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A high throughput method for quantifying number and size distribution of Arabidopsis seeds using large particle flow cytometry.

Alejandro MoralesJ TeapalJ M H AmmerlaanX YinJ B EversN P R AntenRashmi SasidharanMartijn van Zanten
Published in: Plant methods (2020)
The tests performed reveal that there is no single classification algorithm that performs best for all samples, so the recommended strategy is to train and use multiple algorithms and use the median predictions of seed size and number across all algorithms. To facilitate the use of this method, an R package (SeedSorter) that implements the methodology has been developed and made freely available. The method was validated with seed samples from several natural accessions of Arabidopsis thaliana, but our analysis pipeline is applicable to any species with seed sizes smaller than 1.5 mm.
Keyphrases
  • machine learning
  • deep learning
  • flow cytometry
  • arabidopsis thaliana
  • high throughput
  • single cell
  • transcription factor
  • gene expression
  • high speed
  • genetic diversity