Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps-Reference-Cited by-同舟云学术

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Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps

Published:2018-12 Issue:1 Volume:19 Page:
ISSN:1471-2105
Container-title:BMC Bioinformatics
language:en
Short-container-title:BMC Bioinformatics

Author:

Jia Xingang,Han Qiuhong,Lu Zuhong

Funder

Major Program of National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology

Link

http://link.springer.com/content/pdf/10.1186/s12859-018-2495-5.pdf

Reference27 articles.

1. Brazma A, Vilo J. Gene expression data analysis. Febs Lett. 2000; 480(1):17–24.

2. Yu X, Yu G, Wang J. Clustering cancer gene expression data by projective clustering ensemble. PLoS ONE. 2017; 12(2):e171429.

3. Grimes ML, Lee WJ, van der Maaten L, Shannon P. Wrangling phosphoproteomic data to elucidate cancer signaling pathways. PLoS ONE. 2013; 8(1):e52884.

4. Shaik JS, Yeasin M. A unified framework for finding differentially expressed genes from microarray experiments. BMC Bioinformatics. 2007; 8:347.

5. Kong X, Mas V, Archer KJ. A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy. BMC Genomics. 2008; 9:98.

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