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Different techniques for data normalization and analysis were tested.
Our methods for data normalization and analysis are based on the use of "internal standards" [17] that characterize some aspects of the system's behavior, such as technical variability, as presented elsewhere [18], [19].
While collectively these studies may be sufficient to identify useful signatures, combining data or the analytical results is difficult for many reasons, including the use of a variety of array platforms, different data normalization and analysis approaches, and variability in experimental protocols and patient selection.
for data normalization and analysis.
Data normalization and analysis were conducted as described earlier [ 81].
Data normalization and analysis were performed as described previously [ 3].
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All the micro-array experiments were performed in our laboratory with a normalized protocol of labeling, hybridization, data normalization and statistical analysis ensuring a perfect homogeneity of the data.
Of these, 18,653 yielded good quality spots, of which following normalization, 13,078 genes passed stable and uniform log normal distribution settings (see Methods for details on data normalization and statistical analysis).
Affymetrix GCOS software was used to do data normalization and comparative analysis.
Scanning, data normalization and statistical analysis was performed as described [ 14].
Data normalization and further analysis was carried out in GeneSpring GX 7.3 (Agilent Technologies, Palo Alto, CA, USA).
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