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BioMed Research International
Volume 2014, Article ID 969768, 8 pages
Research Article

Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series

1Department of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, China
2Health Ministry Key Lab of Cell Transplantation, Harbin 150001, China
3Heilongjiang Institute of Hematology and Oncology, Harbin 150001, China
4College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
5College of Life Science, Heilongjiang University, Harbin 150080, China

Received 16 February 2014; Revised 5 April 2014; Accepted 6 April 2014; Published 19 May 2014

Academic Editor: Leng Han

Copyright © 2014 Hui Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

Figure S1: Compare of the cluster dendrogram of gene expression patterns for COGO method and direct clustering method.

Figure S2: The functional relationship networks of categories and enriched GO terms of molecular function.

Figure S3: The functional relationship networks of categories and enriched GO terms of cellular component.

Figure S4: Comparison of clustering performance for COGO and STEM.

Figure S5: Classification performance comparisons of COGO, STEM and Direct clustering method using the colon cancer dataset.

Figure S6: Performance comparisons of COGO and direct clustering method using the rat pineal gland RNA-seq dataset.

Figure S7: The functional similarity of GO terms of biological process branch from the category C12.

Table S1: The results of co-expression patterns contain original gene expression values, Der, SE and co-expression pattern categories.

Table S2: GO function analysis results of co-expression categories of interest and functional modules recognized by similarity scores.

Table S3: The comparison table of GO enrichment analysis work of pairwise DE method and COGO method.

  1. Supplementary Material