Journal
CELL
Volume 165, Issue 1, Pages 220-233Publisher
CELL PRESS
DOI: 10.1016/j.cell.2016.01.026
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Funding
- National Science Foundation through a GRFP fellowship
- NIH [NS033245, MH093338]
- Harold and Leila Y. Mathers Foundation
- Brain Research Foundation
- Project A.L.S.
- Gatsby Foundation
- Swartz Foundation
- Mathers Foundation
- ONR [N00014-14-1-0243]
- ARO MURI grant [W911NF-12-1-0594]
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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 similar to 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.
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