Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge

Motivation: Single cell RNA-seq (scRNA-seq) data contains a wealth of information which has to
be inferred computationally from the observed sequencing reads. As the ability to sequence more
cells improves rapidly, existing computational tools suffer from three problems. (i) The decreased
reads-per-cell implies a highly sparse sample of the true cellular transcriptome. (ii) Many tools simply cannot handle the size of the resulting datasets. (iii) Prior biological knowledge such as bulk
RNA-seq information of certain cell types or qualitative marker information is not taken into account. Here we present UNCURL, a preprocessing framework based on non-negative matrix factorization for scRNA-seq data, that is able to handle varying sampling distributions, scales to very
large cell numbers and can incorporate prior knowledge.
Results: We find that preprocessing using UNCURL consistently improves performance of commonly used scRNA-seq tools for clustering, visualization and lineage estimation, both in the absence and presence of prior knowledge. Finally we demonstrate that UNCURL is extremely scalable
and parallelizable, and runs faster than other methods on a scRNA-seq dataset containing 1.3 million cells.
Availability and implementation: Source code is available at https://github.com/yjzhang/uncurl_
python.