The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
An expression atlas of human primary cells: inference of gene function from coexpression networks
Open accessAbstract
BACKGROUND: The specialisation of mammalian cells in time and space requires genes associated with specific pathways and functions to be co-ordinately expressed. Here we have combined a large number of publically available microarray datasets derived from human primary cells and analysed large correlation graphs of these data. RESULTS: Using the network analysis tool BioLayout Express3D we identify robust co-associations of genes expressed in a wide variety of cell lineages. We discuss the biological significance of a number of these associations, in particular the coexpression of key transcription factors with the genes that they are likely to control. CONCLUSIONS: We consider the regulation of genes in human primary cells and specifically in the human mononuclear phagocyte system. Of particular note is the fact that these data do not support the identity of putative markers of antigen-presenting dendritic cells, nor classification of M1 and M2 activation states, a current subject of debate within immunological field. We have provided this data resource on the BioGPS web site (http://biogps.org/dataset/2429/primary-cell-atlas/) and on macrophages.com (http://www.macrophages.com/hu-cell-atlas).
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Cites
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Alternative activation of macrophages: mechanism and functions
Gordon, S., & Martinez, F. O. (2010) · Immunity · doi:10.1016/j.immuni.2010.05.007 · Cited by 4,197
The Transcriptional Landscape of the Mammalian Genome
Carninci, P., Kasukawa, T., Katayama, S., Gough, J., Frith, M. C., Maeda, N., Oyama, R., Ravasi, T., Lenhard, B., Wells, C., et al. (2005) · Science · doi:10.1126/science.1112014 · Cited by 3,499
Self-citationA gene atlas of the mouse and human protein-encoding transcriptomes
Su, A. I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K. A., Block, D., Zhang, J., Soden, R., Hayakawa, M., Kreiman, G., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400782101 · Cited by 3,475
Transcriptional regulation of macrophage polarization: enabling diversity with identity
Lawrence, T., & Natoli, G. (2011) · Nature Reviews Immunology · doi:10.1038/nri3088 · Cited by 2,284
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003) · Science · doi:10.1126/science.1087447 · Cited by 2,256
Martinez, F. O., Gordon, S., Locati, M., & Mantovani, A. (2006) · The Journal of Immunology · doi:10.4049/jimmunol.177.10.7303 · Cited by 2,125
the Immunological Genome Consortium, Gautier, E. L., Shay, T., Miller, J., Greter, M., Jakubzick, C., Ivanov, S., Helft, J., Chow, A., Elpek, K. G., et al. (2012) · Nature Immunology · doi:10.1038/ni.2419 · Cited by 1,972 · cited 2×
“Our conclusions based upon the unbiased clustering of gene expression with BioLayout Express contrast with recent studies on the mouse by the ImmGen consortium [62,63], in which the authors have sought to identify marker genes based upon a preconception of the separate identity of macrophages and DC.”
MethodGenome-wide analysis of mammalian promoter architecture and evolution
Carninci, P., Sandelin, A., Lenhard, B., Katayama, S., Shimokawa, K., Ponjavic, J., Semple, C. A. M., Taylor, M. S., Engström, P. G., Frith, M. C., et al. (2006) · Nature Genetics · doi:10.1038/ng1789 · Cited by 1,350
Self-citationMultiple-laboratory comparison of microarray platforms
Irizarry, R. A., Warren, D., Spencer, F., Kim, I. F., Biswal, S., Frank, B. C., Gabrielson, E., Garcia, J. G. N., Geoghegan, J., Germino, G., et al. (2005) · Nature Methods · doi:10.1038/nmeth756 · Cited by 862
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Aran, D., Hu, Z., & Butte, A. J. (2017) · Genome Biology · doi:10.1186/s13059-017-1349-1 · Cited by 4,397
Barkley, D., Moncada, R., Pour, M., Liberman, D. A., Dryg, I., Werba, G., Wang, W., Baron, M., Rao, A., Xia, B., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01141-9 · Cited by 532
Lee, J. C., Biasci, D., Roberts, R., Gearry, R. B., Mansfield, J. C., Ahmad, T., Prescott, N. J., Satsangi, J., Wilson, D. C., Jostins, L., et al. (2017) · Nature Genetics · doi:10.1038/ng.3755 · Cited by 304
Research Advances in How the cGAS-STING Pathway Controls the Cellular Inflammatory Response
Wan, D., Jiang, W., & Hao, J. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00615 · Cited by 270
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Metadata
- Authors
- Mabbott, N. A., Baillie, J., Brown, H., Freeman, T. C., & Hume, D. A.
- Venue
- BMC Genomics
- Published
- 2013
- Volume
- 14
- Issue
- 1
- Pages
- 632
- ISSN
- 1471-2164 (ISSN-L)
- OpenAlex
- W2151572058
- Cited by
- 474
- References
- 63
- FWCI
- 4.44
- Type
- journal article
- Language
- en
- Open access
- gold
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