arboreto

To infer gene regulatory networks from single-cell RNA-seq data, you can use GRNBoost2 or GENIE3 algorithms. You need to provide an expression matrix of observations by genes and optionally a list of transcription factor names to filter the network inference.

Yes, gene regulatory network inference can be scaled using distributed computing with Dask. This allows you to process large-scale bulk or single-cell RNA-seq datasets by moving from local multicore workstations to distributed clusters.

GRNBoost2 and GENIE3 are both algorithms used to infer gene regulatory networks from expression data. GRNBoost2 is the default method used for scalable network inference, while GENIE3 serves as an alternative algorithm option within the same pipeline.

Yes, you need a Python environment with Arboreto and its dependencies installed, including pandas. The environment must support Dask if you plan to use distributed computing for large-scale network inference.

Gene regulatory network inference integrates directly with pySCENIC workflows for regulon analysis. The inferred transcription factor-target relationships output by GRNBoost2 can be used as input for downstream regulon identification and analysis.

Inferring gene regulatory networks requires a matrix of expression data formatted as observations by genes. You can optionally provide a list of transcription factor names to include in the network inference process.