Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons

Mariano I. Gabitto, Ari Pakman, Jay B. Bikoff, L. F. Abbott, Thomas M. Jessell, Liam Paninski

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

Summary 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.

Original languageEnglish
Pages (from-to)220-233
Number of pages14
JournalCell
Volume165
Issue number1
DOIs
StatePublished - 24 Mar 2016
Externally publishedYes

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology

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