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Series GSE210747 Query DataSets for GSE210747
Status Public on Apr 18, 2023
Title SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics (Cortex)
Organism Mus musculus
Experiment type Expression profiling by high throughput sequencing
Genome binding/occupancy profiling by high throughput sequencing
Summary Joint profiling of chromatin accessibility and gene expression of individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (eGRN). Here we present a new method for the inference of eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression. To improve both recall and precision of TF identification, we curated and clustered more than 40,000 position weight matrices that we could associate with ~1,500 human TFs. We validated and benchmarked each of the SCENIC+ components on diverse data sets from different species, including human peripheral blood mononuclear cell types, human ENCODE cell lines, human 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 provide new capabilities that exploit the inferred eGRNs to study the dynamics of gene regulation along differentiation trajectories; to map regulatory activities onto tissues using spatial omics data; and to predict the effect of TF perturbations on cell state. SCENIC+ provides critical insight into gene regulation, starting from multi-ome atlases of scATAC-seq and scRNA-seq. The SCENIC+ suite is available as a set of Python modules at scenicplus.readthedocs.io.
 
Overall design 10x single cell multiome on the mouse cortex
 
Contributor(s) González-Blas CB, De Winter S, Aerts S
Citation(s) 37443338
Submission date Aug 08, 2022
Last update date Jul 18, 2023
Contact name Gert Hulselmans
E-mail(s) [email protected]
Organization name VIB
Department Center for Brain and Disease Research
Lab Laboratory of Computational Biology
Street address Herestraat 49 PO Box 602
City Leuven
ZIP/Postal code 3000
Country Belgium
 
Platforms (1)
GPL24247 Illumina NovaSeq 6000 (Mus musculus)
Samples (10)
GSM6436470 10x multiome (ATAC) on the mouse cortex using the '10x complex UC' protocol
GSM6436471 10x multiome (RNA) on the mouse cortex using the '10x complex UC' protocol
GSM6436472 10x multiome (ATAC) on the mouse cortex using the '10x complex' protocol
This SubSeries is part of SuperSeries:
GSE210749 SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics
Relations
BioProject PRJNA867356

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE210747_10x_complex_atac_fragments.tsv.gz 1.8 Gb (ftp)(http) TSV
GSE210747_10x_no_perm_atac_fragments.tsv.gz 4.5 Gb (ftp)(http) TSV
GSE210747_Mouse_cortex_DGEM.tsv.gz 46.3 Mb (ftp)(http) TSV
GSE210747_TST_NP40_004_atac_fragments.tsv.gz 1.8 Gb (ftp)(http) TSV
GSE210747_TST_atac_fragments.tsv.gz 732.6 Mb (ftp)(http) TSV
GSE210747_scATACseq_Mouse_cortex_fragment_counts.tsv.gz 377.5 Mb (ftp)(http) TSV
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Raw data are available in SRA
Processed data are available on Series record

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