Article

scPrediXcan integrates advances in deep learning and single-cell data into a powerful cell-type–specific transcriptome-wide association study framework

2024-11-14

Abstract excerpt

Transcriptome-wide association studies (TWAS) help identify disease causing genes, but often fail to pinpoint disease mechanisms at the cellular level because of the limited sample sizes and sparsity of cell-type-specific expression data. Here we propose scPrediXcan which integrates state-of-the-art deep learning approaches that predict epigenetic features from DNA sequences with the canonical TWAS framework. Our...

Topics

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

Identifiers and source

Literature Corpus work: 81b61af2-0fd7-53f7-9218-481035271b62
DOI: 10.1101/2024.11.11.623049

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

scPrediXcan integrates advances in deep learning and single-cell data into a powerful cell-type–specific transcriptome-wide association study frameworkDOI 10.1101/2024.11.11.623049Read

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.Semantic proximity Read

Decoding sequence determinants of gene expression in diverse cellular and disease statesSemantic proximity Read

Harnessing machine learning models for epigenome to transcriptome association studiesSemantic proximity Read

High-resolution reconstruction of cell-type specific transcriptional regulatory processes from bulk sequencing samplesSemantic proximity Read

Pan-cell-type prediction of splicing patterns from sequence and splicing factor expressionSemantic proximity Read

Current genomic deep learning models display decreased performance in cell type specific accessible regionsSemantic proximity Read

Mapping enhancer-gene regulatory interactions from single-cell dataSemantic proximity Read

An analytical framework for decoding cell type-specific genetic variation of gene regulation.Semantic proximity Read

A sequence-based global map of regulatory activity for deciphering human geneticsSemantic proximity Read

Sequential regulatory activity prediction across chromosomes with convolutional neural networksSemantic proximity Read

CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq dataSemantic proximity Read

Sequential regulatory activity prediction across chromosomes with convolutional neural networks.Semantic proximity Read