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Table 1 Definition of factors assessed in gene expression data analysis papers.

From: An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors

Factor

Brief definition or question of interest

Key references*

Sample size

Estimation of the number of arrays required in order to identify significantly, differentially expressed genes.

[15–26]

Statistical power

Ability of a study to detect a true difference between genes, biological category or condition

[2,24,27–28]

Normalisation

Does the paper report normalisation of data? (yes or no)

[29–32]

Normalisation method

Does the paper describe how sources of variation were removed or data standardisation method, e.g. total intensity normalisation, normalisation using regression techniques, normalisation using ratio statistics etc.

[29–32]

Test directionality

Explicit statement of directionality of the statistical test applied, i.e. one-sided or two-sided test

[33–35]

Hypothesis and alternative

Explicit statement of null (H0) or alternative hypothesis (H1)

[36–39]

Missing values

Report of missing values, report of estimation of missing values or description of method for estimating missing values.

[40–42]

Software

Which software, programs or tools were used for statistical analysis?

[43–44]

Analysis technique

Which statistical approaches were used for gene expression data analysis?

[1,45–47]

Homogeneity of variances

Does the paper report the equality of variances assumption for the application of ANOVA and t-test?

[48–49]

  1. * Review articles that may be useful to introduce the reader to these concepts and relevant approaches.