A downsampling method enables robust clustering and integration of single-cell transcriptome data

The random noises, sampling biases, and batch effects often confound true biological variations in single-cell RNA-sequencing (scRNA-seq) data. Adjusting such biases is key to the robust discoveries in downstream analyses, such as cell clustering, gene selection and data integration. Here we propose a model-based downsampling algorithm based on minimal unbiased representative points (MURP^XMBD). MURP^XMBD is designed to retrieve a set of representative points by reducing gene-wise random independent errors, while retaining the covariance structure of biological origin hence provide an unbiased representation of the cell population. Subsequent validation using benchmark datasets shows that MURP^XMBD can improve the quality and accuracy of clustering algorithms, and thus facilitate the discovery of new cell types. Besides, MURP^XMBD also improves the performance of dataset integration algorithms. In summary, MURP^XMBD serves as a useful noise-reduction method for single-cell sequencing analysis in biomedical studies.