Integrated single cell data analysis reveals cell specific networks and novel coactivation markers. Issue 5 (December 2016)

• Record Type: Journal Article
• Authors: Ghazanfar, Shila; Bisogni, Adam; Ormerod, John; Lin, David; Yang, Jean
• Is Part Of: BMC systems biology. Volume 10:Issue 5(2016)
• Page Start: 11
• Page End: 24
• Publication Date: 2016-12
• Subjects: Single-cell transcriptomics -- RNA-sequencing -- Mixture modelling -- ScRNA-Seq -- Olfactory sensory neuron -- Neuron; Biological systems -- Periodicals; Biology -- Research -- Periodicals; Cell physiology -- Periodicals; Genes -- Analysis -- Periodicals; 571
• DOI: 10.1186/s12918-016-0370-4
• Languages: English
• ISSNs: 1752-0509

Abstract

Abstract Background Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression. Results We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons. Conclusion Integration of multiple datasets promises to bring about more statistical power to identify genes and patterns of interest. We found that transforming the data into active and inactive gene states allowed for more direct comparison of datasets, leading to identification of maturity marker genes and cell-specific network observations, taking into account the unique characteristics of single cell transcriptomics data.