Abstract
Performing joint analysis of gene expression datasets from different experiments can present challenges brought on by multiple factors-differences in equipment, protocols, climate etc. "Cross-study normalization" is a general term for transformations aimed at eliminating such effects, thus making datasets more comparable. However, joint analysis of datasets from different species is rarely done, and there are no dedicated normalization methods for such inter-species analysis. In order to test the usefulness of cross-studies normalization methods for inter-species analysis, we first applied three cross-study normalization methods, EB, DWD and XPN, to RNA sequencing datasets from different species. We then developed a new approach to evaluate the performance of cross-study normalization in eliminating experimental effects, while also maintaining the biologically significant differences between species and conditions. Our results indicate that all normalization methods performed relatively well in the cross-species setting. We found XPN to be better at reducing experimental differences, and found EB to be better at preserving biological differences. Still, according to our in-silico experiments, in all methods it is not possible to enforce the preservation of the biological differences in the normalization process. In addition to the study above, in this work we propose a new dedicated cross-studies and cross-species normalization method. Our aim is to address the shortcoming mentioned above: in the normalization process, we wish to reduce the experimental differences while preserving the biological differences. We term our method as CSN, and base it on the performance evaluation criteria mentioned above. Repeating the same experiments, the CSN method obtained a better and more balanced conservation of biological differences within the datasets compared to existing methods. To summarize, we demonstrate the usefulness of cross-study normalization methods in the inter-species settings, and suggest a dedicated cross-study cross-species normalization method that will hopefully open the way to the development of improved normalization methods for the inter-species settings.