EcoTyper: a machine learning framework for large-scale identification of cell type-specific transcriptional states and cellular ecosystemsBogdan-Alexandru Luca, Chloé Steen, Armon Azizi, Andrew Gentles, Arash Ash Alizadeh & Aaron Newman
EcoTyper is a machine learning framework for large-scale identification of cell type-specific transcriptional states and their co-association patterns from bulk, single-cell, and spatially resolved gene expression data. EcoTyper performs the following major steps: 1) In silico purification: This step enables imputation of cell type-specific gene expression profiles from bulk tissue transcriptomes, using CIBERSORTx (Newman et al., Nature Biotechnology 2019). 2) Cell state discovery: This step enables identification and quantitation of cell type-specific transcriptional states. 3)...
A supplementary resource for “T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma”.