Plate-based single cell RNA-Seq (scRNA-seq) methods can detect a comprehensive profile for gene expression but suffers from high library cost of each single cell. Although cost can be reduced significantly by massively parallel scRNA-seq techniques, these approaches lose sensitivity for gene detection. Inspired by group testing and compressed sensing, here, we designed a computational framework to close the gap between sensitivity and library cost. In our framework, single cells were overlapped assigned into plenty of pools. Expression profile of each pool was then obtained by using plate-based sequence approach. The expression profile of all single cells was recovered based on the pool expression and the overlapped pooling design. The inferred expression profile showed highly consistency with the original data in both accuracy and cell types identification. A parallel computing scheme was designed to boost speed when processing the enormous single cells, and elastic net regression was combined with compressed sensing to auto-adapt for both sparsely and densely expressed genes.

Efficient inference of single cell expression profiles with overlapping pooling and compressed sensing