arboreto

You infer gene regulatory networks from single-cell RNA-seq data by using scalable algorithms like GRNBoost2 and GENIE3 to derive transcription factor-target relationships and importance scores from your transcriptomic expression datasets.

Gene regulatory network inference scales for large datasets by supporting distributed computing, allowing you to run analyses across local multi-core machines or multi-node clusters using libraries like dask and distributed.

Constructing gene regulatory networks from bulk transcriptomics data is best handled by scalable algorithms like GRNBoost2 and GENIE3, which identify transcription factor-target relationships and calculate their importance scores efficiently.

You need pandas, dask, distributed, and scikit-learn to run gene regulatory network inference, as these libraries provide the necessary data structures and scalable machine learning framework for processing transcriptomic data.

Arboreto supports both single-cell and bulk transcriptomics analysis, providing scalable algorithms to construct gene regulatory networks and identify transcription factor-target relationships across different expression data formats.

GRNBoost2 and GENIE3 are the scalable algorithms available for identifying transcription factor-target relationships, deriving regulatory links and importance scores from your gene expression data.