Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data.

Journal: BMC bioinformatics
Published Date: Jul 8, 2019

Abstract

BACKGROUND: Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural networks, namely autoencoders, has been useful for denoising of single cell data, imputation of missing values and dimensionality reduction.

Authors

  • Savvas Kinalis
    Centre for Genomic Medicine Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
  • Finn Cilius Nielsen
    Centre for Genomic Medicine Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
  • Ole Winther
    The Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200 Copenhagen N, Denmark.
  • Frederik Otzen Bagger
    Centre for Genomic Medicine Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. frederik@binf.ku.dk.

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