Inferring single cell expression profiles from overlapped pooling sequencing data with compressed sensing strategy.
IF: 19.160
Cited by: 1


Though single cell RNA sequencing (scRNA-seq) technologies have been well developed, the acquisition of large-scale single cell expression data may still lead to high costs. Single cell expression profile has its inherent sparse properties, which makes it compressible, thus providing opportunities for solutions. Here, by computational simulation as well as experiment of 54 single cells, we propose that expression profiles can be compressed from the dimension of samples by overlapped assigning each cell into plenty of pools. And we prove that expression profiles can be inferred from these pool expression data with overlapped pooling design and compressed sensing strategy. We also show that by combining this approach with plate-based scRNA-seq measurement, it can maintain its superiorities in gene detection sensitivity and individual identity and recover the expression profile with high precision, while saving about half of the library cost. This method can inspire novel conceptions on the measurement, storage or computation improvements for other compressible signals in many biological areas.


Gene Expression
Spatial Transcriptomics

MeSH terms

Computer Simulation
Databases, Genetic
Gene Expression Profiling
Gene Library
Models, Theoretical
Reproducibility of Results
Sequence Analysis, RNA
Single-Cell Analysis


Huang, Mengting
Yang, Yixuan
Wen, Xingzhao
Xu, Weiqiang
Lu, Na
Sun, Xiao
Tu, Jing
Lu, Zuhong

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