RNA A260/A280 ratios are reported and are above 1.8 to indicate sample purity, or are consistent across samples
The integrity of RNA was assessed (common strategies include an RNA integrity number (RIN), an RNA quality indicator (RQI) or 28s:18s ratio) to ensure minimal RNA degradation or consistency across samples
When multiple microarrays are necessary and the experiment was run over different days, the samples were randomized across the slides/days to avoid confounding effects (often referred to as a block design) Note: not always specified in the methods
Generally, gene annotation and data quality are more robust when commercially produced microarray platforms are used
Species appropriate microarrays were used (i.e., mouse arrays for mouse samples)
Labeling and hybridization were done according to manufacturer protocol. Any deviations are reported
When co-hybridizations of treated and control samples are done (use of different fluorophores for control and treated samples), dye-swapping experiments were done, or there is an indication that dye bias was assessed statistically
Scanner specific quality control software was used to test microarray quality
Data quality was assessed (through MA plots, heat maps, boxplots, scatterplots, signal to noise ratio, etc.)
In the case that outliers are identified, there is a minimum of three replicates remaining per group and a justification for removal has been provided
The data were preprocessed (e.g., background subtracted and log transformed) and normalized (i.e., adjusted to remove technical variations between arrays) prior to statistical analysis*
An appropriate statistical analysis of data was conducted to identify differentially expressed genes*
Data were adjusted to account for false positives (most commonly referred to as false discovery rate (FDR) adjustment or Benjamini–Hochberg method). Note: this is not always done, though a lower p-value may be set to minimize false positives, with more focus given to pathway/functional effects over individual genes
Software versions, parameters, and gene annotation/references versions and builds were recorded
The number of genes considered significant was restricted based on fold-change induction (typically not less than 1.5-fold)
Gene significance was restricted on the basis of some measure of statistical significance (not more than 10%)
Validation of results was assessed using one of the following approaches: (1)the predicted biological effect was verified directly in the test animals or through the literature; (2) changes in important genes were confirmed using an alternative method such as real-time quantitative PCR; (3) other measures were carried out to confirm the predicted response
Data files were made available through an open access public database such as Gene Expression Omnibus (GEO), Chemical Effects in Biological Systems (CEBS) or ArrayExpress)
