BioRxiv
bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
Reconstructing cellular differentiation networks and identifying cell fate-determining features with CIBER
Most existing trajectory inference methods are similarity-based and lack an understanding of the latent causality among differentiation processes. Here, we present CIBER, a Causal Inference-Based framework for the Evaluation of feature effects and the Reconstruction of cellular differentiation networks. CIBER is suitable for various types of omics data, including profilings of gene expression and chromatin accessibility. We show that CIBER can capture known cell-lineage structures and identify potential novo differentiation branches. By combining the CIBER-learned network with the structural causal model and applying in silico perturbation, we constructed an effect-matrix that quantifies the impacts of different features on each branch. We subsequently identified features important to the network structure through differentiation driver feature (DDF) analysis. We demonstrated that DDF analysis can identify features crucial to haematopoiesis but show no significant difference in expression between lineages. We further experimentally verified the effect of transcription factor Bcl11b on haematopoiesis, showing that it promotes lymphoid lineage differentiation while suppressing myeloid lineage differentiation, which is consistent with our predictions.
Xu, L., Cong, T., Xu, H., Yang, S., Sinha, R., Yamamoto, R., Zhang, W., Wang, J., Lan, X.
February 9, 2023
http://biorxiv.org/cgi/content/short/2023.02.08.527606v1?rss=1