RT Journal Article
SR Electronic
T1 CuBlock: A cross-platform normalization method for gene-expression microarrays
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.29.360198
DO 10.1101/2020.10.29.360198
A1 Valentin Junet
A1 Judith Farrés
A1 José M. Mas
A1 Xavier Daura
YR 2020
UL http://biorxiv.org/content/early/2020/10/29/2020.10.29.360198.abstract
AB Motivation Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.Results We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct data sets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these data sets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study.Availability CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublockContact xouse{at}anaxomics.com, xavier.daura{at}uab.catSupplementary information Supplementary data are available at bioRxiv online.Competing Interest StatementThe authors have declared no competing interest.