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kimberle9 / rfca

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R package: {rfca} Random forest-based cell annotation methods for scRNAseq analysis. {rfca} contains methods which identifies cell types using machine learning trained on a diversity of cell types, without the need for a labelled training dataset. It also allows you to train your own cell prediction models with your own labels (cell type, subtype, cell state, cluster number etc). This package is best suited for researchers who want to annotate their datasets in a quick and unbiased way, phenotype their datasets based on cell identity proportions, and discover common cell states across different datasets and disease models.

machine-learning random-forest scrna-seq 10x 10x-genomics seurat scrna-seq-analysis scrnaseq-datasets
  • Updated Oct 7, 2020
  • R

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