Title:Testing the Significance of Ranked Gene Sets in Genome-wide Transcriptome
Profiling Data Using Weighted Rank Correlation Statistics
Volume: 25
Issue: 3
Author(s): Min Yao, Hao He, Binyu Wang, Xinmiao Huang, Sunli Zheng, Jianwu Wang, Xuejun Gao and Tinghua Huang*
Affiliation:
- College of Animal Science, Yangtze University, Jingzhou, Hubei, 434025, China
Keywords:
Flaver, ranked gene set, enrichment analysis, weighted rank correlation, GSEA, GOStats, transcription factor.
Abstract:
Background: Popular gene set enrichment analysis approaches assumed that genes in the
gene set contributed to the statistics equally. However, the genes in the transcription factors (TFs)
derived gene sets, or gene sets constructed by TF targets identified by the ChIP-Seq experiment,
have a rank attribute, as each of these genes have been assigned with a p-value which indicates the
true or false possibilities of the ownerships of the genes belong to the gene sets.
Objectives: Ignoring the rank information during the enrichment analysis will lead to improper statistical
inference. We address this issue by developing of new method to test the significance of
ranked gene sets in genome-wide transcriptome profiling data.
Methods: A method was proposed by first creating ranked gene sets and gene lists and then applying
weighted Kendall's tau rank correlation statistics to the test. After introducing top-down weights
to the genes in the gene set, a new software called "Flaver" was developed.
Results: Theoretical properties of the proposed method were established, and its differences over
the GSEA approach were demonstrated when analyzing the transcriptome profiling data across 55
human tissues and 176 human cell-lines. The results indicated that the TFs identified by our method
have higher tendency to be differentially expressed across the tissues analyzed than its competitors.
It significantly outperforms the well-known gene set enrichment analyzing tools, GOStats (9%) and
GSEA (17%), in analyzing well-documented human RNA transcriptome datasets.
Conclusions: The method is outstanding in detecting gene sets of which the gene ranks were correlated
with the expression levels of the genes in the transcriptome data.
