MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data
The identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-10-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00953/full |
| _version_ | 1818597198523269120 |
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| author | Nelson Nazzicari Danila Vella Danila Vella Claudia Coronnello Dario Di Silvestre Riccardo Bellazzi Riccardo Bellazzi Simone Marini Simone Marini |
| author_facet | Nelson Nazzicari Danila Vella Danila Vella Claudia Coronnello Dario Di Silvestre Riccardo Bellazzi Riccardo Bellazzi Simone Marini Simone Marini |
| author_sort | Nelson Nazzicari |
| collection | DOAJ |
| description | The identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module identification algorithms are available, although none is explicitly designed to perform the task on single-cell RNA sequencing (scRNA-seq) data. Here, we introduce MTGO-SC, an adaptation for scRNA-seq of our biological network module detection algorithm MTGO. MTGO-SC isolates gene functional modules by leveraging on both the network topological structure and the annotations characterizing the nodes (genes). These annotations are provided by an external source, such as databases and literature repositories (e.g., the Gene Ontology, Reactome). Thanks to the depth of single-cell data, it is possible to define one network for each cell cluster (typically, cell type or state) composing each sample, as opposed to traditional bulk RNA-seq, where the emerging gene network is averaged over the whole sample. MTGO-SC provides two complexity levels for interpretation: the gene-gene interaction and the intermodule interaction networks. MTGO-SC is versatile in letting the users define the rules to extract the gene network and integrated with the Seurat scRNA-seq analysis pipeline. MTGO-SC is available at https://github.com/ne1s0n/MTGOsc. |
| first_indexed | 2024-12-16T11:44:00Z |
| format | Article |
| id | doaj.art-fa690c36af2648c0a22dfa9b7ed605f8 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-16T11:44:00Z |
| publishDate | 2019-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-fa690c36af2648c0a22dfa9b7ed605f82022-12-21T22:32:53ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-10-011010.3389/fgene.2019.00953457354MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing DataNelson Nazzicari0Danila Vella1Danila Vella2Claudia Coronnello3Dario Di Silvestre4Riccardo Bellazzi5Riccardo Bellazzi6Simone Marini7Simone Marini8Research Centre for Fodder Crops and Dairy Productions, Council for Agricultural Research and Economics (CREA), Lodi, ItalyBioengineering Unit, Ri. MED Foundation, Palermo, ItalyIstituti Clinici Scientifici Maugeri, Pavia, ItalyComputational Biology Unit, Ri. MED Foundation, Palermo, ItalyInstitute of Biomedical Technologies, National Research Council, Segrate, ItalyIstituti Clinici Scientifici Maugeri, Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering; Centre for Health, Technologies, University of Pavia, Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering; Centre for Health, Technologies, University of Pavia, Pavia, ItalyDepartment of Surgery, University of Michigan, Ann Arbor, MI, United StatesThe identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module identification algorithms are available, although none is explicitly designed to perform the task on single-cell RNA sequencing (scRNA-seq) data. Here, we introduce MTGO-SC, an adaptation for scRNA-seq of our biological network module detection algorithm MTGO. MTGO-SC isolates gene functional modules by leveraging on both the network topological structure and the annotations characterizing the nodes (genes). These annotations are provided by an external source, such as databases and literature repositories (e.g., the Gene Ontology, Reactome). Thanks to the depth of single-cell data, it is possible to define one network for each cell cluster (typically, cell type or state) composing each sample, as opposed to traditional bulk RNA-seq, where the emerging gene network is averaged over the whole sample. MTGO-SC provides two complexity levels for interpretation: the gene-gene interaction and the intermodule interaction networks. MTGO-SC is versatile in letting the users define the rules to extract the gene network and integrated with the Seurat scRNA-seq analysis pipeline. MTGO-SC is available at https://github.com/ne1s0n/MTGOsc.https://www.frontiersin.org/article/10.3389/fgene.2019.00953/fullsingle cellRNA-seqenrichmentgene networkclusteringgene module |
| spellingShingle | Nelson Nazzicari Danila Vella Danila Vella Claudia Coronnello Dario Di Silvestre Riccardo Bellazzi Riccardo Bellazzi Simone Marini Simone Marini MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data Frontiers in Genetics single cell RNA-seq enrichment gene network clustering gene module |
| title | MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data |
| title_full | MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data |
| title_fullStr | MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data |
| title_full_unstemmed | MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data |
| title_short | MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data |
| title_sort | mtgo sc a tool to explore gene modules in single cell rna sequencing data |
| topic | single cell RNA-seq enrichment gene network clustering gene module |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00953/full |
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