Targeted integrating hyperspectral and metabolomic data with spectral indices and metabolite content models for efficient salt-tolerant phenotype discrimination in

Background: Plant phenomics has made significant progress recently, with new demand to move from external characterization to internal exploration through data combination. Hyperspectral and metabolomic data, with cause-and-effect relationship, are given priority for integration. However, few efficient integrating methods are available. Results: Here, we showed the way to explore hyperspectral data through combining with upper-level metabolomic data and perform higher-level-data-guided dimension reduction in target-trait-oriented manner to obtain high analysis efficiency. To verify its feasibility, two-stage pipeline combining hyperspectral and metabolic data was designed to discriminate salt-tolerant phenotype for Conclusions: Mutant-screening pipeline provided here is a practical example for targeted data integration and data mining under the guide of upper-layer omic data. Targeted combination of phenomic and metabolomic data provides the ability for accurate phenotype discrimination and prediction from both external and internal aspects, providing a powerful tool for phenotype selection in new-generation crop breeding.