Use Case 4: Identify the Tissue Activity for a TF based on its Targets

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The workflow starts with selecting KLF4 as the search term. Gene sets with set labels containing KLF4 were queried from Enrichr[1]. Identified matching terms from the ENCODE TF ChIP-seq 2015[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for ENCODE_TF_ChIP-seq_2015. Identified matching terms from the ChEA 2022[4] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for ChEA_2022. Identified matching terms from the ARCHS4 TF Co-Expression[5] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for ARCHS4_TFs_Coexp. Multiple GMTs were combined into one GMT. A consensus gene set was created by only retaining genes that appear in at least two sets. The gene set was submitted to Enrichr[1]. The gene set was enriched against the GTEx Tissues V8 2023[6] library to identify statistically significant GTEx Tissue Signatures.

1. Xie, Z. et al. Gene Set Knowledge Discovery with Enrichr. Current Protocols vol. 1 (2021). doi:10.1002/cpz1.90
2. An integrated encyclopedia of DNA elements in the human genome. Nature vol. 489 57–74 (2012). doi:10.1038/nature11247
3. Keenan, A. B. et al. ChEA3: transcription factor enrichment analysis by orthogonal omics integration. Nucleic Acids Research vol. 47 W212–W224 (2019). doi:10.1093/nar/gkz446
4. Lachmann, A. et al. Massive mining of publicly available RNA-seq data from human and mouse. Nature Communications vol. 9 (2018). doi:10.1038/s41467-018-03751-6
5. Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nature Genetics vol. 45 580–585 (2013). doi:10.1038/ng.2653