• Journal of the Korean Home Economics Association
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• v.49 no.4
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• pp.11-23
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• 2011

The purpose of this study was to identify children's different styles of anger-expression and the characteristics of each anger-expression style. The Q-methodology with a 32 Q-sample was used to investigate and analyze the subjective expression of childrens's anger experiences. The subjects were 22 children living in D city, and the data were collected by means of interviews. The analysis revealed six styles of anger-expression, a solution which accounted for 65.13% of the total variance. The six styles were labeled "Patience type", "Avoidance-conversion type", "Reaction-seek type", "Control type", "Repression type", and "Conversion-consideration type". It was recommended that intervention strategies for children's appropriate anger-expression mode should be based on these six anger-expression styles.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.181-199
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• 2015

In a short history, RNA-seq data have established a revolutionary tool to directly decode various scenarios occurring on whole genome-wide expression profiles in regards with differential expression at gene, transcript, isoform, and exon specific quantification, genetic and genomic mutations, and etc. RNA-seq technique has been rapidly replacing arrays with seq-based platform experimental settings by revealing a couple of advantages such as identification of alternative splicing and allelic specific expression. The remarkable characteristics of high-throughput large-scale expression profile in RNA-seq are lied on expression levels of read counts, structure of correlated samples and genes, larger number of genes compared to sample size, different sampling rates, inevitable systematic RNA-seq biases, and etc. In this study, we will comprehensively review how robust Bayesian and non-parametric methods have a better performance than classical statistical approaches by explicitly incorporating such intrinsic RNA-seq specific features with flexible and more appropriate assumptions and distributions in practice.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.315-321
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• 2011

Gene expression microarray data often include multiple missing values. Most gene expression analysis (including gene clustering analysis); however, require a complete data matric as an input. In ordinary clustering methods, just a single missing value makes one abandon the whole data of a gene even if the rest of data for that gene was intact. The quality of analysis may decrease seriously as the missing rate is increased. In the opposite aspect, the imputation of missing value may result in an artifact that reduces the reliability of the analysis. To clarify this contradiction in microarray clustering analysis, this paper compared the accuracy of clustering with and without imputation over several microarray data having different missing rates. This paper also tested the clustering efficiency of several imputation methods including our propose algorithm. The results showed it is worthwhile to check the clustering result in this alternative way without any imputed data for the imperfect microarray data.

• Animal Bioscience
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• v.36 no.10
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• pp.1517-1529
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• 2023

Objective: The objective of this study was to investigate the phylogenetic and expression analysis of the angiopoietin-like (ANGPTL) gene family and their role in lipid metabolism in pigs. Methods: In this study, the amino acid sequence analysis, phylogenetic analysis, and chromosome adjacent gene analysis were performed to identify the ANGPTL gene family in pigs. According to the body weight data from 60 Jinhua pigs, different tissues of 6 pigs with average body weight were used to determine the expression profile of ANGPTL1-8. The ileum, subcutaneous fat, and liver of 8 pigs with distinct fatness were selected to analyze the gene expression of ANGPTL3, ANGPTL4, and ANGPTL8. Results: The sequence length of ANGPTLs in pigs was between 1,186 and 1,991 bp, and the pig ANGPTL family members shared common features with human homologous genes, including the high similarity of the amino acid sequence and chromosome flanking genes. Amino acid sequence analysis showed that ANGPTL1-7 had a highly conserved domain except for ANGPTL8. Phylogenetic analysis showed that each ANGPTL homologous gene shared a common origin. Quantitative reverse-transcription polymerase chain reaction analysis showed that ANGPTL family members had different expression patterns in different tissues. ANGPTL3 and ANGPTL8 were mainly expressed in the liver, while ANGPTL4 was expressed in many other tissues, such as the intestine and subcutaneous fat. The expression levels of ANGPTL3 in the liver and ANGPTL4 in the liver, intestine and subcutaneous fat of Jinhua pigs with low propensity for adipogenesis were significantly higher than those of high propensity for adipogenesis. Conclusion: These results increase our knowledge about the biological role of the ANGPTL family in this important economic species, it will also help to better understand the role of ANGPTL3, ANGPTL4, and ANGPTL8 in lipid metabolism of pigs, and provide innovative ideas for developing strategies to improve meat quality of pigs.

• Journal of Animal Science and Technology
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• v.57 no.5
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• pp.18.1-18.8
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• 2015

Background: Quantitative real time reverse transcription PCR (qRT-PCR) is one of the most important techniques for gene-expression analysis in molecular based studies. Selecting a proper internal control gene for normalizing data is a crucial step in gene expression analysis via this method. The expression levels of reference genes should be remained constant among cells in different tissues. However, it seems that the location of cells in different tissues might influence their expression. The purpose of this study was to determine whether the source of mesenchymal stem cells (MSCs) has any effect on expression level of three common reference genes (GAPDH, ${\beta}$-actin and ${\beta}2$-microglobulin) in equine marrow- and adipose-derived undifferentiated MSCs and consequently their reliability for comparative qRT-PCR. Materials and methods: Adipose tissue (AT) and bone marrow (BM) samples were harvested from 3 mares. MSCs were isolated and cultured until passage 3 (P3). Total RNA of P3 cells was extracted for cDNA synthesis. The generated cDNAs were analyzed by quantitative real-time PCR. The PCR reactions were ended with a melting curve analysis to verify the specificity of amplicon. Results: The expression levels of GAPDH were significantly different between AT- and BM-derived MSCs (p < 0.05). Differences in expression level of ${\beta}$-actin (P < 0.001) and B2M (P < 0.006.) between MSCs derived from AT and BM were substantially higher than GAPDH. In addition, the fold change in expression levels of GAPDH, ${\beta}$-actin and B2M in AT-derived MSCs compared to BM-derived MSCs were 2.38, 6.76 and 7.76, respectively. Conclusion: This study demonstrated that GAPDH and especially ${\beta}$-actin and B2M express in different levels in equine AT- and BM-derived MSCs. Thus they cannot be considered as reliable reference genes for comparative quantitative gene expression analysis in MSCs derived from equine bone marrow and adipose tissue.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.125-133
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• 1996

