MURP – a downsampling method that 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. Researchers at Xiamen University have developed 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.

A schematic representation of MURP

(a) MURP is applied to preprocessed scRNA-seq data before downstream analyses of different purposes; (b) MURP is defined as an optimal set of representative points of the original cell population by iterative minimization of pseudo-BIC.

Availability – R implementations of MURP are available on GitHub (https://github.com/renjun0324/MURP) for academic use.