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Microarray data contain more tissues and developmental stages as compared to MPSS data.
Co-expression network analysis is based on the idea that large collections of microarray data contain information about concerted changes in transcript levels.
Finally, it is worth mentioning that although public repositories of microarray data contain hundreds of normalized data sets, each data set having a hundred or so of microarray experiments concerning a single study, the different datasets cannot be combined easily.
Affymetrix microarray data contain intensity values and absent/present flags for each probe set.
Basically, microarray data contain 1-10% missing values that could affect up to 95% of genes [ 10].
Nonetheless, such methods are considered to be strict since the families of microarray data contain very large numbers of genes.
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The two-channel microarray data containing 8102 cDNA genes/clones generated by Sorlie et al. [2] were downloaded from the Stanford Microarray Database (SMD) (http://genome-www.stanford.edu/MicroArray/).edu/MicroArray/
The settings used for the searches were: gene: VAV1 or RASGRF2; dataset size: microarray data containing a minimum of 75 independent samples; microarray platform: unrestricted; gene rank: top 10%; P value≤0.001.
The settings for the searches were: gene: VAV1 or RASGRF2; cancer type: leukemia, lymphoma and myeloma; dataset size: microarray data containing a minimum of 75 independent samples; microarray platform: unrestricted; gene rank: top 10%; P value≤1×10 4; COPA value: less than –3 or more than 3 for the downregulated and upregulated outliers, respectively.
The microarray data containing balanced 2-fold differences in the levels of expression of 20% of the genes measured are provided as Supplemental material (Table S1) and may be used by readers for quality analysis estimation of their own analytical methods.
The resulting microarray data contained 16,015 remaining genes.
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