Functional annotation and network reconstruction through cross-platform integration of microarray data.
The rapid accumulation of microarray data translates into a need for methods to effectively integrate data generated with different platforms. Here we introduce an approach, 2(nd)-order expression analysis, that addresses this challenge by first extracting expression patterns as meta-information from each data set (1(st)-order expression analysis) and then analyzing them across multiple data sets. Using yeast as a model system, we demonstrate two distinct advantages of our approach: we can identify genes of the same function yet without coexpression patterns and we can elucidate the cooperativities between transcription factors for regulatory network reconstruction by overcoming a key obstacle, namely the quantification of activities of transcription factors. Experiments reported in the literature and performed in our lab support a significant number of our predictions.
Zhou
Xianghong Jasmine
XJ
Kao
Ming-Chih J
MC
Huang
Haiyan
H
Wong
Angela
A
Nunez-Iglesias
Juan
J
Primig
Michael
M
Aparicio
Oscar M
OM
Finch
Caleb E
CE
Morgan
Todd E
TE
Wong
Wing Hung
WH
D000465:Algorithms; D003198:Computer Simulation; D005656:Fungal Proteins / Q000235:genetics / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
D005656:Fungal Proteins; D014157:Transcription Factors