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. 2021 Jan 14;12(1):84. doi: 10.1038/s41419-020-03356-2

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Schematic overview of the CenTFinder workflow for identification of TFs (central in differentiation and specification). Left panel: data acquisition and pre-processing. The analysis starts by defining queries that are used for filtering and retrieving relevant expression data from the ArrayExpress database. Retrieved microarray data (of different formats) are combined and normalised and batch corrections are applied. Alternatively, a pre-processed expression matrix can be provided as input. Middle panel: core analyses. The expression matrix is subsequently subjected to gene co-expression analysis (WGCNA), TF binding motif enrichment analysis (RcisTarget), and the results are overlapped with differential expression analysis. Right panel: integration and TF ranking. The last step in the workflow is to combine the output of WGCNA, RcisTarget, and differential expression analyses to prioritise and rank TFs. Additional downstream analyses, e.g., Gene Ontology enrichment, and Cytoscape network visualisations are performed at this step.