Name
Title | ||
|---|---|---|
A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data. |
Abstract | ||
|---|---|---|
Motivation: Molecular profiling techniques have evolved to single-cell assays, where dense molecular profiles are screened simultaneously for each cell in a population. High-throughput single-cell experiments from a heterogeneous population of cells can be experimentally and computationally sorted as a sequence of samples pseudo-temporally ordered samples. The analysis of these datasets, comprising a large number of samples, has the potential to uncover the dynamics of the underlying regulatory programmes. Results: We present a novel approach for modelling and inferring gene regulatory networks from high-throughput time series and pseudo-temporally sorted single-cell data. Our method is based on a first-order autoregressive moving-average model and it infers the gene regulatory network within a variational Bayesian framework. We validate our method with synthetic data and we apply it to single cell qPCR and RNA-Seq data for mouse embryonic cells and hematopoietic cells in zebra fish. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/bioinformatics/btx605 | BIOINFORMATICS |
Field | DocType | Volume |
Time series,Population,Data mining,Autoregressive model,Profiling (computer programming),Computer science,Inference,Synthetic data,Gene regulatory network,Bayesian probability | Journal | 34 |
Issue | ISSN | Citations |
6 | 1367-4803 | 2 |
PageRank | References | Authors |
0.37 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Sanchez-Castillo | 1 | 11 | 2.54 |
| D. Blanco | 2 | 7 | 2.96 |
| Isabel M. Tienda-Luna | 3 | 27 | 5.28 |
| Maria Carmen Carrion Perez | 4 | 5 | 2.16 |
| Yufei Huang | 5 | 262 | 43.28 |