Batch effect removal methods for microarray gene expression data integration: a survey

Abstract

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.

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis

“…As the sample quality is affected by the day of processing and differences in the staff and laboratories, so-called batch effects are produced (Leek et al, 2010;De Livera et al, 2012;Lazar et al, 2013). Because we included more than 650 samples in our study, we could demonstrate that removing such batch effects from our data set by appropriate experimental designs and by quality control samples allows comparison between mutants (Fig.…”

“…The COMBAT algorithm (Johnson et al, 2007) may not be the best choice for adjusting batch effects in metabolomics data sets (Chen et al, 2011). A number of batch adjustment methods are available (De Livera et al, 2012;Lazar et al, 2013), and we are in the process of comparing these methods for their enhancement of the interpretation of metabolomics data sets.…”

“…Using this design, we carefully normalized the data to reduce the so-called batch effect (i.e. unwanted nonbiological variations; Leek et al, 2010;Lazar et al, 2013). Before batch normalization, we observed Figure 1.…”

Quality Control of Expression Profiling Data

“…A common shortcoming of many previous attempts is that tissue specificity of the genes was reported [102][103][104][105][106][107][108][109][110][111][112], or avoided [113][114][115]. However, no attempts were made to actually use such data for quality control or evaluation of the expression data, or if they were, it was for cancer analysis within one tissue [116][117][118] or to study the expression of synonymous codons in plants [119].…”

Study of Meta-analysis Strategies for Network Inference Using Information-Theoretic Approaches

“…COMBAT, which was shown to outperform other commonly used batch removal methods in some specific scenarios [26], uses estimations for the LS (location-scale) parameters (e.x. mean and variance) for each gene independently [27]. The gene, afterwards, is adjusted to meet the estimated model.…”