Expression Recovery in Single Cell RNA Sequencing

• Activity Number: 143 - Advancing Translational Research Using Novel Statistical Analyses for Complex and Omics Data
• Type: Invited
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Mo R Huang and Mingyao R Li and Nancy Ruonan Zhang*
• Companies: University of Pennsylvania and University of Pennsylvania and Wharton School , University of Pennsylvania
• Keywords: Missing data ; genomics ; single cell ; RNA sequencing ; imputation

Abstract

In single cell RNA sequencing experiments, not all transcripts present in the cell are captured in the library, and not all molecules present in the library are sequenced. The efficiency, that is, the proportion of transcripts in the cell that are eventually represented by reads, can vary between 2-60%, and can be especially low in highly parallelized technologies where the number of reads allocated for each cell can be very small. This leads to a severe case of not-at-random missing data, which hinders and confounds analysis, especially for low to moderately expressed genes. To address this issue, we introduce a noise reduction and missing-data imputation framework for single cell RNA sequencing, which allows for cell-specific efficiency parameters and borrows information across genes and cells to impute the zeros in the expression matrix as well as improve the expression estimates for genes with low read counts. We demonstrate the accuracy of the procedure through comparison to RNA-FISH experiments through the subsampling of high quality scRNA-seq data sets. We illustrate how expression recovery improves downstream analyses in single cell experiments.