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Cell. 2016 Mar 24;165(1):220-233. doi: 10.1016/j.cell.2016.01.026. Epub 2016 Mar 3.

Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons.

Author information

1
Department of Neuroscience, Columbia University, New York, NY 10032, USA; Department of Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Kavli Institute for Brain Science, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10032, USA. Electronic address: mig2118@columbia.edu.
2
Department of Statistics and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA.
3
Department of Neuroscience, Columbia University, New York, NY 10032, USA; Department of Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Kavli Institute for Brain Science, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10032, USA.
4
Department of Neuroscience, Columbia University, New York, NY 10032, USA; Department of Physiology and Cellular Biophysics, Columbia University, New York, NY 10032, USA.
5
Department of Neuroscience, Columbia University, New York, NY 10032, USA; Department of Statistics and Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA. Electronic address: liam@stat.columbia.edu.

Abstract

Documenting the extent of cellular diversity is a critical step in defining the functional organization of tissues and organs. To infer cell-type diversity from partial or incomplete transcription factor expression data, we devised a sparse Bayesian framework that is able to handle estimation uncertainty and can incorporate diverse cellular characteristics to optimize experimental design. Focusing on spinal V1 inhibitory interneurons, for which the spatial expression of 19 transcription factors has been mapped, we infer the existence of ~50 candidate V1 neuronal types, many of which localize in compact spatial domains in the ventral spinal cord. We have validated the existence of inferred cell types by direct experimental measurement, establishing this Bayesian framework as an effective platform for cell-type characterization in the nervous system and elsewhere.

PMID:
26949187
PMCID:
PMC4831714
DOI:
10.1016/j.cell.2016.01.026
[Indexed for MEDLINE]
Free PMC Article

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