A Fisher Exact Test Will Be More Proper

Liao, Yuan-Ya; Lee, Tu-Sheng; Lin, and Yu-Min

doi:10.1148/radiol.2391051114

Cited by 15 publications

(7 citation statements)

References 2 publications

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“…The DEGs for each group of HNSCC patients were defined as N. Fisher's exact test (Liao et al, 2006) was used to test the significance of the overlap between Mi and N. P < 0.05 was set as the cut-off to identify relevant miRNAs, TFs and drugs for each group of DEGs.…”

Section: Screening Of Relevant Mirnas Tfs and Drugsmentioning

confidence: 99%

Critical genes in head and neck squamous cell carcinoma revealed by bioinformatic analysis of gene expression data

Wang

Wang²,

Cao

et al. 2015

Genet. Mol. Res.

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ABSTRACT. In this study, bioinformatic analysis of gene expression data of head and neck squamous cell carcinoma (HNSCC) was performed to identify critical genes. Gene expression data of HNSCC were downloaded from the Cancer Genome Atlas (TCGA) and differentially expressed genes were determined through significance analysis of microarrays. Protein-protein interaction networks were constructed and used to identify hub genes. Functional enrichment analysis was performed with DAVID. Relevant microRNAs, transcription factors, and small molecule drugs were predicted by the Fisher exact test. Survival analysis was performed with the Kaplan-Meier plot from a package for survival analysis in R. In the five groups of HNSCC patients, a total of 5946 DEGs were identified in group 1, 4575 DEGs in group 2, 5580 DEGs in group 3, 8017 DEGs in group 4, and 5469 DEGs in group 5. DEGs in the cell cycle and immune response were significantly over-represented. Five PPI networks were constructed from (2015) which hub genes were acquired, such as minichromosome maintenance complex component 7 (MCM7), MCM2, decorin (DCN), retinoblastoma 1 (RB1), and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma (YWHAG). No significant difference in survival was observed among the 5 groups; however, a significant difference existed between two combined groups (groups 1, 3, and 5 vs groups 2 and 4). Our study revealed critical genes in HNSCC, which could supplement the knowledge about the pathogenesis of HNSCC and provide clues for future therapy development.

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Section: Screening Of Relevant Mirnas Tfs and Drugsmentioning

confidence: 99%

Critical genes in head and neck squamous cell carcinoma revealed by bioinformatic analysis of gene expression data

Wang

Wang²,

Cao

et al. 2015

Genet. Mol. Res.

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“…SNPsyn detects synergy SNP pairs taking use of mutual information, and can also give SNP importance measurement. The performance of Random Forest in genetic data analysis has been investigated which has been shown to out-perform Fisher’s exact test [ 35 ] and many other methods [ 18 ] as a screening tool when interactions are present. For this reason and lack of other high confidence reference datasets, top ranked 150 SNPS predicted by Random Forest in each dataset were used as benchmark datasets (Shown in S2 File ).…”

Section: Resultsmentioning

confidence: 99%

Research on Single Nucleotide Polymorphisms Interaction Detection from Network Perspective

Liu

Wang

et al. 2015

PLoS ONE

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Single Nucleotide Polymorphisms (SNPs) found in Genome-Wide Association Study (GWAS) mainly influence the susceptibility of complex diseases, but they still could not comprehensively explain the relationships between mutations and diseases. Interactions between SNPs are considered so important for deeply understanding of those relationships that several strategies have been proposed to explore such interactions. However, part of those methods perform poorly when marginal effects of disease loci are weak or absent, others may lack of considering high-order SNPs interactions, few methods have achieved the requirements in both performance and accuracy. Considering the above reasons, not only low-order, but also high-order SNP interactions as well as main-effect SNPs, should be taken into account in detection methods under an acceptable computational complexity. In this paper, a new pairwise (or low-order) interaction detection method IG (Interaction Gain) is introduced, in which disease models are not required and parallel computing is utilized. Furthermore, high-order SNP interactions were proposed to be detected by finding closely connected function modules of the network constructed from IG detection results. Tested by a wide range of simulated datasets and four WTCCC real datasets, the proposed methods accurately detected both low-order and high-order SNP interactions as well as disease-associated main-effect SNPS and it surpasses all competitors in performances. The research will advance complex diseases research by providing more reliable SNP interactions.

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“…Screening of subtype related miRNA/TF/drug. The overlapping significance between DEGs (gene set N) in a subgroup and target genes (gene set Mi) of selected miRNA/ TF/drug (i) were detected using Fisher test [22]. The cut-off threshold was set as p<0.05, and if there is significant overlapping, the corresponding gene associated miRNAs/ TFs/drugs were considered as subtype related.…”

Section: Methodsmentioning

confidence: 99%

Integrated analysis of the RNA-Seq data of liver hepatocellular carcinoma

Xue¹,

Luo²,

Yao³

et al. 2018

neo

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The present study aimed to explore the genetic changes involved in the liver hepatocellular carcinoma (HCC) development. The RNA-Seq data of 212 HCC tissue samples and 50 normal tissue samples were downloaded using TCGA-Assembler. A total of 4 subgroups were obtained, and 4167, 6279, 5379, and 2548 DEGs were screened in group 1, group 2, group 3, and group 4, respectively. Enrichment analysis found that cell cycle, metabolism, and translation related terms were the most significantly changed functions and pathways. There were 454 genes (1114 pairs), 803 genes (722 pairs), and 788 genes (724 pairs), separately interacted in the condition specific PPI network of group 1, 2, 3, and 4, with MMP2, ATNXN1, F2, and HDAC1 as the hub genes. What's more, using these genes, total 7, 20, 198, and 1 subtype related miRNAs; 35, 50, 47, and 17 subtype related TFs; 1, 1, 0, and 2 subtype related drugs were screened in group 1, 2, 3, and 4, respectively. The integrated biological analysis on RNA-Seq data provided substantial of bio-molecular related to the HCC development. miR-147b, SP1, and Riboflavin were the subtype-related regulator/drug for HCC. The study about the big data of HCC RNA-Seq data reveals the intrinsic gene expression pattern of the tumor, which provides a novel perspective to understand the heterogeneity of pathogenesis in HCC tumorigenesis.

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A Fisher Exact Test Will Be More Proper

Cited by 15 publications

References 2 publications

Critical genes in head and neck squamous cell carcinoma revealed by bioinformatic analysis of gene expression data

Critical genes in head and neck squamous cell carcinoma revealed by bioinformatic analysis of gene expression data

Research on Single Nucleotide Polymorphisms Interaction Detection from Network Perspective

Integrated analysis of the RNA-Seq data of liver hepatocellular carcinoma

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