ABSTRACT
Gene set analysis aims to identify differentially expressed or co-expressed genes within a biological pathway between two experimental conditions, so that it can eventually reveal biological processes and pathways involved in disease development. In the last few decades, various statistical and computational methods have been proposed to improve statistical power of gene set analysis. In recent years, much attention has been paid to differentially co-expressed genes since they can be potentially disease-related genes without significant difference in average expression levels between two conditions. In this paper, we propose a new statistical method to identify differentially co-expressed genes from microarray gene expression data. The proposed method first estimates co-expression levels of paired genes using covariance regularization by thresholding, and then significance of difference in covariance estimation between two conditions is evaluated. We demonstrated that the proposed method is more powerful than the existing main-stream methods to detect co-expressed genes through extensive simulation studies. Also, we applied it to various microarray gene expression datasets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer.
Subject(s)
Breast Neoplasms/genetics , Computational Biology/methods , Gene Expression Profiling/methods , Oligonucleotide Array Sequence Analysis/methods , Breast Neoplasms/pathology , Computer Simulation , Female , Gene Expression Profiling/statistics & numerical data , Gene Expression Regulation, Neoplastic , Humans , Mutation , Oligonucleotide Array Sequence Analysis/statistics & numerical data , Tumor Suppressor Protein p53/geneticsABSTRACT
PURPOSE: This study was performed to investigate the incidence and configuration of the bifid mandibular canal in a Korean population by using cone-beam computed tomography (CBCT) imaging. MATERIALS AND METHODS: CBCT images of 1933 patients (884 male and 1049 female) were evaluated using PSR-9000N and Alphard-Vega 3030 Dental CT units (Asahi Roentgen Ind. Co., Ltd, Kyoto, Japan). Image analysis was performed by using OnDemand3D software (CyberMed Inc., Seoul, Korea). The bifid mandibular canal was identified and classified into four types, namely, the forward canal, buccolingual canal, dental canal, and retromolar canal. Statistical analysis was performed by using the chi-squared test and one-way analysis of variance (ANOVA). RESULTS: Bifid mandibular canals were observed in 198 (10.2%) of 1933 patients. The most frequently observed type of bifid mandibular canal was the retromolar canal (n=104, rate: 52.5%) without any significant difference among the incidence of each age and gender. The mean diameter of the accessory canal was 1.27 mm (range: 0.27-3.29 mm) without any significant difference among the mean diameter of each type of the bifid mandibular canal. The mean length of the bifid mandibular canals was 14.97mm(range: 2.17-38.8 mm) with only a significant difference between the dental canal and the other types. CONCLUSION: The bifid mandibular canal is not uncommon in Koreans and has a prevalence of 10.2% as indicated in the present study. It is suggested that a CBCT examination be recommended for detecting a bifid canal.
