MAGMA: Generalized Gene-Set Analysis of GWAS Data

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well.     

农业生态系统能量分析

农业生态系统的能量分析从能流着手进行农业生态系统的功能量化分析,是农业生态系统重要的研究方法之一。本文简要回顾讨论了农业生态系统能量分析研究的历史和进展。着重讨论了能值分析方法对传统能量分析方法的新发展,分析了农业生态系统能量分析目前存在的问题,并就其进一步发展方向进行了探讨能值分析方法用生产某种能量或物质所直接或间接耗用的太阳能值量来衡量该能量或物质的价值,不仅在概念上而且在算法上都是原有能量分析方法的新发展．发展到能值分析阶段的农业生态系统的能量分析方法亦存有自身的不足之处,如能值转换率的计算过于繁复,能值指标体系缺乏统一性及系统可持续发展的能值综合评价指标缺乏等。这些问题的解决与能值最大功率的明晰化、能值分析与能量及耗散分析的结合、与物质分析及景观结构分析的结合等研究等共同构成了农业生态系统能量分析的几大发展方向。     

Excel在药学数据分析工作中的应用

目的：采用常用的电子表格处理系统Microsoft Excel解决药学实验过程中遇到的数据分析问题。方法：应用工作表函数中内置的统计函数,以线性回归为例说明源数据的输入与结果返回的具体操作过程;对数据分析工具中的＂描述统计＂工具、t检验与方差分析,结合具体实例对药学实践中遇到的药学统计实际问题进行综合探讨。结果：用Excel表中内置的统计函数工具进行线性回归分析,方法简单、结果可靠;Excel表中的数据分析工具适用于日常药学实验数据分析过程中遇到的描述统计分析、t检验与方差分析。Excel与其它数据处理软件相比具有操作快捷、使用方便、计算精确、易于学习与掌握等优点。结论：Excel友好的界面,清晰的统计分析结果,使医药工作者在使用Excel的数据分析软件时会感到非常的方便快捷,灵活实用,值得在药学实践中应用推广。     

Comparison of methods for enumeration of interacting species in a biological system

Matrix rank analysis, a method extensively used to enumerate interacting species or components in various biological systems, has been compared with the more readily available technique of factor analysis. Data previously analyzed by matrix rank analysis has been subjected to factor analysis. The results show that there are some agreements and disagreements between the two methods. The sources and nature of the disagreements are discussed. Our work indicates that factor analysis may be preferable to matrix rank analysis.     

环境投入产出分析在产业生态学中的应用

综述了环境投入产出分析的基本知识及其在产业生态学领域的应用。环境投入产出分析的核心是投入产出模型,包括价值型投入产出模型、实物型投入产出模型和混合型投入产出模型。环境投入产出分析在产业生态学领域主要用于环境压力核算、生命周期评估、社会经济因素相对贡献分析、产业链路径分析、风险影响分析和环境网络分析。同时,相关学者进行环境投入产出数据库开发,给环境投入产出分析提供便捷、标准化的数据渠道。讨论了环境投入产出分析的若干发展趋势。     

群落二维格局分析的两种方法

群落二维空间格局研究能够更好地揭示群落的特征,但在分析方法上有较大的困难。用垂直相交的两条样带在两个方向上同时取样的二维取样法,获得数据,用DCA排序与格局分析相结合的方法,得到群落不同格局规模斑块的长、宽及面积,实现二维格局研究。在亚高山草甸群落格局研究中应用表明,两个方法均是有效的。     

文本分析技术在蛋白质生物信息学中应用的案例综述

海量数据时代考察文本分析技术在生物信息学领域的应用具有重要的理论和现实价值。本文讨论了文本分析在蛋白质计算分析中的几个应用实例以及核心技术内容。文本分析技术应用于生物信息学领域可发挥引领和导向作用,在生物信息学中的应用又进一步促进了文本分析技术的发展。文本分析技术虽然广泛在生物信息学中应用,但是其发展仍然有需要尚待解决的几个问题,本文对此也进行了讨论。     

谱分析在DNA序列分析中的应用

目的：谱分析是信号处理的常用方法,其中的统计相关分析、傅里叶变换、小波变换和数字滤波等手段已逐渐应用到DNA序列的分析中,这些应用包括DNA序列的周期性分析、基因识别和同源性分析等方面。本文对谱分析方法在DNA序列分析中的应用情况进行简单的综述。     

Exploring contingency tables with correspondence analysis

An algorithm for correspondence analysis is described and implementedin SAS/IML (SAS Institute, 1985a). The technique is shown, throughthe analysis of several biological examples, to supplement thelog-linear models approach to the analysis of contingency tables,both in the model identification and model interpretation stagesof analysis. A simple two-way contingency table of tumor datais analyzed using correspondence analysis. This example emphasisesthe relationships between the parameters of the log-linear modelfor the table and the graphical correspondence analysis results.The technqiue is also applied to a three-way table of surveydata concerning ulcer patients to demonstrate applications ofsimple correspondence analysis to higher dimensional tableswith fixed margins. Finally, the diets and foraging behaviorsof birds of the Hubbard Brook Forest are each analyzed and thena simultaneous display of the two separate but related tablesis constructed to highlight relationships between the tables. Received on August 29, 1988; accepted on April 25, 1989     

