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21 de abr. de 2020 · In dviraran/SingleR: Reference-based single-cell RNA-seq annotation. Description Usage Arguments Value. View source: R/SingleR.R. Description. Given single-cell RNAseq data and reference dataset the function returns the best annotation for each single-cell.
SingleR reference data sets. We will now do our cell type identification using SingleR. SingleR comes with a collection of reference data sets. There are two for human: hpca, the Human Primary Cell Atlas (microarray-based), and blueprint\_encode, a combined Blueprint Epigenomics and Encode data set (RNASeq based) .
This process is analogous to current practice in single-cell data analysis; simply replace reads with cells, assemblies with clusters, and genes with cell types. A typical practitioner will hope that their clusters are reasonable proxies for the biological states of interest and that their manual annotation of the clusters is accurate.
8.5 Cell type annotation using SingleR. Given the markers that we’ve defined, we can mine the literature and identify each observed cell type (it’s probably the easiest for PBMC). However, we can try automaic annotation with SingleR is workflow-agnostic (can be used with Seurat, SCE, etc).
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DOI: 10.18129/B9.bioc.SingleR. Bioconductor version: 3.18. Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb], Jared ...
