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Deep-learning-based cell composition analysis from tissue expression profiles
[article]
2019
bioRxiv
pre-print
We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single cell RNA-seq data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness, across tissues and species. A single trained
doi:10.1101/659227
fatcat:wsl5l5wpujh7dcimeuuekgemlm