TY - GEN
T1 - Unsupervised single-cell analysis in triple-negative breast cancer
T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
AU - Athreya, Arjun P.
AU - Gaglio, Alan J.
AU - Kalbarczyk, Zbigniew T.
AU - Iyer, Ravishankar K.
AU - Cairns, Junmei
AU - Kalari, Krishna R.
AU - Weinshilboum, Richard M.
AU - Wang, Liewei
N1 - This material is based upon work partially supported by Mayo Clinic and Illinois Alliance Fellowship for Technology-Based Healthcare Research, CompGen Fellowship, IBM Faculty Award, National Science Foundation (NSF) under Grants CNS 13-37732, National Institutes of Health (NIH) under Grants RO1 GM28157, R01CA19664, U19 GM61388 (The Pharmacogenomics Research Network), Breast SPORE P50CA116201 and U19 GM61388 and Mayo Clinic Center for Individualized Medicine. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF and NIH. We thank Prof. Gene Robinson for helpful comments and Jenny Applequist in helping prepare the manuscript
PY - 2017/1/17
Y1 - 2017/1/17
N2 - This paper demonstrates an unsupervised learning approach to identify genes with significant differential expression across single-cell subpopulations induced by therapeutic treatment. Identifying this set of genes makes it possible to use well-established bioinformatics approaches such as pathway analysis to establish their biological relevance. Then, a biologist can use his/her prior knowledge to investigate in the laboratory, a few particular candidates among the subset of genes overlapping with relevant pathways. Due to the large size of the human genome and limitations in cost and skilled resources, biologists benefit from analytical methods combined with pathway analysis to design laboratory experiments focusing on only a few significant genes. As an example, we show how model-based unsupervised methods can identify a small set of genes (1% of the genome) that have significant differential expression in single-cells and are also highly correlated to pathways (p-value < 1E - 7) with anticancer effects driven by the antidiabetic drug metformin. Further analysis of genes on these relevant pathways reveal three candidate genes previously implicated in several anticancer mechanisms in other cancers, not driven by metformin. Identification of these genes can help biologists and clinicians design laboratory experiments to establish the molecular mechanisms of metformin in triple-negative breast cancer. In a domain where there is no prior knowledge of small biologically significant data, we demonstrate that careful data-driven methods can infer such significant small data to explain biological mechanisms.
AB - This paper demonstrates an unsupervised learning approach to identify genes with significant differential expression across single-cell subpopulations induced by therapeutic treatment. Identifying this set of genes makes it possible to use well-established bioinformatics approaches such as pathway analysis to establish their biological relevance. Then, a biologist can use his/her prior knowledge to investigate in the laboratory, a few particular candidates among the subset of genes overlapping with relevant pathways. Due to the large size of the human genome and limitations in cost and skilled resources, biologists benefit from analytical methods combined with pathway analysis to design laboratory experiments focusing on only a few significant genes. As an example, we show how model-based unsupervised methods can identify a small set of genes (1% of the genome) that have significant differential expression in single-cells and are also highly correlated to pathways (p-value < 1E - 7) with anticancer effects driven by the antidiabetic drug metformin. Further analysis of genes on these relevant pathways reveal three candidate genes previously implicated in several anticancer mechanisms in other cancers, not driven by metformin. Identification of these genes can help biologists and clinicians design laboratory experiments to establish the molecular mechanisms of metformin in triple-negative breast cancer. In a domain where there is no prior knowledge of small biologically significant data, we demonstrate that careful data-driven methods can infer such significant small data to explain biological mechanisms.
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U2 - 10.1109/BIBM.2016.7822581
DO - 10.1109/BIBM.2016.7822581
M3 - Conference contribution
AN - SCOPUS:85013277453
T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
SP - 556
EP - 563
BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
A2 - Burrage, Kevin
A2 - Zhu, Qian
A2 - Liu, Yunlong
A2 - Tian, Tianhai
A2 - Wang, Yadong
A2 - Hu, Xiaohua Tony
A2 - Jiang, Qinghua
A2 - Song, Jiangning
A2 - Morishita, Shinichi
A2 - Burrage, Kevin
A2 - Wang, Guohua
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2016 through 18 December 2016
ER -