TY - GEN
T1 - Large-scale dynamic gene regulatory networks analysis for time course DNA microarray data from C. Elegans, preliminary results and findings
AU - Zhang, L.
AU - Wu, H. C.
AU - Chan, S. C.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/9
Y1 - 2015/9/9
N2 - This paper presents preliminary results and findings of a dynamic gene regulatory networks analysis obtained from Caenorhabditis elegans (C. Elegans) time course DNA microarray data using a maximum a posteriori probability and time-varying autoregression model (MAP-TVAR) approach. High dimensionality and non-stationarity of the time course microarray data are two major challenges of time-varying GRNs analysis. The proposed method employs the L1-regularization based sparsity and continuity constraints, which facilitate the identification of sparse GRNs and reduce the estimation variance respectively. To process dataset which may contain extremely large amount of genes, the MAP-TVAR is extended to a distributed framework based on the concept similar to the spirit of Split Bregman method. Well-known interactions such as the eEF-1A.1 and RPL-12, can be identified by the MAP-TVAR approach. These interactions and their corresponding genes are found to be related in the embryo development process of C. Elegans. These suggest that the MAP-TVAR approach may serve as a wonderful tool for large-scale time-varying GRNs analysis using gene microarray data and other related datasets.
AB - This paper presents preliminary results and findings of a dynamic gene regulatory networks analysis obtained from Caenorhabditis elegans (C. Elegans) time course DNA microarray data using a maximum a posteriori probability and time-varying autoregression model (MAP-TVAR) approach. High dimensionality and non-stationarity of the time course microarray data are two major challenges of time-varying GRNs analysis. The proposed method employs the L1-regularization based sparsity and continuity constraints, which facilitate the identification of sparse GRNs and reduce the estimation variance respectively. To process dataset which may contain extremely large amount of genes, the MAP-TVAR is extended to a distributed framework based on the concept similar to the spirit of Split Bregman method. Well-known interactions such as the eEF-1A.1 and RPL-12, can be identified by the MAP-TVAR approach. These interactions and their corresponding genes are found to be related in the embryo development process of C. Elegans. These suggest that the MAP-TVAR approach may serve as a wonderful tool for large-scale time-varying GRNs analysis using gene microarray data and other related datasets.
KW - Caenorhabditis elegans
KW - Gene regulatory networks (GRNs)
KW - distributed computing
KW - large-scale DNA microarray dataset
KW - time course data analysis
UR - https://www.scopus.com/pages/publications/84961377898
U2 - 10.1109/ICDSP.2015.7251934
DO - 10.1109/ICDSP.2015.7251934
M3 - Conference contribution
AN - SCOPUS:84961377898
T3 - International Conference on Digital Signal Processing, DSP
SP - 552
EP - 556
BT - 2015 IEEE International Conference on Digital Signal Processing, DSP 2015
T2 - 2015 IEEE International Conference on Digital Signal Processing, DSP 2015
Y2 - 21 July 2015 through 24 July 2015
ER -