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Summary: DECONVersation is a tool designed for the deconvolution of bulk RNA-seq data using embeddings derived from large-scale, LLM-based foundation models. DECONVersation produces robust cell type proportions by leveraging these high-dimensional embeddings to mitigate batch effects typically present in single-cell reference signature matrices.
Latest version: 0.0.1
Required dependencies: anndata | datasets | matplotlib | numpy | pandas | peft | scanpy | scikit-learn | scipy | seaborn | torch | tqdm | transformers

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