Abstract
In this paper, we studied and analyzed the significant ontologies by gene ontology in which the differentially expressed genes (DEG) of human pancreatic stellate cell participate. We identified up-regulated and down-regulated differentially expressed genes between dose response and time course gene expression data after retinoic acid treatment of human pancreatic stellate cells. We first perform statistical t-test and calculate false discovery rate (FDR) then compute quantile value of test and found minimum FDR. We set the pvalue cutoff at 0.02 as threshold and get 213 up-regulated (increased in expression) genes and 99 down-regulated (decreased in expression) genes and analyzed the significant GO terms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Biology 4361. Differential gene expression. Technical report, Developmental Biology (2008)
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. 57, 289–300 (1995)
Chen, Y., Wu, R., Felton, J., et al.: A method to detect differential gene expression in cross-species hybridization experiments at gene and probe level. Biomedical Informatics Insights 3, 1–10 (2010)
Chen, Z., Liu, J., Tony Ng, H.K., et al.: Statistical methods on detecting differentially expressed genes for rna-seq data. BMC Systems Biology 5(3), 1–9 (2011)
Dudoit, S., Yang, Y.H., et al.: Statistical methods for identifying diferentially expressed genes in replicated cdna microarray experiments. Technical report, Department of Biochemistry, Stanford University, Stanford University School of Medicine, Beckman Center, B400 Stanford, CA (2000)
Dudoit, S., Shaffer, J.P., Boldrick, J.C.: Multiple hypothesis testing in microarray experiment. Statistical Science 18, 71–103 (2003)
Dudoit, S., Yang, Y.H., Callow, M.J., et al.: Statistical methods for identifying differentially expressed genes in replicates cdna microarray experiments. Statistica Sinica 12 (2002)
Kauffmann, A., Huber, W.: Microarray data quality control improves the detection of differentially expressed genes. Genomics 95 (2010)
Kumar, A.A., Holm, L., Toronen, P.: Gopargenpy: a high throughput method to generate gene ontology data matrices. BMC Bioinformatics 14, 242 (2013)
Ospina, L., Kleine, L.: Identification of differentially expressed genes in microarray data in a principal component space. SpringerPlus 2, 60 (2013)
Troyanskaya, O.G., Garber, M.E., et al.: Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 18(11) (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Barman, B., Mukhopadhyay, A. (2015). Studying Gene Ontological Significance of Differentially Expressed Genes in Human Pancreatic Stellate Cell. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-13728-5_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13727-8
Online ISBN: 978-3-319-13728-5
eBook Packages: EngineeringEngineering (R0)