Article

Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer’s disease

2021-12-23

Abstract excerpt

Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on gene expression data collected from bulk tissues with different cell type compositions across samples. As a result, the co-expression estimates only offer an aggregate view of the underlying gene regulations and can be con...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 473ec305-1917-58b5-bcf7-f4035b57b066
DOI: 10.1101/2021.12.21.473558

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer’s diseaseDOI 10.1101/2021.12.21.473558Read

Cell-type-specific co-expression inference from single cell RNA-sequencing dataSemantic proximity Read

Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlasSemantic proximity Read

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression DataSemantic proximity Read

Co-expression networks reveal the tissue-specific regulation of transcription and splicingSemantic proximity Read

Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s DiseaseSemantic proximity Read

Shared regulation and functional relevance of local gene co-expression revealed by single cell analysisSemantic proximity Read

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.Semantic proximity Read

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma.Semantic proximity Read

Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer’s diseaseSemantic proximity Read

Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s DiseaseSemantic proximity Read

NEBULA: a fast negative binomial mixed model for differential expression and co-expression analyses of large-scale multi-subject single-cell dataSemantic proximity Read