TY  - JOUR
TI  - Gene Networks Based on the Graphical Gaussian Model
AU  - Ma, Shisong 
VL  - 2
IS  - 4
PY  - 2012
DA  - 2012/02/20
SP  - e119
C1  - Bio-protocol 2012;2:e119
DO  - 10.21769/BioProtoc.119
UR  - https://doi.org/10.21769/BioProtoc.119
AB  - This protocol describes how to build a gene network based on the graphical Gaussian model (GGM) from large scale microarray data. GGM uses partial correlation coefficient (pcor) to infer co-expression relationship between genes. Compared to the traditional Pearson’ correlation coefficient, partial correlation is a better measurement of direct dependency between genes. However, to calculate pcor requires a large number of observations (microarray slides) greatly exceeding the number of variables (genes). This protocol uses a regularized method to circumvent this obstacle, and is capable of building a network for ~20,000 genes from ~2,000 microarray slides. For more details, see Ma et al.  (2007). For help regarding the script, please contact the author.
JF  - Bio-protocol
SN  - 2331-8325
PB  - Bio-protocol LLC.
BIO101  - False