Prediction of single-cell gene expression for transcription factor analysis.

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 me

SUBMITTER: Behjati Ardakani F 

PROVIDER: S-EPMC7596801 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature