Bayesian Inference for Single-cell Clustering and Imputing

  • Elham Azizi Memorial Sloan Kettering Cancer Center
  • Sandhya Prabhakaran Memorial Sloan Kettering Cancer Center
  • Ambrose Carr Memorial Sloan Kettering Cancer Center
  • Dana Pe'er Memorial Sloan Kettering Cancer Center

Abstract

Single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is noise-prone due to experimental errors and cell type-specific biases. Current computational approaches for analyzing single-cell data involve a global normalization step which introduces incorrect biases and spurious noise and does not resolve missing data (dropouts). This can lead to misleading conclusions in downstream analyses. Moreover, a single normalization removes important cell type-specific information. We propose a data-driven model, BISCUIT, that iteratively normalizes and clusters cells, thereby separating noise from interesting biological signals. BISCUIT is a Bayesian probabilistic model that learns cell-specific parameters to intelligently drive normalization. This approach displays superior performance to global normalization followed by clustering in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

Published
2017-01-26
How to Cite
AZIZI, Elham et al. Bayesian Inference for Single-cell Clustering and Imputing. Genomics and Computational Biology, [S.l.], v. 3, n. 1, p. e46, jan. 2017. ISSN 2365-7154. Available at: <https://genomicscomputbiol.org/ojs3/GCB/article/view/22>. Date accessed: 28 jan. 2022. doi: https://doi.org/10.18547/gcb.2017.vol3.iss1.e46.
Section
Short Communications