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Modeling Bi-modality Improves Characterization of Cell Cycle on Gene Expression in Single Cells
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 10, Iss 7, p e1003696 (2014)
- Publication Year :
- 2014
- Publisher :
- Public Library of Science (PLoS), 2014.
-
Abstract
- Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%–17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.<br />Author Summary Recent technological advances have enabled the measurement of gene expression in individual cells, revealing that there is substantial variability in expression, even within a homogeneous cell population. In this paper, we develop new analytical methods that account for the intrinsic, stochastic nature of single cell expression in order to characterize the effect of cell cycle on gene expression at the single-cell level. Applying these methods to populations of asynchronously cycling cells, we are able to identify large numbers of genes with cell cycle-associated expression patterns. By measuring and adjusting for cellular-level factors, we are able to derive estimates of co-expressing gene networks that more closely reflect cellular-level processes as opposed to sample-level processes. We find that cell cycle phase only accounts for a modest amount of the overall variability of gene expression within an individual cell. The analytical methods demonstrated in this paper are universally applicable to single cell expression data and represent a promising tool to the scientific community.
- Subjects :
- Cell
Gene regulatory network
Gene Expression
01 natural sciences
010104 statistics & probability
0302 clinical medicine
Gene expression
Molecular Cell Biology
Gene Regulatory Networks
Biology (General)
Genetics
Regulation of gene expression
0303 health sciences
Ecology
Cell Cycle
Cell cycle
Cell Cycle Gene
Cell biology
medicine.anatomical_structure
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Statistics (Mathematics)
Research Article
QH301-705.5
Gene prediction
Phase (waves)
Biostatistics
Biology
Cell Line
Cell cycle phase
Molecular Genetics
03 medical and health sciences
Cellular and Molecular Neuroscience
medicine
Humans
0101 mathematics
Statistical Methods
Gene
Molecular Biology
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Models, Genetic
Gene Expression Profiling
Computational Biology
Biology and Life Sciences
Cell Biology
Genes, cdc
Gene expression profiling
Cell culture
Mathematics
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....f140ac7057736f83f75693c725ef9aec