Prediction of single-cell gene expression for transcription factor analysis.
Gigascience
; 9(11)2020 10 30.
Article
em En
| MEDLINE
| ID: mdl-33124660
ABSTRACT
BACKGROUND:
Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data.RESULTS:
Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature.CONCLUSION:
Our proposed method allows us to identify distinct TFs that show cell type-specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https//github.com/SchulzLab/Triangulate.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Fatores de Transcrição
/
Regulação da Expressão Gênica
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article