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Methods & citation

What to write in your methods section – and whom to cite for the statistics and plot types you use.

Citing MES.Plot

Please mention MES.Plot with its version number and web address:

MES.Plot (version 1.0). https://mesplot.eu

Methods paragraph

A template to adapt – replace the parts in square brackets with your own settings. It is in English because most journals publish in English.

Pathway enrichment analysis was performed with Enrichr [1–3] using the [Reactome_Pathways_2024] library [4]. Genes were selected as [differentially expressed genes with adjusted P < 0.05 and |log2 fold change| > 1] and compared against [Enrichr's default background gene set]. Adjusted P-values were calculated by Enrichr using the Benjamini–Hochberg procedure [5]. The exported results table was checked for internal consistency and visualised with MES.Plot (version 1.0; https://mesplot.eu). The [ten] terms with the lowest adjusted P-values are shown as a [dot plot], with gene ratio defined as the number of overlapping genes divided by the pathway size, point size indicating the number of overlapping genes and colour indicating −log10(adjusted P).

References

Cite what you use. Each reference notes what it is for.

  1. Enrichr – enrichment analysis Chen EY, Tan CM, Kou Y, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128.
  2. Enrichr – enrichment analysis Kuleshov MV, Jones MR, Rouillard AD, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90–W97.
  3. Enrichr – enrichment analysis Xie Z, Bailey A, Kuleshov MV, et al. Gene set knowledge discovery with Enrichr. Curr Protoc. 2021;1(3):e90.
  4. Reactome – pathway library and hierarchy (treemap) Milacic M, Beavers D, Conley P, et al. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 2024;52(D1):D672–D678.
  5. Benjamini–Hochberg – adjusted P-values Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B. 1995;57(1):289–300.
  6. Enrichment map Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment Map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE. 2010;5(11):e13984.
  7. Dot plot, gene–pathway network and heatmap concepts Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. The Innovation. 2021;2(3):100141.
  8. UpSet plot Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H. UpSet: visualization of intersecting sets. IEEE Trans Vis Comput Graph. 2014;20(12):1983–1992.
  9. Network and treemap layout algorithms Fruchterman TMJ, Reingold EM. Graph drawing by force-directed placement. Softw Pract Exp. 1991;21(11):1129–1164.
  10. Network and treemap layout algorithms Bruls M, Huizing K, van Wijk JJ. Squarified treemaps. In: Data Visualization 2000. Springer; 2000:33–42.