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Title: SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles

Abstract

Molecular signatures are collections of genes characteristic of a particular cell type, tissue, disease, or perturbation. Signatures can also be used to interpret expression profiles generated from heterogeneous samples. Large collections of gene signatures have been previously developed and catalogued in the MSigDB database. In addition, several consortia and large-scale projects have systematically profiled broad collections of purified primary cells, molecular perturbations of cell types, and tissues from specific diseases, and the specificity and breadth of these datasets can be leveraged to create additional molecular signatures. However, to date there are few tools that allow the visualization of individual signatures across large numbers of expression profiles. Signature visualization of individual samples allows, for example, the identification of patient subcategories a priori on the basis of well-defined molecular signatures.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Univ. of California, Los Angeles, CA (United States)
Publication Date:
Research Org.:
Univ. of California, Los Angeles, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1504905
Grant/Contract Number:  
FC02-02ER63421
Resource Type:
Accepted Manuscript
Journal Name:
BMC Genomics
Additional Journal Information:
Journal Volume: 18; Journal Issue: 1; Journal ID: ISSN 1471-2164
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Molecular signatures; Transcriptomic analysis; Tissue-specific expression; Heterogeneous samples; Visualization tools

Citation Formats

Lopez, David, Montoya, Dennis, Ambrose, Michael, Lam, Larry, Briscoe, Leah, Adams, Claire, Modlin, Robert L., and Pellegrini, Matteo. SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles. United States: N. p., 2017. Web. doi:10.1186/s12864-017-4167-7.
Lopez, David, Montoya, Dennis, Ambrose, Michael, Lam, Larry, Briscoe, Leah, Adams, Claire, Modlin, Robert L., & Pellegrini, Matteo. SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles. United States. doi:10.1186/s12864-017-4167-7.
Lopez, David, Montoya, Dennis, Ambrose, Michael, Lam, Larry, Briscoe, Leah, Adams, Claire, Modlin, Robert L., and Pellegrini, Matteo. Wed . "SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles". United States. doi:10.1186/s12864-017-4167-7. https://www.osti.gov/servlets/purl/1504905.
@article{osti_1504905,
title = {SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles},
author = {Lopez, David and Montoya, Dennis and Ambrose, Michael and Lam, Larry and Briscoe, Leah and Adams, Claire and Modlin, Robert L. and Pellegrini, Matteo},
abstractNote = {Molecular signatures are collections of genes characteristic of a particular cell type, tissue, disease, or perturbation. Signatures can also be used to interpret expression profiles generated from heterogeneous samples. Large collections of gene signatures have been previously developed and catalogued in the MSigDB database. In addition, several consortia and large-scale projects have systematically profiled broad collections of purified primary cells, molecular perturbations of cell types, and tissues from specific diseases, and the specificity and breadth of these datasets can be leveraged to create additional molecular signatures. However, to date there are few tools that allow the visualization of individual signatures across large numbers of expression profiles. Signature visualization of individual samples allows, for example, the identification of patient subcategories a priori on the basis of well-defined molecular signatures.},
doi = {10.1186/s12864-017-4167-7},
journal = {BMC Genomics},
number = 1,
volume = 18,
place = {United States},
year = {2017},
month = {10}
}

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Cited by: 6 works
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Works referenced in this record:

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
journal, September 2005

  • Subramanian, A.; Tamayo, P.; Mootha, V. K.
  • Proceedings of the National Academy of Sciences, Vol. 102, Issue 43, p. 15545-15550
  • DOI: 10.1073/pnas.0506580102