celaref

Function

Single cell RNA sequencing (scRNAseq) has made it possible to examine the cellular heterogeny within a tissue or sample, and observe changes and characteristics in specific cell types. To do this, we need to group the cells into clusters and figure out what they are.

The celaref (cell labelling by reference) package aims to streamline the cell-type identification step, by suggesting cluster labels on the basis of similarity to an already-characterised reference dataset - wheather that's from a similar experiment performed previously in the same lab, or from a public dataset from a similar sample.

Input

To look for cluster similarities celaref needs:

• The query dataset :

  • a table of read counts per cell per gene
  • a list of which cells belong in which cluster

• A reference dataset:

  • a table of read counts per cell per gene
  • a list of which cells belong in which annotated cluster

Output

Query Group	Short Label	pval
cluster_1	cluster_1:astrocytes_ependymal	2.98e-23
cluster_2	cluster_2:endothelial-mural	8.44e-10
cluster_3	cluster_3:no_similarity	NA
cluster_4	cluster_4:microglia	2.71e-19
cluster_5	cluster_5:pyramidal SS|interneurons	3.49e-10
cluster_6	cluster_6:oligodendrocytes	2.15e-28

This is a comparison of brain scRNAseq data from :

• Zeisel, A., Manchado, A. B. M., Codeluppi, S., Lonnerberg, P., La Manno, G., Jureus, A., … Linnarsson, S. (2015). Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347(6226), 1138–42. http://doi.org/10.1126/science.aaa1934
• Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., … Quake, S. R. (2015). A survey of human brain transcriptome diversity at the single cell level. Proceedings of the National Academy of Sciences, 112(23), 201507125. http://doi.org/10.1073/pnas.1507125112

More information?

Full details in the vignette html - method description, manual and example analyses.