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BiodataAnalysisGroup / BioHackathon

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script SCENIC+ #2

Open theinvisibleliya opened 1 month ago

theinvisibleliya commented 1 month ago

Paper: https://www.nature.com/articles/s41592-023-01938-4 Abstract: Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. We benchmarked SCENIC+ on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, melanoma cell states and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io.

Code availability: pycisTopic is available at https://github.com/aertslab/pycisTopic and deposited in Zenodo at https://doi.org/10.5281/zenodo.7857024. pycisTarget is available at https://github.com/aertslab/pycistarget and deposited in Zenodo at https://doi.org/10.5281/zenodo.7857022. SCENIC+ is available at https://github.com/aertslab/scenicplus and deposited in Zenodo at https://doi.org/10.5281/zenodo.7857017. Detailed tutorials and documentation on the SCENIC+ workflow are available at scenicplus.readthedocs.io and tutorials on pycisTopic and pycisTarget (within the SCENIC+ workflow and as standalone packages) are available at pycisTopic.readthedocs.io and pycistarget.readthedocs.io, respectively. Code to generate custom cisTarget databases is available at https://github.com/aertslab/create_cisTarget_databases. Our implementation of Cluster-Buster is available at https://github.com/ghuls/cluster-buster/tree/change_f4_output. Notebooks to reproduce the analyses presented in this manuscript are available at https://github.com/aertslab/scenicplus_analyses.

What can it do? (+) Digital KOs (+) Elucidation of true perturbation effects

Limitations: (-) Enhancer-promoter distance relationship unclear (-) eGRNs have limited predictive power (-) if the sc data is not human, mouse or fly; it is not actually possible to run the pipeline

marinaEM commented 3 weeks ago

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