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Each sample in the dataset is part of a library plate that was visually inspected for printing quality during the materials synthesis phase.
Thus, each sample in the dataset was tested once, using a model that was not fitted with that sample.
This is then repeated for each sample in the dataset.
We are grateful to Dr. Blalock who has kindly given us these values of the Braak stage for each sample in the dataset.
During LOOCV, each sample in the dataset was used as a test sample and predicted based on the model trained with the other samples.
MiRABELLE takes as input an expression dataset and produces a miR-seed activity matrix, giving for each sample in the dataset, the activity scores computed for each of the miRNA species for which predictions exist.
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D.R.H. and D.G. thank R. Miskewitz for assistance in assigning Köppen Geiger classes to the samples in the dataset.
Sample-by-read abundance matrices from each plate are merged based on the unique zOTU sequence, providing a sample-by-read abundance matrix for all samples in the dataset.
Notably, ICA-based decomposition methods (decomposition methods A and B) seem to be much more sensitive to the number of samples in the dataset compared with other methods (Fig. 4 and Supplementary Fig. 13).
The addition of more samples in the dataset in order to perform local calibrations would probably make the elaboration of a global NIR-model possible.
Matric potential is considered as an input parameter, which enables to increase the number of samples in the training dataset with a factor equal to the number of matric potentials used to determine the water retention curve of the soil samples in the dataset.
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