Categorical data analysis in experimental biology

Bo Xu; Xuyan Feng; Rebecca D. Burdine

doi:10.1016/j.ydbio.2010.08.018

Categorical data analysis in experimental biology

Bo Xu, Xuyan Feng, Rebecca D. Burdine

Molecular Biology

Research output: Contribution to journal › Review article › peer-review

26 Scopus citations

Abstract

The categorical data set is an important data class in experimental biology and contains data separable into several mutually exclusive categories. Unlike measurement of a continuous variable, categorical data cannot be analyzed with methods such as the Student's t-test. Thus, these data require a different method of analysis to aid in interpretation. In this article, we will review issues related to categorical data, such as how to plot them in a graph, how to integrate results from different experiments, how to calculate the error bar/region, and how to perform significance tests. In addition, we illustrate analysis of categorical data using experimental results from developmental biology and virology studies.

Original language	English (US)
Pages (from-to)	3-11
Number of pages	9
Journal	Developmental biology
Volume	348
Issue number	1
DOIs	https://doi.org/10.1016/j.ydbio.2010.08.018
State	Published - Dec 1 2010

All Science Journal Classification (ASJC) codes

Molecular Biology
Cell Biology
Developmental Biology

Keywords

Categorical data
Chi-square
Confidence interval
Statistical significance
Ternary diagram

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10.1016/j.ydbio.2010.08.018

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title = "Categorical data analysis in experimental biology",

abstract = "The categorical data set is an important data class in experimental biology and contains data separable into several mutually exclusive categories. Unlike measurement of a continuous variable, categorical data cannot be analyzed with methods such as the Student's t-test. Thus, these data require a different method of analysis to aid in interpretation. In this article, we will review issues related to categorical data, such as how to plot them in a graph, how to integrate results from different experiments, how to calculate the error bar/region, and how to perform significance tests. In addition, we illustrate analysis of categorical data using experimental results from developmental biology and virology studies.",

keywords = "Categorical data, Chi-square, Confidence interval, Statistical significance, Ternary diagram",

author = "Bo Xu and Xuyan Feng and Burdine, {Rebecca D.}",

note = "Funding Information: We thank Prof. Ned Wingreen, Prof. Carlos Brody, Prof. John Story, Dr. Monica Skoge, Liang Li and Andrew Miri for helpful discussions; Andrew Miri, Prof. Eric Wieschaus, and members of the Burdine lab for critical reading of the manuscript; and Drs. Arcady Mushegian and Hua Li for their helpful critique of the manuscript and suggestions related to R/Bioconductor. BX and RDB were funded in part by a grant from the NICHD , Grant R01-HD048584 .",

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doi = "10.1016/j.ydbio.2010.08.018",

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AU - Xu, Bo

AU - Feng, Xuyan

AU - Burdine, Rebecca D.

N1 - Funding Information: We thank Prof. Ned Wingreen, Prof. Carlos Brody, Prof. John Story, Dr. Monica Skoge, Liang Li and Andrew Miri for helpful discussions; Andrew Miri, Prof. Eric Wieschaus, and members of the Burdine lab for critical reading of the manuscript; and Drs. Arcady Mushegian and Hua Li for their helpful critique of the manuscript and suggestions related to R/Bioconductor. BX and RDB were funded in part by a grant from the NICHD , Grant R01-HD048584 .

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N2 - The categorical data set is an important data class in experimental biology and contains data separable into several mutually exclusive categories. Unlike measurement of a continuous variable, categorical data cannot be analyzed with methods such as the Student's t-test. Thus, these data require a different method of analysis to aid in interpretation. In this article, we will review issues related to categorical data, such as how to plot them in a graph, how to integrate results from different experiments, how to calculate the error bar/region, and how to perform significance tests. In addition, we illustrate analysis of categorical data using experimental results from developmental biology and virology studies.

AB - The categorical data set is an important data class in experimental biology and contains data separable into several mutually exclusive categories. Unlike measurement of a continuous variable, categorical data cannot be analyzed with methods such as the Student's t-test. Thus, these data require a different method of analysis to aid in interpretation. In this article, we will review issues related to categorical data, such as how to plot them in a graph, how to integrate results from different experiments, how to calculate the error bar/region, and how to perform significance tests. In addition, we illustrate analysis of categorical data using experimental results from developmental biology and virology studies.

KW - Categorical data

KW - Chi-square

KW - Confidence interval

KW - Statistical significance

KW - Ternary diagram

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Categorical data analysis in experimental biology

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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