Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data

Lingfei Wang; Tom Michoel

doi:10.1007/978-1-4939-8882-2_4

Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data

Methods Mol Biol. 2019:1883:95-109. doi: 10.1007/978-1-4939-8882-2_4.

Authors

Lingfei Wang¹, Tom Michoel^{2

3}

Affiliations

¹ Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Midlothian, Scotland, UK. lingfei@broadinstitute.org.
² Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Midlothian, Scotland, UK.
³ Current address: Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.

PMID: 30547397
DOI: 10.1007/978-1-4939-8882-2_4

Abstract

Reconstruction 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 3000 genes from the Geuvadis dataset. Subsequent analysis reveals major hub genes in the reconstructed network.

Keywords: Causal gene network; Causal inference; Genome–transcriptome variation; Whole-transcriptome network.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Datasets as Topic
Gene Expression Profiling / instrumentation
Gene Expression Profiling / methods
Gene Regulatory Networks*
Genetic Variation
Genome, Human / genetics
Genomics / instrumentation
Genomics / methods*
High-Throughput Nucleotide Sequencing
Humans
Models, Genetic*
Single-Cell Analysis / methods
Software
Transcriptome / genetics*

Abstract

Publication types

MeSH terms

Grants and funding