publication . Article . Other literature type . 2017

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes.

Atif Khan; Juilee Thakar;
Open Access English
  • Published: 06 Jun 2017 Journal: BMC Bioinformatics, volume 18 (eissn: 1471-2105, Copyright policy)
  • Publisher: BioMed Central
Abstract
Background Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for ...
Subjects
free text keywords: Research Article, Epithelial cells, Dendritic cells, Gene-sets, Influenza infections, Gene-gene mutual information, Overlapping gene-sets, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Funded by
NIH| Virology/Immunology Core
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5P30AI078498-04
  • Funding stream: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
51 references, page 1 of 4

Mootha, VK, Lindgren, CM, Eriksson, KF, Subramanian, A, Sihag, S, Lehar, J, Puigserver, P, Carlsson, E, Ridderstråle, M, Laurila, E, Houstis, N. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003; 34 (3): 267-73 [OpenAIRE] [PubMed] [DOI]

Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA, Paulovich, A, Pomeroy, SL, Golub, TR, Lander, ES, Mesirov, JP. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005; 102 (43): 15545-50 [OpenAIRE] [PubMed] [DOI]

Abatangelo, L, Maglietta, R, Distaso, A, D’Addabbo, A, Creanza, TM, Mukherjee, S, Ancona, N. Comparative study of gene-set enrichment methods. BMC Bioinformatics. 2009; 10 (1): 1 [OpenAIRE] [PubMed] [DOI]

Greenblum, SI, Efroni, S, Schaefer, CF, Buetow, KH. The PathOlogist: an automated tool for pathway-centric analysis. BMC Bioinformatics. 2011; 12 (1): 1 [OpenAIRE] [PubMed] [DOI]

Wu, MC, Lin, X. Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene-sets and pathways. Stat Methods Med Res. 2009; 18 (6): 577-93 [OpenAIRE] [PubMed] [DOI]

6.Yaari G, Bolen CR, Thakar J, Kleinstein SH. Quantitative set analysis for gene expression: a method to quantify gene-set differential expression including gene-gene correlations. Nucleic Acids Res. 2013;41(18):gkt660.

Thakar, J, Hartmann, BM, Marjanovic, N, Sealfon, SC, Kleinstein, SH. Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism. BMC Immunol. 2015; 16 (1): 46 [OpenAIRE] [PubMed] [DOI]

Thakar, J, Mohanty, S, West, AP, Joshi, SR, Ueda, I, Wilson, J, Meng, H, Blevins, TP, Tsang, S, Trentalange, M, Siconolfi, B. Aging-dependent alterations in gene expression and a mitochondrial signature of responsiveness to human influenza vaccination. Aging. 2015; 7 (1): 38-52 [OpenAIRE] [PubMed] [DOI]

Chaussabel, D, Baldwin, N. Democratizing systems immunology with modular transcriptional repertoires analyses. Nature reviews. Immunology. 2014; 14 (4): 271 [OpenAIRE] [PubMed]

Chaussabel, D, Quinn, C, Shen, J, Patel, P, Glaser, C, Baldwin, N, Stichweh, D, Blankenship, D, Li, L, Munagala, I, Bennett, L. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity. 2008; 29 (1): 150-64 [OpenAIRE] [PubMed] [DOI]

Li, S, Rouphael, N, Duraisingham, S, Romero-Steiner, S, Presnell, S, Davis, C, Schmidt, DS, Johnson, SE, Milton, A, Rajam, G, Kasturi, S. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol. 2014; 15 (2): 195-204 [OpenAIRE] [PubMed] [DOI]

Obermoser, G, Presnell, S, Domico, K, Xu, H, Wang, Y, Anguiano, E, Thompson-Snipes, L, Ranganathan, R, Zeitner, B, Bjork, A, Anderson, D. Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines. Immunity. 2013; 38 (4): 831-44 [OpenAIRE] [PubMed] [DOI]

Ramilo, O, Allman, W, Chung, W, Mejias, A, Ardura, M, Glaser, C, Wittkowski, KM, Piqueras, B, Banchereau, J, Palucka, AK, Chaussabel, D. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood. 2007; 109 (5): 2066-77 [OpenAIRE] [PubMed] [DOI]

Belacel, N, Wang, Q, Cuperlovic-Culf, M. Clustering methods for microarray gene expression data. OMICS. 2006; 10 (4): 507-31 [OpenAIRE] [PubMed] [DOI]

