Stably expressed genes in single-cell RNA-sequencing
Abstract
MotivationIn 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.\n\nResultsWe 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.\n\nSupplementary informationThe Supplement is available on bioRxiv, and the gene set database is available through GitHub.\n\nContactjohanngb@umich.edu
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 10
- FWCI
- 0.41
- Field percentile
- top 49%
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Evaluating stably expressed genes in single cells
Yingxin Lin, Shila Ghazanfar, … Pengyi Yang·GigaSciencePeer-reviewed·Sep 1, 2019
- Bayesian inference of the gene expression states of single cells from scRNA-seq data
Jérémie Breda, Mihaela Zavolan, Erik van Nimwegen·bioRxiv·Dec 28, 2019
- RNA-Seq is not required to determine stable reference genes for qPCR normalization
Nirmal Kumar Sampathkumar, Venkat Krishnan Sundaram, … Jacqueline C. Mitchell·PLoS Computational BiologyPeer-reviewed·Feb 28, 2022
- Beyond comparisons of means: understanding changes in gene expression at the single-cell level
Catalina A Vallejos, Sylvia Richardson, John C Marioni·bioRxiv·Jan 5, 2016
- Missing Data and Technical Variability in Single-Cell RNA- Sequencing Experiments
Stephanie C. Hicks, F. William Townes, … Rafael A. Irizarry·BiostatisticsPeer-reviewed·Sep 15, 2017
- Normalizing single-cell RNA sequencing data with internal spike-in-like genes
Li Lin, Minfang Song, … Liye Zhang·NAR Genomics and BioinformaticsPeer-reviewed·Aug 18, 2020