Data Denoising with transfer learning in single-cell transcriptomics

BioRxiv : the Preprint Server for Biology
Jingshu WangHugh MacMullan

Abstract

Single-cell RNA sequencing (scRNA-seq) data is noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions, and divergent species to denoise target new datasets.

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JM 2820
Sequence Determinations, RNA
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