Evaluation and comparison of GeoChip data pre-processing methods: LnMR and RAln

Abstract:

[Objective] To evaluate and compare two GeoChip data pre-processing methods, LnMR and RAln. [Methods] The rank-abundance curve, evenness indice, one-way ANOVA, Q-Q plot, α diversity indice and response ratio were used to evaluate the pre-processing methods of GeoChip data from two recently published studies, a summer grazing experiment in the Tibetan grassland and a field study on the mutual effects of soil transplant and maize cropping. [Results] Both methods are efficient in removing or diminishing extreme values, optimizing data distribution, reducing random errors, improving data normalization and manifesting experimental differences, which makes GeoChip data more suitable for further statistical analysis. In particular, LnMR is more suitable for detecting subtle differences of microbial community compositions among different treatments, whereas RAln is more efficient in removing systematic errors. [Conclusion] LnMR and RAln are two powerful GeoChip data pre-processing methods, and should be applied with caution.