JCVenterInstitute/NSForest
A machine learning method for the discovery of the minimum marker gene combinations for cell type identification from single-cell RNA sequencing
This tool helps researchers analyze single-cell RNA sequencing (scRNA-seq) data to pinpoint the most effective marker genes for identifying specific cell types. You input your scRNA-seq data with cell type labels, and it outputs a minimal set of 1-6 marker genes for each cell cluster, along with metrics to assess their classification accuracy. This is ideal for biologists and life scientists working with cellular genomics and transcriptomics.
Use this if you need to discover the most precise and minimal set of marker genes to accurately distinguish between cell types in your single-cell RNA sequencing experiments.
Not ideal if you are looking to analyze bulk RNA sequencing data or perform general gene expression analysis without a focus on cell type identification from single-cell data.
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HTML
License
MIT
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Last pushed
Mar 26, 2026
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