Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines
Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines
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加强跨学科数学和生物学教育：连接这些学科的微阵列数据分析课程
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Irene M. Evans
中科院分区：
文献类型：
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作者：
Y. Tra;Irene M. Evans
BIO2010提出了改善生物系学生数学教育背景的目标。微阵列高维数据的分析和解释可能非常具有挑战性，最好由统计学家和生物学家以合作的方式工作和教学。我们建立了这样的合作，并设计了一门关于微阵列数据分析的课程。我们开始使用基因组主动教学联盟(GCAT)材料和微阵列基因组和聚类工具软件，并在BioConductor软件包中添加了R统计软件。为了回应学生的反馈，一个微阵列数据集在课堂上进行了全面的分析，从预处理到基因发现，再到使用后一种软件进行路径分析。一个课堂项目是进行类似的分析，学生们分析自己的数据或从发表的期刊论文中获得的数据。这项工作展示了过滤、预处理和不同的归一化方法对最终数据集中的基因包含的影响。我们的结论是，这门课程达到了让学生掌握分析微阵列实验数据的技能的目标。我们提供了关于协作教学的见解，以及其他教员如何设计和实施类似的跨学科课程。
BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course.
影响因子：
7
作者：
Callow, MJ;Dudoit, S;Rubin, EM
通讯作者：
Rubin, EM
影响因子：
56.9
作者：
DeRisi, JL;Iyer, VR;Brown, PO
通讯作者：
Brown, PO