Imputing single-cell RNA-seq data by considering cell heterogeneity and prior expression of dropouts

• 作 者: Lihua Zhang Shihua Zhang
• 机 构: NCMIS,CEMS,RCSDS,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 1001
• 出 处: 分子细胞生物学报（英文版）
• 发表日期: 2021年1期
• 关键词: single-cell RNA-seq dropout imputation low-rank systems biology

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to determine expression patterns of thousands of individual cells.However,the analysis of scRNA-seq data remains a computational challenge due to the high technical noise such as the presence of dropout events that lead to a large proportion of zeros for expressed genes.Taking into account the cell heterogene-ity and the relationship between dropout rate and expected expression level,we present a cell sub-population based bounded low-rank (PBLR) method to impute the dropouts of scRNA-seq data.Through application to both simulated and real scRNA-seq datasets,PBLR is shown to be effective in recovering dropout events,and it can dramatically improve the low-dimensional repre-sentation and the recovery of gene-gene relationships masked by dropout events compared to several state-of-the-art methods.Moreover,PBLR also detects accurate and robust cell sub-populations automatically,shedding light on its flexibility and general-ity for scRNA-seq data analysis.