Frequently Asked Questions (FAQ)

System and data preparation Questions

How do I pre-process my gene lists for optimal results?

Each tool provides an info button next to the file upload area that displays a small example of the optimal list format, along with concise instructions. If you paste your lists directly, the placeholder text defines the required structure—each list separated by ###. You can also download example files for a deeper look at the format. The tools automatically remove duplicates and NAs, but you must still use the official gene nomenclature for your organism.

Can I save and load analysis sessions?

Session saving is not yet supported. Download your result tables and export plots when done. Future releases may include project-based session management.

Is there a limit on the number of gene lists or the size of each list?

There is no strict cap on how many gene lists you can upload or how many genes each list can contain—our example files range from 200 to 25,000 genes—bear in mind that processing time tends to increase once you work with more than around 30 lists.

What download formats are available for graphics and tables?

You can export graphics in PNG, JPG, or HTML. PNG offers transparent backgrounds for publications, JPG has a solid background, and HTML provides interactive plots. Tables are available in CSV or TSV, ready to use in tools like Excel, R, or Python.

How does the customization of graphics and tables work?

Each module includes a panel where you can filter table columns and adjust graph settings like colors, fonts, and size. In MetaEnrichGO, you can also select how many terms to display. All changes update instantly, and charts reflect filtered table content automatically.

Is the enrichment method used in both tools the same?

Both tools use the same annotation sources, but their analytical approaches differ. MetaRank generates a single consensus gene list based on ranking agreement across inputs, and performs enrichment on the top 100 genes. MetaEnrichGO, in contrast, analyzes all genes from all input lists and applies an additional meta-analysis step to combine p-values across results. As a result, MetaEnrichGO outputs include an appearance column indicating in how many input files each gene associated with a term was found, while MetaRank focuses on genes selected through consensus ranking.

Why are excluded genes included in the report?

To ensure transparency, excluded genes are listed separately with a brief explanation—typically due to low recurrence across lists—so users can understand why they weren’t part of the main analysis.

Why are the selected methods used in MetaRank?

These two methods were selected because they offer complementary approaches—one weighted (RankProd) and one unweighted (RobustRankAggreg)—while meeting key criteria: straightforward integration into the codebase, clear documentation, flexible parameter settings, efficient performance, and the ability to handle large datasets reliably.

Why does MetaRank automatically exclude genes that appear only once?

Genes present in a single list lack robust evidence and can bias the consensus ranking. Imputation (e.g., median) would assign identical scores to singletons, possibly elevating them unfairly. Exclusion yields a more reliable ranking.

What is the meta-analysis method used in MetaEnrichGO?

The meta-analysis method in MetaEnrichGO combines p-values from multiple enrichment analyses to provide a more robust evaluation of the biological term relevance. It uses a statistical technique to combine results from different gene lists, allowing the identification of common terms that are significantly enriched across all lists. This approach improves the accuracy of the analysis by reducing the bias that might arise from variability between different datasets.