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Pando leverages multi-modal single-cell measurements to infer gene regulatory networks using a flexible linear model-based framework. By modeling the relationship between TF-binding site pairs with the expression of target genes, Pando simultaneously infers gene modules and sets of regulatory regions for each transcription factor.
The fate and state of a cell is regulated through complex circuits of transcription factors (TFs) converging at regulatory elements to enable precise control of gene expression. Modern single-cell genomic approaches allow the simultaneous profiling of gene expression and chromatin accessibility in individual cells, which opens up new opportunities for the inference of cell regulomes. Pando jointly utilizes scRNA-seq and scATAC-seq data to infer regulatory relationships between TFs and target genes.
devtools::install_github('quadbio/Pando')
If you have aseurat_object
with transcriptomic and chromantin accessibility data, you can start right away with inferring the regulatory network:
# Load Packageslibrary(Pando)library(Seurat)library(BSgenome.Hsapiens.UCSC.hg38)# Get motif datadata(motifs)# Select variable featuresseurat_object<-Seurat::FindVariableFeatures(seurat_object,assay='RNA')# Initiate GRN object and select candidate regionsgrn_object<- initiate_grn(seurat_object)# Scan candidate regions for TF binding motifsgrn_object<- find_motifs(grn_object,pfm=motifs,genome=BSgenome.Hsapiens.UCSC.hg38)# Infer gene regulatory networkgrn_object<- infer_grn(grn_object)# Print inferred coefficientscoef(grn_object)# Find gene and regulatory modulestest_srt<- find_modules(grn_object)# Print modulesNetworkModules(test_srt)
More info about Pando can be found on ourwebsite. There you can find an API reference and a number of tutorial vignettes that give an introduction on how to use Pando most effectively.
If you find Pando helpful for your research, please consider citing
Fleck, J.S., Jansen, S.M.J., Wollny, D. et al. Inferring and perturbing cell fate regulomes in human brain organoids. Nature 621, 365–372 (2023).https://doi.org/10.1038/s41586-022-05279-8
About
Multiome GRN inference.