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https://doi.org/10.1101/475426...
Article . 2018 . Peer-reviewed
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Journal of Bioinformatics and Computational Biology
Article . 2020 . Peer-reviewed
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Stably expressed genes in single-cell RNA-sequencing

Authors: Deeke, Julie M.; Gagnon-Bartsch, Johann A.;

Stably expressed genes in single-cell RNA-sequencing

Abstract

Abstract Motivation In single-cell RNA-sequencing (scRNA-seq) experiments, RNA transcripts are extracted and measured from isolated cells to understand gene expression at the cellular level. Measurements from this technology are affected by many technical artifacts, including batch effects. In analogous bulk gene expression experiments, external references, e.g., synthetic gene spike-ins often from the External RNA Controls Consortium (ERCC), may be incorporated to the experimental protocol for use in adjusting measurements for technical artifacts. In scRNA-seq experiments, the use of external spike-ins is controversial due to dissimilarities with endogenous genes and uncertainty about sufficient precision of their introduction. Instead, endogenous genes with highly stable expression could be used as references within scRNA-seq to help normalize the data. First, however, a specific notion of stable expression at the single cell level needs to be formulated; genes could be stable in absolute expression, in proportion to cell volume, or in proportion to total gene expression. Different types of stable genes will be useful for different normalizations and will need different methods for discovery. Results We compile gene sets whose products are associated with cellular structures and record these gene sets for future reuse and analysis. We find that genes whose final product are associated with the cytosolic ribosome have expressions that are highly stable with respect to the total RNA content. Notably, these genes appear to be stable in bulk measurements as well. Supplementary information The Supplement is available on bioRxiv, and the gene set database is available through GitHub. Contact johanngb@umich.edu

Keywords

Mice, Databases, Factual, Sequence Analysis, RNA, Gene Expression Profiling, Animals, Computational Biology, Humans, Single-Cell Analysis

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Top 10%
Top 10%
Green
hybrid