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Figure 1 | BMC Bioinformatics

Figure 1

From: Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

Figure 1

Results for mouse dataset. (A) Cross-validation results for the wild-type mouse time-course data. The RMSECV has the minimum value at number of PCs 2. So two PCs are used to model this dataset. (B) The PCs extracted in the wild-type mouse dataset. First PC shows the pattern related to activation of genes. The second PC has increased expression in the first time-point and then decreases. It corresponds to the dynamic changes in genes expression due to heat-shock. (C) The distribution of p-value of the genes in mouse dataset. There are 288 genes in the p-value range 0–0.01. After that the distribution if more or less uniform. The p-value threshold selected for this dataset is 0.01. (D) Difference of scores of mouse genes on first two PCs. The differentially expressed genes identified by the proposed method are marked '*'.

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BMC Bioinformatics

ISSN: 1471-2105

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