tmod: an R package for general and multivariate enrichment analysis

German Conference on Bioinformatics, GCB 2016

Volumn , Issue , 2016, Pages

  Weiner, January ^a   Domaszewska, Teresa ^a  

^a MAX PLANCK INSTITUTE FOR INFECTION BIOLOGY   (Germany)

Author keywords

Gene set enrichment analysis; metabolic profiling; metabolomics; transcription

Indexed keywords

DATA VISUALIZATION; GENE ONTOLOGY; METABOLITES; MULTIVARIANT ANALYSIS; STATISTICAL TESTS; VISUALIZATION;

'OMICS'; DATA SET; DISEASE PROCESS; DISEASE TREATMENT; FEATURES SETS; GENE ONTOLOGY TERMS; GENE SET ENRICHMENT ANALYSIS; METABOLIC PROFILING; METABOLOMICS; PATHWAY GENES;

TRANSCRIPTION;

EID: 85180014966     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.7287/peerj.preprints.2420v1     Document Type: Conference Paper

References (22)

1. Alsina L., Israelsson E., Altman MC., Dang KK., Ghandil P., Israel L., Bernuth H von., Baldwin N., Qin H., Jin Z., others. 2014. A narrow repertoire of transcriptional modules responsive to pyogenic bacteria is impaired in patients carrying loss-of-function mutations in myd88 or irak4. Nature immunology 15:1134–1142.
2. Bar-Joseph Z., Gerber GK., Lee TI., Rinaldi NJ., Yoo JY., Robert F., Gordon DB., Fraenkel E., Jaakkola TS., Young RA., others. 2003. Computational discovery of gene modules and regulatory networks. Nature biotechnology 21:1337–1342.
3. Berry MP., Graham CM., McNab FW., Xu Z., Bloch SA., Oni T., Wilkinson KA., Banchereau R., Skinner J., Wilkinson RJ., others. 2010. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466:973–977.
4. Calvano SE., Xiao W., Richards DR., Felciano RM., Baker HV., Cho RJ., Chen RO., Brownstein BH., Cobb JP., Tschoeke SK., others. 2005. A network-based analysis of systemic inflammation in humans. Nature 437:1032–1037.
5. Chaussabel D., Quinn C., Shen J., Patel P., Glaser C., Baldwin N., Stichweh D., Blankenship D., Li L., Munagala I., others. 2008. A modular analysis framework for blood genomics studies: Application to systemic lupus erythematosus. Immunity 29:150–164.
6. Efron B., Tibshirani R. 2007. On testing the significance of sets of genes. The annals of applied statistics:107–129.
7. Esterhuyse MM., Weiner J., Caron E., Loxton AG., Iannaccone M., Wagman C., Saikali P., Stanley K., Wolski WE., Mollenkopf H-J., others. 2015. Epigenetics and proteomics join transcriptomics in the quest for tuberculosis biomarkers. mBio 6:e01187–15.
8. Falcon S., Gentleman R. 2007. Using gostats to test gene lists for go term association. Bioinformatics 23:257–258.
9. Kaforou M., Wright VJ., Oni T., French N., Anderson ST., Bangani N., Banwell CM., Brent AJ., Crampin AC., Dockrell HM., others. 2013. Detection of tuberculosis in hiv-infected and-uninfected african adults using whole blood rna expression signatures: A case-control study. PLoS Med 10:e1001538.
10. Kelder T., Iersel MP van., Hanspers K., Kutmon M., Conklin BR., Evelo CT., Pico AR. 2012. WikiPathways: Building research communities on biological pathways. Nucleic acids research 40:D1301–D1307.
11. Li S., Rouphael N., Duraisingham S., Romero-Steiner S., Presnell S., Davis C., Schmidt DS., Johnson SE., Milton A., Rajam G., others. 2014. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nature immunology 15:195–204.
12. Liu X., Jessen WJ., Sivaganesan S., Aronow BJ., Medvedovic M. 2007. Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and chip-chip data. BMC bioinformatics 8:283.
13. Maertzdorf J., Ota M., Repsilber D., Mollenkopf HJ., Weiner J., Hill PC., Kaufmann SH. 2011. Functional correlations of pathogenesis-driven gene expression signatures in tuberculosis. PloS one 6:e26938.
14. Pascual V., Chaussabel D., Banchereau J. 2010. A genomic approach to human autoimmune diseases. Annual review of immunology 28:535.
15. Ritchie ME., Phipson B., Wu D., Hu Y., Law CW., Shi W., Smyth GK. 2015. Limma powers differential expression analyses for rna-sequencing and microarray studies. Nucleic acids research:gkv007.
16. Smyth GK. 2005. Limma: Linear models for microarray data. In: Bioinformatics and computational biology solutions using r and bioconductor. Springer, 397–420.
17. Subramanian A., Tamayo P., Mootha VK., Mukherjee S., Ebert BL., Gillette MA., Paulovich A., Pomeroy SL., Golub TR., Lander ES., others. 2005. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 102:15545–15550.
18. Vremo L., Nielsen J., Nookaew I. 2013. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic acids research:gkt111.
19. Weiner 3rd J., Parida SK., Maertzdorf J., Black GF., Repsilber D., Telaar A., Mohney RP., Arndt-Sullivan C., Ganoza CA., Fa KC., others. 2012. Biomarkers of inflammation, immunosuppression and stress with active disease are revealed by metabolomic profiling of tuberculosis patients. PloS one 7:e40221.
20. Wishart DS., Jewison T., Guo AC., Wilson M., Knox C., Liu Y., Djoumbou Y., Mandal R., Aziat F., Dong E., others. 2012. HMDB 3.0—the human metabolome database in 2013. Nucleic acids research:gks1065.
21. Wu D., Smyth GK. 2012. Camera: A competitive gene set test accounting for inter-gene correlation. Nucleic acids research 40:e133–e133.
22. Yamaguchi KD., Ruderman DL., Croze E., Wagner TC., Velichko S., Reder AT., Salamon H. 2008. IFN-β-regulated genes show abnormal expression in therapy-nave relapsing–remitting ms mononuclear cells: Gene expression analysis employing all reported protein–protein interactions. Journal of neuroimmunology 195:116–120.