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Activity Number: 453 - Advances on the Analysis of Single-Cell Sequencing Data
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #303065 Presentation
Title: Accurate Correction on Dropout Events in Single-Cell RNAseq Data
Author(s): Lingling An* and Di Ran and Shanshan Zhang and Nick Lytal
Companies: University of Arizona and University of Arizona and University of Arizona and University of Arizona
Keywords: single-cell; RNA_seq data; dropout; imputation

Single-cell RNA sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of “drop-out” events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this paper, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information of the same gene from highly similar cells. scDoc will be the first method that involves drop-out information to account for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined/defined. We evaluated the performance of the new method using both simulated data and real scRNA-seq studies. Results show that the proposed method can impute the drop-out events more accurately and robustly, specifically, it outperforms all available imputation methods in reference to data visualization, cell subpopulation identification, and differential expression detection in scRNA-seq data.

Authors who are presenting talks have a * after their name.

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