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, 3:474 | Cite as

Rapid publication-ready MS-Word tables for one-way ANOVA

  • Houssein I AssaadEmail author
  • Lan Zhou
  • Raymond J Carroll
  • Guoyao WuEmail author
Software
Part of the following topical collections:
  1. Statistics
  2. Amino Acids

Abstract

Background

Statistical tables are an important component of data analysis and reports in biological sciences. However, the traditional manual processes for computation and presentation of statistically significant results using a letter-based algorithm are tedious and prone to errors.

Results

Based on the R language, we present two web-based software for individual and summary data, freely available online, at https://houssein-assaad.shinyapps.io/TableReport/ and https://houssein-assaad.shinyapps.io/SumAOV/, respectively. The software are capable of rapidly generating publication-ready tables containing one-way analysis of variance (ANOVA) results. No download is required. Additionally, the software can perform multiple comparisons of means using the Duncan, Student-Newman-Keuls, Tukey Kramer, and Fisher’s least significant difference (LSD) tests. If the LSD test is selected, multiple methods (e.g., Bonferroni and Holm) are available for adjusting p-values. Using the software, the procedures of ANOVA can be completed within seconds using a web-browser, preferably Mozilla Firefox or Google Chrome, and a few mouse clicks. Furthermore, the software can handle one-way ANOVA for summary data (i.e. sample size, mean, and SD or SEM per treatment group) with post-hoc multiple comparisons among treatment means. To our awareness, none of the currently available commercial (e.g., SPSS and SAS) or open-source software (e.g., R and Python) can perform such a rapid task without advanced knowledge of the corresponding programming language.

Conclusions

Our new and user-friendly software to perform statistical analysis and generate publication-ready MS-Word tables for one-way ANOVA are expected to facilitate research in agriculture, biomedicine, and other fields of life sciences.

Keywords

Statistical analysis Multiple comparisons Online software Computation Biology Shiny 

Abbreviations

ANOVA

Analysis of variance

IFNT

Interferon tau

SAS

Statistical analysis system

SD

Standard deviation

SEM

Standard error of the mean

ZDF

Zucker diabetic fatty.

Notes

Acknowledgments

Dr. H. Assaad and Dr. R.J. Carroll were supported by a postdoctoral training grant (R25T-CA090301) and a research grant (R37-CA057030) from the National Cancer Institute, respectively. Dr. G. Wu was supported by Agriculture and Food Research Initiative Competitive Grants No. 2008-35203-19120, 2011-67015-20028, and 2014-67015-21770 from the USDA National Institute of Food and Agriculture, as well as by a Hatch project from Texas A&M AgriLife Research (H-8200).

Supplementary material

40064_2014_1169_MOESM1_ESM.tif (1.7 mb)
Authors’ original file for figure 1
40064_2014_1169_MOESM2_ESM.tif (1.5 mb)
Authors’ original file for figure 2
40064_2014_1169_MOESM3_ESM.tif (1.4 mb)
Authors’ original file for figure 3

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Copyright information

© Assaad et al.; licensee Springer. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of StatisticsTexas A&M UniversityCollege StationUSA
  2. 2.Department of Animal ScienceTexas A&M UniversityCollege StationUSA
  3. 3.Department of Medical PhysiologyTexas A&M UniversityCollege StationUSA

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