Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons

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


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Animals
  • Bayes Theorem*
  • Mice
  • Renshaw Cells / chemistry*
  • Renshaw Cells / classification
  • Renshaw Cells / cytology*
  • Spinal Cord / cytology*
  • Transcription Factors / analysis*
  • Transcriptome


  • Transcription Factors