Paper Info
Title
Differential gene expression analysis in single-cell RNA sequencing data
Abstract
Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a logistic regression model and a nonparametric method based on Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model is used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated data and real data to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity.
Year
DOI
Venue
2017
10.1109/BIBM.2017.8217650
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
DocType
ISSN
single-cell RNAseq,differential gene expression analysis,nonparametric models,multimodal data
Conference
2156-1125
ISBN
Citations 
PageRank 
978-1-5090-3051-4
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Tianyu Wang112030.07
Sheida Nabavi2188.68