• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1629-1633
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• 2013

Background: Gene set analysis (GSA) incorporates biological with statistical knowledge to identify gene sets which are differentially expressed that between two or more phenotypes. Materials and Methods: In this paper gene sets differentially expressed between acute lymphoblastic leukaemia (ALL) with BCR-ABL and those with no observed cytogenetic abnormalities were determined by GSA methods. The BCR-ABL is an abnormal gene found in some people with ALL. Results: The results of two GSAs showed that the Category test identified 30 gene sets differentially expressed between two phenotypes, while the Hotelling's $T^2$ could discover just 19 gene sets. On the other hand, assessment of common genes among significant gene sets showed that there were high agreement between the results of GSA and the findings of biologists. In addition, the performance of these methods was compared by simulated and ALL data. Conclusions: The results on simulated data indicated decrease in the type I error rate and increase the power in multivariate (Hotelling's $T^2$) test as increasing the correlation between gene pairs in contrast to the univariate (Category) test.

• The Journal of the Korean bone and joint tumor society
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• v.3 no.1
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• pp.1-8
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• 1997

Osteosarcoma is the most common primary bony malignancy and its survivorship has been progressed markedly through refined chemotherapy and surgery. But still there are many non-responders and analysis of prognostic factors may be helpful for them. Two hundred and sixty-six patients were enlisted between Mar, 1985 and Sep. 1994. Among them our inclusion criteria were: 1)primary, nonmetastatic classical osteosarcoma 2)extremity in location 3)no prior treatment at other institute and completed neoadjuvant chemotherapy and surgery according to our protocol. One hundred and eleven cases were eligible. Analyzed factors were:age, sex, location, tumor size, and pathologic response. Statistical methods were log-rank test for univariate and Cox's test for multivariate analysis. Male to female ratio was 69:42 with an average age of 17.2 years. Locations of tumor were distal femur 59, proximal tibia 29, and proximal humerus 8. Tumor size were measured by its maximal diameter and 48 cases were above 10cm and 47 cases were below 10cm. For pathologic response, 57 cases showed more than 90% and 54 cases were less than that. Limb salvage procedure was 101 cases and amputation was 10 cases and their local recurrence rate were 3.6%. Average follow-up period was 24(9-78.2) months and their final status was CDF 86, AWD 8, NED 5, and DOD 12 cases. In univariate study: type of operation(p=0.005), tumor size(p=0.005), and pathologic response(p=0.02) were significant variables. Pathologic response(p=0.03) and type of operation(p=0.01) were meaningful prognostic factors on multivariate analysis. But the latter result was interpreted as a bias, so pathologic response remained as a sole meaningful prognostic factor. More aggressive chemotherapy will be needed to improve the survival.

• Journal of the Korean Chemical Society
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• v.31 no.6
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• pp.567-575
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• 1987

Fourty five ancient Korean glass pieces have been determined for 19 elements such as Ag, As, Br, Ce, Co, Cr, Eu, Fe, Hf, K, La, Lu, Na, Ru, Sb, Sc, Sm, Th and Zn, and for one such as Pb by instrumental neutron activation analysis and by atomic absorption spectrometry, respectively. The multivariate data have been analyzed for the relation among elemental contents through the variance-covariance matrix. The data have been further analyzed by a principal component mapping method. As the results training set of 5 class have been chosen, based on the spread of sample points in an eigen vector plot and archaeological data. The 5 training set consisting of 36 species and a test set consisting of 9 species bave finally been analyzed for the assignment to certain classes or outliers through the statistical isolinear multiple component analysis (SIMCA). The results have showed the whole species for 5 training set and 3 species in the test set are assigned appropriately and these are in accord with the results by principal component mapping.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.592-599
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• 2015

In big data environment, we need new approach for big data analysis, because the characteristics of big data, such as volume, variety, and velocity, can analyze entire data for inferring population. But traditional methods of statistics were focused on small data called random sample extracted from population. So, the classical analyses based on statistics are not suitable to big data analysis. To solve this problem, we propose an approach to efficient big data analysis. In this paper, we consider a big data analysis using principal component analysis, which is popular method in multivariate statistics. To verify the performance of our research, we carry out diverse simulation studies.

• Journal of Korea Water Resources Association
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• v.42 no.2
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• pp.149-160
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• 2009

The objective of this study is to suggest parameter regionalization scheme which is integrated two multivariate statistical methods: principal components analysis(PCA) and hierarchical cluster analysis(HCA). This technique is to apply semi-distributed rainfall-runoff model on ungauged catchments. 7 catchment characteristics (area, mean altitude, mean slope, ratio of forest, water content at saturation, field capacity and wilting point) are estimated for 109 mid-sized sub-basins. The first two components from PCA results account for 82.11% of the total variance in the dataset. Component 1 is related to the location of the catchments relevant to the altitude and Component 2 is connected with the area of these. 103 ungauged catchments are clustered using HCA as the following 6 groups: Goesan 23, Andong 6, Imha 5, Hapcheon 21, Yongdam 4, Seomjin 44. SWAT model is used to simulate runoff and the parameters of the model on the 6 gauged basins are estimated. The model parameters were regionalized for Soyang, Chungju and Daecheong dam basins which are assumed as ungauged ones. The model efficiency coefficients of the simulated inflows for these three dams were at least 0.8. These results also mean that goodness of fit is high to the observed inflows. This research will contribute to estimate and analyze hydrologic components on the ungauged catchments.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.425-435
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• 2023

