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Comment
. 2016 Sep;13(5):557-9.
doi: 10.1038/cmi.2016.22. Epub 2016 May 2.

Transcriptomic signatures decode Th17 cell pathogenicity

P A Reyfman  1 , E T Bartom  2 , B D Singer  1
Affiliations
Comment

Transcriptomic signatures decode Th17 cell pathogenicity

P A Reyfman et al. Cell Mol Immunol. 2016 Sep.
No abstract available

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Figures

Figure 1
Figure 1
Principal component analysis (PCA) can be used to represent in two dimensions the most important distinguishing features of a large genomic data set such as a single-cell RNA-seq experiment (a). In their paper, Gaublomme and colleagues used signatures based on published immune cell expression data in order to annotate the PCA space of their in vivo-differentiated Th17 cells, and used covariation with a Th17 cell pathogenicity signature to identify new potential regulators of pathogenicity in Th17 cells (b).
See this image and copyright information in PMC

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References

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