Whole-transcriptome causal network inference with genomic and transcriptomic data
Whole-transcriptome causal network inference with genomic and transcriptomic data
AbstractReconstruction of causal gene networks can distinguish regulators from targets and reduce false positives by integrating genetic variations. Its recent developments in speed and accuracy have enabled whole-transcriptome causal network inference on a personal computer. Here we demonstrate this technique with program Findr on 3,000 genes from the Geuvadis dataset. Subsequent analysis reveals major hub genes in the reconstructed network.
- University of Bergen Norway
- University of Edinburgh United Kingdom
- Roslin Institute United Kingdom
Models, Genetic, Genome, Human, Gene Expression Profiling, Datasets as Topic, Genetic Variation, High-Throughput Nucleotide Sequencing, Genomics, Humans, Gene Regulatory Networks, Single-Cell Analysis, Transcriptome, Software
Models, Genetic, Genome, Human, Gene Expression Profiling, Datasets as Topic, Genetic Variation, High-Throughput Nucleotide Sequencing, Genomics, Humans, Gene Regulatory Networks, Single-Cell Analysis, Transcriptome, Software
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