Organoleptic parameters such as color, odor, and flavor influence consumer perception of drinking water quality. This study aims to evaluate the taste of the selected bottled and tap water samples using an electronic tongue (E-tongue) instead of a sensory test. Bottled and tap water's mineral components are related to the overall preference for water taste. Contrary to the sensory test, the potentiometric E-tongue method presented in this study distinguishes taste by measuring the mineral components in water, and the data obtained can be statistically analyzed. Eleven bottled water products from various brands and one tap water from I city in Korea were evaluated. The E-tongue data were statistically analyzed using multivariate statistical tools such as hierarchical clustering analysis (HCA), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results show that the E-tongue method can clearly distinguish taste discrimination in drinking water differing in water quality based on the ion-related water quality parameters. The water quality parameters that affect taste discrimination were found to be total dissolved solids (TDS), sodium (Na+), calcium (Ca²⁺), magnesium (Mg²⁺), sulfate (SO₄^2-), chloride (Cl^-), potassium (K⁺) and pH. The distance calculation of HCA was used to quantify the differences between 12 different types of drinking water. The proposed E-tongue method is a practical tool to quantitatively evaluate the differences between samples in water quality items related to the ionic components. It can be helpful in quality control of drinking water.

• The Journal of Engineering Geology
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• v.23 no.4
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• pp.353-361
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• 2013

To analyze the distribution of earthquake-induced strain data in rock masses, statistical analysis was performed on four-directional strain data obtained from a ground movement monitoring system installed in Korea. Strain data related to the 2011 Tohoku-oki earthquake and two aftershocks of >M7.0 in 2011 were used in x-MR control chart analysis, a type of univariate statistical analysis that can detect an abnormal distribution. The analysis revealed different dispersion times for each measurement orientation. In a more comprehensive analysis, the strain data were re-evaluated using multivariate statistical analysis (MSA) considering correlations among the various data from the different measurement orientations. $T_2$ and Q-statistics, based on principal component analysis, were used to analyze the time-series strain data in real-time. The procedures were performed with 99.9%, 99.0%, and 95.0% control limits. It is possible to use the MSA data to successfully detect an abnormal distribution caused by earthquakes because the dispersion time using the 99.9% control limit is concurrent with or earlier than that from the x-MR analysis. In addition, the dispersion using the 99.0% and 95.0% control limits detected an abnormal distribution in advance. This finding indicates the potential use of MSA for recognizing abnormal distributions of strain data.

• Journal of Plant Biotechnology
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• v.33 no.4
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• pp.283-288
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• 2006

Discrimination of 5 rice cultivars (Sangjubyeo , Dongjinbyeo Simbaekbyeo , Hwamanbyeo , and Simbaek-hetero ) using metabolic profiling was carried out. Whole cell extracts from each cultivar were subjected to $^1H$ NMR spectroscopy. When spectral data were analyzed by principal component analysis, 5 cultivars were clustered into 3 groups: SJ, DJ + SB, and HM + SH. Thecultivars showed great difference in carbohydrate region of $^1H$ NMR spectra, suggesting that qualitative and quantitative differences in carbohydrate compounds play a major role in discrimination of the cultivars. In addition, it was readily possible to determine relative quantification of major carbohydrates including sucrose, glucose, maltose from spectral data of the cultivars. SJ showed 2 to 4 times higher content of maltose than the other rice cultivars. Overall results indicate that metabolic discrimination of rice cultivars using $^1H$ NMR spectroscopy combined by multivariate statistical analysis can be used for rapid discrimination of numerous rice cultivars and simple quantitative analysis system of major carbohydrate compounds in rice grains.

• Journal of the Society of Disaster Information
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• v.20 no.3
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• pp.519-532
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• 2024

Purpose: This study analyze the correlation between elections and wild Fire to provide information necessary for formulating wild fire prevention and response policies. Method: Data of the Forest Service and the Meteorological Administration were used to compare the occurrence and burned area of wild fires in election and non-election years. Statistical significance between the two groups was analyzed with an independent sample t-test, and MANOVA(multivariate analysis of variance) was used to evaluate the effects of temperature and humidity. Result: There was no statistical significance in the occurrence and burn area of wild fires between election and non-election years. However, analysis of the raw data indicated significantly greater damage in election years. MANOVA revealed that election status, temperature, and humidity did not significantly impact the occurrence and burn area of large wild fires. Conclusion: Wild fire occurrence and burned area were higher election years than non-election years, possibly due to election-related social factors. Thus, enhancing wild fire prevention and response policies in election years and considering weather factors and social activites is necessary.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.747-758
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• 2013

The covariance matrix is important in multivariate statistical analysis and a sample covariance matrix is used as an estimator of the covariance matrix. High dimensional data has a larger dimension than the sample size; therefore, the sample covariance matrix may not be suitable since it is known to perform poorly and event not invertible. A number of covariance matrix estimators have been recently proposed with three different approaches of shrinkage, thresholding, and modified Cholesky decomposition. We compare the performance of these newly proposed estimators in various situations.