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.
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
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A reference dataset:
- a table of read counts per cell per gene
- a list of which cells belong in which annotated cluster
| 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
Full details in the vignette html - method description, manual and example analyses.