Inference of gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations
Ishikawa, M.; Sugino, S.; Masuda, Y.; Tarumoto, Y.; Seto, Y.; Taniyama, N.; Wagai, F.; Yamauchi, Y.; Kojima, Y.; Kiryu, H.; Yusa, K.; Eiraku, M.; Mochizuki, A.
Show abstract
Single-cell RNA-seq analysis coupled with CRISPR-based perturbation (scCRISPR) has enabled the inference of gene regulatory networks (GRNs) with causal relationships. However, a snapshot of scCRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a new computational method that infers GRNs using a time-series scCRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its GRN. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring GRNs. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a GRN consistent with multiple databases and literature. Accurate inference of GRNs by RENGE would enable the identification of key factors for various biological systems.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples 96%
- G2S3: a gene graph-based imputation method for single-cell RNA sequencing data 96%
- Cell-type annotation with accurate unseen cell-type identification using multiple references 95%
Similar papers in this journal
- scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data 96%
- PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data 96%
- Scalable identification of lineage-specific gene regulatory networks from metacells with NetID 96%
Similar papers in this journal
- DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data 97%
- SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection 96%
- CoRegNet: Unraveling Gene Co-regulation Networks from Public RNA-Seq Repositories Using a Beta-Binomial Statistical Model 96%