The gene expression profiles based different platform arrays from different stages of prostate cancer (i.e. disease stages I, II, II, IV) were collected from various laboratories.
Because the PM signals and MM signals were collected from different arrays, it is possible that the difference between the signals resulted in part from different hybridization conditions on the two types of array.
Microarray signals were rescaled to co-analyze data from different arrays by equating the average of the spike-specific signals from each microarray.
The F2_O, G2M, and G2F maps included different marker types: AFLP, single sequence repeat (SSR), expressed sequence tag (EST), and SNPs from different arrays (Chancerel et al. 2011, 2013), whereas the F2_N map contained only SNPs from the 9k SNP-array (Plomion et al. 2015).
Since we did not consider pairs consisting of microphones from different arrays, the resulting number of used pairs is.
Pairwise comparisons of DXZ4 monomer sequences revealed that repeat units from an array are more similar to one another than those originating from different arrays.
Normalization methods make measurements from different arrays comparable and multi-chip methods have proved to perform very well.
After pair-wise concordance check and further visual inspection, the genotype datasets from different arrays were merged.
This offers the possibility of using data from different arrays as confirmations of findings which may appear to be outliers.
When we matched genes from different arrays using the mouse UniGene IDs, we found that there were multiple gene IDs in an array corresponding to one UniGene ID.
