal layer using extracellular electrodes (eIPSPs). Taking advantage of this approach, we have investigated the recruitment of perisomatic inhibition during spontaneous hippocampal activity in vitro. Combining intracellular and extracellular recordings from pyramidal cells and interneurons, we confirm that inhibitory signals generated by basket cells can be recorded extracellularly, but our results suggest that, during spontaneous activity, eIPSPs are mostly confined to the CA3 rather than CA1 region. CA3 eIPSPs produced the powerful time-locked inhibition of multi-unit activity expected from perisomatic inhibition. Analysis of the temporal dynamics of spike discharges relative to eIPSPs suggests significant but moderate recruitment of excitatory and inhibitory neurons within the CA3 network on a 10 ms time scale, within which neurons recruit each other through recurrent collaterals and trigger powerful feedback inhibition. Such quantified parameters of neuronal interactions in the hippocampal network may serve as a basis for future characterisation of pathological conditions potentially affecting the interactions between excitation and inhibition in this circuit.

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.