A discriminative learning approach to differential expression analysis for single-cell RNA-seq

被引：0
|
作者：
Vasilis Ntranos
Lynn Yi
Páll Melsted
Lior Pachter
机构：
[1] UC Berkeley,Department of Electrical Engineering & Computer Science
[2] Stanford University,Department of Electrical Engineering
[3] UCLA,UCLA–Caltech Medical Science Training Program
[4] California Institute of Technology,Division of Biology and Biological Engineering
[5] University of Iceland,Faculty of Industrial Engineering, Mechanical Engineering and Computer Science
[6] California Institute of Technology,Department of Computing and Mathematical Sciences
来源：
Nature Methods | 2019年 / 16卷
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学科分类号：
摘要：
Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3′ single-cell RNA-seq that can identify previously undetectable marker genes.
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页码：163 / 166
页数：3
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