Consequences and opportunities arising due to sparser single-cell RNA-seq datasets
G.A. Bouland (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
AMETA Mahfouz (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
Marcel J.T. Reinders (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
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Abstract
With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to ~ 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.
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