Jiang, D, Tang, C, Zhang, A. Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng. 2004; 16 (11): 1370-86 [DOI]

51 references, page 1 of 4
Abstract
Background Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for ...
Subjects
free text keywords: Research Article, Epithelial cells, Dendritic cells, Gene-sets, Influenza infections, Gene-gene mutual information, Overlapping gene-sets, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Funded by
NIH| Virology/Immunology Core
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5P30AI078498-04
  • Funding stream: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
51 references, page 1 of 4

Mootha, VK, Lindgren, CM, Eriksson, KF, Subramanian, A, Sihag, S, Lehar, J, Puigserver, P, Carlsson, E, Ridderstråle, M, Laurila, E, Houstis, N. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003; 34 (3): 267-73 [OpenAIRE] [PubMed] [DOI]

Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA, Paulovich, A, Pomeroy, SL, Golub, TR, Lander, ES, Mesirov, JP. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005; 102 (43): 15545-50 [OpenAIRE] [PubMed] [DOI]

Abatangelo, L, Maglietta, R, Distaso, A, D’Addabbo, A, Creanza, TM, Mukherjee, S, Ancona, N. Comparative study of gene-set enrichment methods. BMC Bioinformatics. 2009; 10 (1): 1 [OpenAIRE] [PubMed] [DOI]

Greenblum, SI, Efroni, S, Schaefer, CF, Buetow, KH. The PathOlogist: an automated tool for pathway-centric analysis. BMC Bioinformatics. 2011; 12 (1): 1 [OpenAIRE] [PubMed] [DOI]

Wu, MC, Lin, X. Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene-sets and pathways. Stat Methods Med Res. 2009; 18 (6): 577-93 [OpenAIRE] [PubMed] [DOI]

6.Yaari G, Bolen CR, Thakar J, Kleinstein SH. Quantitative set analysis for gene expression: a method to quantify gene-set differential expression including gene-gene correlations. Nucleic Acids Res. 2013;41(18):gkt660.

Thakar, J, Hartmann, BM, Marjanovic, N, Sealfon, SC, Kleinstein, SH. Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism. BMC Immunol. 2015; 16 (1): 46 [OpenAIRE] [PubMed] [DOI]

Thakar, J, Mohanty, S, West, AP, Joshi, SR, Ueda, I, Wilson, J, Meng, H, Blevins, TP, Tsang, S, Trentalange, M, Siconolfi, B. Aging-dependent alterations in gene expression and a mitochondrial signature of responsiveness to human influenza vaccination. Aging. 2015; 7 (1): 38-52 [OpenAIRE] [PubMed] [DOI]

Chaussabel, D, Baldwin, N. Democratizing systems immunology with modular transcriptional repertoires analyses. Nature reviews. Immunology. 2014; 14 (4): 271 [OpenAIRE] [PubMed]

Chaussabel, D, Quinn, C, Shen, J, Patel, P, Glaser, C, Baldwin, N, Stichweh, D, Blankenship, D, Li, L, Munagala, I, Bennett, L. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity. 2008; 29 (1): 150-64 [OpenAIRE] [PubMed] [DOI]

Li, S, Rouphael, N, Duraisingham, S, Romero-Steiner, S, Presnell, S, Davis, C, Schmidt, DS, Johnson, SE, Milton, A, Rajam, G, Kasturi, S. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol. 2014; 15 (2): 195-204 [OpenAIRE] [PubMed] [DOI]

Obermoser, G, Presnell, S, Domico, K, Xu, H, Wang, Y, Anguiano, E, Thompson-Snipes, L, Ranganathan, R, Zeitner, B, Bjork, A, Anderson, D. Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines. Immunity. 2013; 38 (4): 831-44 [OpenAIRE] [PubMed] [DOI]

Ramilo, O, Allman, W, Chung, W, Mejias, A, Ardura, M, Glaser, C, Wittkowski, KM, Piqueras, B, Banchereau, J, Palucka, AK, Chaussabel, D. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood. 2007; 109 (5): 2066-77 [OpenAIRE] [PubMed] [DOI]

Belacel, N, Wang, Q, Cuperlovic-Culf, M. Clustering methods for microarray gene expression data. OMICS. 2006; 10 (4): 507-31 [OpenAIRE] [PubMed] [DOI]

Jiang, D, Tang, C, Zhang, A. Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng. 2004; 16 (11): 1370-86 [DOI]

51 references, page 1 of 4
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