Disentangling neural cell diversity using single-cell transcriptomics

By J. Poulin, Bosiljka Tasic, Jens Hjerling-Leffler, J. Trimarchi, R. Awatramani. 2016

288 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Ziv Bar-Joseph
2. Roser Vento-Tormo
3. Hannah Kim
4. Sarah A. Teichmann
5. Valentine Svensson
6. Siddhartha Jain
7. Chieh Hua Lin

In Molecular Biology

1. Valentine Svensson
2. Roser Vento-Tormo
3. Ziv Bar-Joseph
4. Hannah Kim
5. Sarah A. Teichmann
6. Siddhartha Jain
7. Chieh Hua Lin
8. Rahul Satija

Citations in the corpus

• Exponential scaling of single-cell RNA-seq in the past decade (2017, background): “Cells have traditionally been classified by their morphology or by expression of certain proteins in functionally distinct settings (Mosmann et al., 1986; Orkin, 2000; Poulin et al., 2016).”
• Using neural networks for reducing the dimensions of single-cell RNA-Seq data (2017, background): “An important and exciting application of single cell sequencing is the ability to identify and characterize new cell types and cell states (4,5).”