M3Drop: dropout-based feature selection for scRNASeq
نویسندگان
چکیده
منابع مشابه
Introduction to M3Drop: Michaelis-Menten modelling of dropouts in scRNASeq
Single-cell RNA sequencing is able to quantify the whole transcriptome from the small amount of RNA present in individual cells. However, a consequence of reverse-transcribing and amplifying small quantities of RNA is a large number of dropouts, genes with zero expression in particular cells. The frequency of dropout events is strongly non-linearly related to the measured expression levels of t...
متن کاملModelling dropouts for feature selection in scRNASeq experiments
A key challenge of single-cell RNASeq (scRNASeq) is the many genes with zero reads in some cells, but high expression in others. In full-transcript datasets modelling zeros using the Michaelis-Menten equation provides an equal or superior fit to existing scRNASeq datasets compared to other approaches and enables fast and accurate identification of features corresponding to differentially expres...
متن کاملModelling dropouts improves feature selection in scRNASeq experiments
A key challenge of single-cell RNASeq (scRNASeq) is the many genes with zero reads in some cells, but high expression in others. Modelling zeros using the Michaelis-Menten equation provides a superior fit to existing scRNASeq datasets compared to other approaches and enables fast and accurate identification of features corresponding to differentially expressed genes without prior identification...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2018
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/bty1044