MAGIC Tool: integrated microarray data analysis

Heyer, Laurie J.; Moskowitz, Dan; Abele, J. A.; Karnik, Pratima; Choi, Dong‐Kug; Campbell, A. Malcolm; Oldham, Ethan; Akin, B. K.

doi:10.1093/bioinformatics/bti247

Cited by 38 publications

(32 citation statements)

References 3 publications

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“…In these experiments students used two-color microarray analysis to compare gene expression between mutant and wild-type yeast strains, which forced them to consider experimental design and technical obstacles. Students also began their analysis with raw data (.tif files) from scans of the microarrays and conduct data analysis using MAGIC Tool [30], forcing them to consider many of the parameters and choices involved in this type of data analysis. They must make decisions about log transformation of data, background subtraction, and other issues that require quantitative analysis of how data manipulation influences results.…”

Section: Advanced Specialized Course (Genomes and Gene Evolution)mentioning

confidence: 99%

Integration of bioinformatics into an undergraduate biology curriculum and the impact on development of mathematical skills

Wightman

Hark

2012

Biochem Molecular Bio Educ

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The development of fields such as bioinformatics and genomics has created new challenges and opportunities for undergraduate biology curricula. Students preparing for careers in science, technology, and medicine need more intensive study of bioinformatics and more sophisticated training in the mathematics on which this field is based. In this study, we deliberately integrated bioinformatics instruction at multiple course levels into an existing biology curriculum. Students in an introductory biology course, intermediate lab courses, and advanced project-oriented courses all participated in new course components designed to sequentially introduce bioinformatics skills and knowledge, as well as computational approaches that are common to many bioinformatics applications. In each course, bioinformatics learning was embedded in an existing disciplinary instructional sequence, as opposed to having a single course where all bioinformatics learning occurs. We designed direct and indirect assessment tools to follow student progress through the course sequence. Our data show significant gains in both student confidence and ability in bioinformatics during individual courses and as course level increases. Despite evidence of substantial student learning in both bioinformatics and mathematics, students were skeptical about the link between learning bioinformatics and learning mathematics. While our approach resulted in substantial learning gains, student ''buy-in'' and engagement might be better in longer project-based activities that demand application of skills to research problems. Nevertheless, in situations where a concentrated focus on project-oriented bioinformatics is not possible or desirable, our approach of integrating multiple smaller components into an existing curriculum provides an alternative.

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Section: Advanced Specialized Course (Genomes and Gene Evolution)mentioning

confidence: 99%

Integration of bioinformatics into an undergraduate biology curriculum and the impact on development of mathematical skills

Wightman

Hark

2012

Biochem Molecular Bio Educ

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“…This helps in recording pathology data as well as core tracking and scoring. Heyer et al [12] have developed an integrated microarray data analysis tool called MAGIC which has been designed to explore and analyze all types of gene expression data.…”

Section: Introductionmentioning

confidence: 99%

Datamining Approach for Automation of Diagnosis of Breast Cancer in Immunohistochemically Stained Tissue Microarray Images

Prasad¹

2010

TOMINFOJ

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Abstract:Cancer of the breast is the second most common human neoplasm, accounting for approximately one quarter of all cancers in females after cervical carcinoma. Estrogen receptor (ER), Progesteron receptor and human epidermal growth factor receptor (HER-2/neu) expressions play an important role in diagnosis and prognosis of breast carcinoma. Tissue microarray (TMA) technique is a high throughput technique which provides a standardized set of images which are uniformly stained, facilitating effective automation of the evaluation of the specimen images. TMA technique is widely used to evaluate hormone expression for diagnosis of breast cancer. If one considers the time taken for each of the steps in the tissue microarray process workflow, it can be observed that the maximum amount of time is taken by the analysis step. Hence, automated analysis will significantly reduce the overall time required to complete the study. Many tools are available for automated digital acquisition of images of the spots from the microarray slide. Each of these images needs to be evaluated by a pathologist to assign a score based on the staining intensity to represent the hormone expression, to classify them into negative or positive cases. Our work aims to develop a system for automated evaluation of sets of images generated through tissue microarray technique, representing the ER expression images and HER-2/neu expression images. Our study is based on the Tissue Microarray Database portal of Stanford university at http://tma.stanford.edu/cgibin/cx?n=her1, which has made huge number of images available to researchers. We used 171 images corresponding to ER expression and 214 images corresponding to HER-2/neu expression of breast carcinoma. Out of the 171 images corresponding to ER expression, 104 were negative and 67 were representing positive cases. Out of the 214 images corresponding to HER-2/neu expression, 112 were negative and 102 were representing positive cases. Our method has 92.31% sensitivity and 93.18% specificity for ER expression image classification and 96.67% sensitivity and 88.24% specificity for HER-2/neu expression image classification.

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“…Another collaboration led to the development of open-source tools for microarray data analysis that could be used not only by workshop participants but also by anyone analyzing data generated by microarrays. Malcolm's collaborator at Davidson, Professor of Mathematics Laurie Heyer, and her students, developed open-source JAVA-based software (MAGIC Tool) by which the microarray-generated data could be analyzed (Heyer et al 2005).…”

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confidence: 99%

The 2013 Genetics Society of America Elizabeth W. Jones Award for Excellence in Education

Stasio¹

2013

Genetics

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MAGIC Tool: integrated microarray data analysis

Abstract: http://www.bio.davidson.edu/MAGIC

Cited by 38 publications

References 3 publications

Integration of bioinformatics into an undergraduate biology curriculum and the impact on development of mathematical skills

Integration of bioinformatics into an undergraduate biology curriculum and the impact on development of mathematical skills

Datamining Approach for Automation of Diagnosis of Breast Cancer in Immunohistochemically Stained Tissue Microarray Images

The 2013 Genetics Society of America Elizabeth W. Jones Award for Excellence in Education

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