Celaref: Annotating single-cell RNAseq clusters by similarity to reference datasets скачать в хорошем качестве

Celaref: Annotating single-cell RNAseq clusters by similarity to reference datasets

Single-cell RNA sequencing (scRNAseq) is a way of measuring gene expression of many individual cells simultaneously, and is often used on samples which contain a mix of different cell types. In an scRNAseq analysis individual cells are typically clustered to group them by cell type. After clustering, identifying what type of cell is in each cluster (e.g. neurons) usually needs domain-specific knowledge of marker genes and function. The celaref package accepts pre-computed cell-clusters and aims to suggest cell-types for each cluster via similarity to reference datasets (scRNAseq experiments or microarrays) from similar samples. Briefly, within-dataset differential expression is calculated to identify the most enriched genes for each cluster, then their rankings are examined in reference datasets. Kolmogorov–Smirnov tests are used to decide if multiple matches should be reported. Initial experiments on brain, lacrimal gland and blood PBMC samples show sensible matching between similar cell types without overreaching on dissimilar cells. Celaref will be submitted to Bioconductor and is available at https://github.com/MonashBioinformati...