Robust classification using average correlations as features (ACF).

Journal: BMC bioinformatics
Published Date: Mar 20, 2023

Abstract

MOTIVATION: In single-cell transcriptomics and other omics technologies, large fractions of missing values commonly occur. Researchers often either consider only those features that were measured for each instance of their dataset, thereby accepting severe loss of information, or use imputation which can lead to erroneous results. Pairwise metrics allow for imputation-free classification with minimal loss of data.

Authors

  • Yannis Schumann
    Chair for High Performance Computing, Helmut-Schmidt University, Hamburg, Germany. schumany@hsu-hh.de.
  • Julia E Neumann
    Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Philipp Neumann
    Chair for High Performance Computing, Helmut-Schmidt University, Hamburg, Germany.

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