Microarray data is noisy gsnapshoth of transcriptional state of cells. Detecting biological correlations among gene expression profiles from multiple laboratories on a large scale remains difficult.
Here, we applied a module (sets of genes working in the same biological pathway)-based correlation analysis in combination with a network analysis to Arabidopsis data and developed a grelation map,h which represents relationships among DNA microarray experiments on a large scale. In each experiment, the gene expression responses of modules are closely correlated with the status of specific biological actions. Therefore, one can assume that samples sharing a common response in a module also share a related biological action or response. According to following idea, we chose modules from each experiment and calculated correlations between gene expression profiles by using it.
A. Dataset structure
Table 1 Example of data matrix of microarray data.
|
|
Experiment A |
|
Experiment B |
|
Experiment C |
|
gene1 |
|
a** |
|
a* |
|
a |
|
gene2 |
|
b |
|
a |
|
c |
|
gene3 |
|
c |
|
c |
|
f |
|
gene4 |
|
d |
|
b |
|
d |
B. Calculation of correlation between experiments
Spearmanfs rank-order correlation coefficients (SCCs) to estimate relationships in gene expression profiles between experiments based on modules with bold characters.
Correlation A: From Experiment A to Experiment B
Experiment A (a, b, c) vs. Experiment B (a, a, c)
(Using a module of Experiment A)
Correlation B: From Experiment A to Experiment B
Experiment B (a, b) vs. Experiment A (b, d)
(Using a module of Experiment B)
C: Drawing relation maps
Combining results based on SCC.
see samples