Semiautomatic microscopy as a tool for cytologists

Despite impressive advances in the application of computer image analysis to cytology, many of the identification tasks that cytologists are called on to perform remain refractory to automated image analysis. The major reason is that a large fraction of these images, though simple for a human to deal with, are too complex to yield to current image analysis methodologies. It may be years before automated computer image analysis is reduced to clinical practicality. Even then, it is not clear that all cytologic image analyses will prove amenable to automation. In the meantime, semiautomatic image analysis (computer-aided microscopy) can provide a viable alternative, especially to persistently difficult image analysis problems. In semiautomatic image analysis, the onerous tasks of data acquisition--e.g., stage movement, data entry and storage--are left to the computer, while the decision-making tasks-e.g., identifying a cell's morphologic class--are left to the observer. Such a system proves to be easy and flexible to use as well as economical to build. It can also provide a reliable data base for the later evaluation of fully automated systems as they are developed. One such semiautomatic system, the Image Combining Computer Microscope (ICCM), is described, and the range of its application is illustrated. Some of the examples of ICCM applications discussed are: neuronal cell plots, three-dimensional dendrite tracking, serial section reconstruction of axons and mapping of plaques and tangles in Alzheimer's disease. They illustrate how powerful a semiautomated system can be in handling complex image analysis problems. It is suggested that semiautomated image analysis provides a viable long-range alternative to many cytologic image analysis problems.     

Pathway-based Analysis Tools for Complex Diseases:A Review

Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehen- sive understanding of the molecular mechanisms underlying complex diseases. Extensive studies uti- lizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available path- way-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are dis- cussed. This review will provide a useful guide to dissect complex diseases.     

基于Linux的cDNA文库序列分析平台的构建与应用

本研究构建了基于Linux的cDNA文库序列分析平台,该分析平台可大批量自动处理测序后的序列,包括载体序列的去除、序列格式的转换、序列的自动拼接、序列对数据库的相似性搜索及全长ORF的预测等,可加速对大规模测序数据的分析和利用。用该平台对构建的野生大豆盐胁迫全长cDNA文库部分测序结果进行分析和利用。用该平台对构建的野生大豆盐胁迫全长cDNA文库部分测序结果进行分析,获得了较好的结果,已得到多个具有潜在价值的新基因序列。     

微生物生态研究中基于BIOLOG方法的数据分析

BIOLOG微平板法作为一种方便快速的微生物检验技术,已广泛应用于环境微生物检测,微生物生态研究等方面,发挥着越来越重要的作用。该方法可以获得关于微生物群落碳源利用能力的大量数据,反映出关于微生物活性的丰富信息。然而大量的数据也对解释和分析提出了挑战,分析了应用于BIOLOG产生数据的统计分析方法,对常用的AWCD值计算,多样性指数计算,主成分分析(PCA),聚类分析,相关、回归等方法深入探讨,阐述各自的功能、不足以及在应用中容易出现的问题。另外也对一些不常见的方法,如非参数多元分析(Non-Parametric version of MANOVA/Permutation version of MANOVA)、动力学参数分析、多元回归树、典范对应分析等也进行了讨论。通过对不同方法应用目标和原理的分析论述了各自优缺点,对微生物研究中基于BIOLOG方法数据分析的选择应用提供参考。     

Cell cycle-dependent AgNOR analysis in invasive breast cancer

OBJECTIVE: To investigate to what extent analysis of silver-stained nucleolar organizer regions (AgNORs) is cell cycle dependent in breast cancer and to assess the prognostic value of an AgNOR analysis that takes into consideration the cell cycle status of tumor cells. STUDY DESIGN: In 97 cases of invasive breast carcinoma, morphometric AgNOR analysis was performed in tumor cells with immunohistochemical MIB-1 reactivity (NORcyc analysis) and in MIB-1-negative tumor cells (NORnon analysis). Additionally, conventional (NORconv) analysis without preceding MIB-1 staining was done. Findings were compared with the Nottingham prognostic index (NPI). RESULTS: In comparison to noncycling tumor cells, cycling ones exhibited significantly higher AgNOR numbers (mean values, 3.84 +/- 1.09 vs. 2.40 +/- 0.78 per nucleus), higher total AgNOR areas (5.95 +/- 3.17 vs. 5.62 +/- 3.05 micron 2, NS) and significantly lower mean AgNOR areas (2.08 +/- 1.14 vs. 2.93 +/- 1.69 micron 2). When related to NPI, correlation coefficients of NORnon analysis were higher than those of NORcyc analysis but lower than those of NORconv analysis. Among the different AgNOR parameters, total AgNOR area correlated best with NPI. CONCLUSION: Cell cycle status has a high impact on AgNOR analysis. However, the best prognostic information in breast cancer is derived from an AgNOR analysis that considers both cycling and noncycling tumor cells.     

Fundamentals of cDNA microarray data analysis

Microarray technology is a powerful approach for genomics research. The multi-step, data-intensive nature of this technology has created an unprecedented informatics and analytical challenge. It is important to understand the crucial steps that can affect the outcome of the analysis. In this review, we provide an overview of the contemporary trend on various main analysis steps in the microarray data analysis process, which includes experimental design, data standardization, image acquisition and analysis, normalization, statistical significance inference, exploratory data analysis, class prediction and pathway analysis, as well as various considerations relevant to their implementation.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.