GitHub topics: scrnaseq-datasets
OlivierRaineteauSBRI/scRNASeq
Repo linked to our recent publication in Science Advances
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kimberle9/rfca
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.
Language: R - Size: 155 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 1
SindiLab/ACTIVA
The main repository for ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders
Language: Python - Size: 769 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 7 - Forks: 5