In this paper, we present a algebraic technique for computing system reliability for complex system. The method was originally developed as an aid to fault tree analysis but it applies to general problems of reliability assessment. A success expression which directly gives the reliability expression is formed and simplified by the procedure. Several algorithms and examples are illustrated.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.08a
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• pp.103-127
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• 2001

Gene expression studies require statistical experimental designs and validation before laboratory confirmation. Various clustering approaches, such as hierarchical, Kmeans, SOM are commonly used for unsupervised learning in gene expression data. Several classification methods, such as gene voting, SVM, or discriminant analysis are used for supervised lerning, where well-defined response classification is possible. Estimating gene-condition interaction effects require advanced, computationally-intensive statistical approaches.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6347-6350
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• 2013

Post-transplant lymphoproliferative disorder (PTLD) is a common complication of therapeutic immunosuppression after organ transplantation. Gene expression profile facilitates the identification of biological difference between Epstein-Barr virus (EBV) positive and negative PTLDs. Previous studies mainly implemented variance/regression analysis without considering unaccounted array specific factors. The aim of this study is to investigate the gene expression difference between EBV positive and negative PTLDs through partial least squares (PLS) based analysis. With a microarray data set from the Gene Expression Omnibus database, we performed PLS based analysis. We acquired 1188 differentially expressed genes. Pathway and Gene Ontology enrichment analysis identified significantly over-representation of dysregulated genes in immune response and cancer related biological processes. Network analysis identified three hub genes with degrees higher than 15, including CREBBP, ATXN1, and PML. Proteins encoded by CREBBP and PML have been reported to be interact with EBV before. Our findings shed light on expression distinction of EBV positive and negative PTLDs with the hope to offer theoretical support for future therapeutic study.

• Journal of Genetic Medicine
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• v.19 no.2
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• pp.63-75
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• 2022

Purpose: Osteoporosis is a common calcium and metabolic skeletal disease which is characterized by decreased bone mass, microarchitectural deterioration of bone tissue and impaired bone strength, thereby leading to enhanced risk of bone fragility. In this study, we aimed to identify novel genes for susceptibility to osteoporosis and/or bone density. Materials and Methods: To identify differentially expressed genes (DEGs) between control and osteoporosis-induced cells, annealing control primer-based differential display reverse-transcription polymerase chain reaction (RT-PCR) was carried out in pre-osteoblast MC3T3-E1 cells. Expression levels of the identified DEGs were evaluated by quantitative RT-PCR. Association studies for the quantitative bone density analysis and osteoporosis case-control analysis of single nucleotide polymorphism (SNPs) were performed in Korean women (3,570 subjects) from the Korean Association REsource (KARE) study cohort. Results: Comparison analysis of expression levels of the identified DEGs by quantitative RT-PCR found seven genes, Anxa6, Col5a1, Col6a2, Eno1, Myof, Nfib, and Scara5, that showed significantly different expression between the dexamethason-treated and untreated MC3T3-E1 cells and between the ovariectomized osteoporosis-induced mice and sham mice. Association studies revealed that there was a significant association between the SNPs in the five genes, ANXA6, COL5A1, ENO1, MYOF, and SCARA5, and bone density and/or osteoporosis. Conclusion: Using a whole-genome comparative expression analysis, gene expression evaluation analysis, and association analysis, we found five genes that were significantly associated with bone density and/or osteoporosis. Notably, the association P-values of the SNPs in the ANXA6 and COL5A1 genes were below the Bonferroni-corrected significance level.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8143-8147
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• 2014

To investigate the expression intensity and prognostic significance of TGF-${\beta}1$ protein in non-small cell lung cancer (NSCLC), immunohistochemistry was carried out in 194 cases of NSCLC and 24 cases of normal lung tissues by SP methods. The PU (positive unit) value was used to assess the TGF-${\beta}1$ protein expression in systematically selected fields under the microscope with Leica Q500MC image analysis. We found that the TGF-${\beta}1$ PU value was nearly two-fold higher in NSCLC than in normal lung tissues (p=0.000), being associated with TNM stages (p=0.000) and lymph node metastases (p=0.000), but not to patient age, gender, smoking history, tumor differentiation, histological subtype and tumor location (P>0.05). Univariate analysis indicated that patients with high TGF-${\beta}1$ protein expression and lymph node metastases demonstrated a poor prognosis (both p=0.000,). Multivariate analysis showed that TGF-${\beta}1$ protein expression (RR = 2.565, p=0.002) and lymph node metastases (RR=1.874, p=0.030) were also independent prognostic factors. Thus, TGF-${\beta}1$ protein expression may be correlated to oncogenesis and serve as an independent prognostic biomarker for NSCLC.