Fold change cut-off, usually applied to microarray data in case of insufficient number of replicates, leads to large number of false positive.
We also used the open R2 statistics application (microarray analysis and visualization platform; http://r2.amc.nl) using microarray data from 88 cases of NB-Versteeg-88 MAS5.0-u133p2 (http://hgserver1.amc.nl/cgi-bin/r2/main.cgi).
The reason for this could be the different nature of the data: High-throughput sequencing in the case of the within-species comparison (Schaefke et al. 2013) and microarray data in the case of the between-species comparison (Tirosh, Reikhav, et al. 2009).
In this paper we have presented a summary of the available microarray data from PD case-control studies and have suggested some potential strategies for uncovering primary pathogenic mechanisms.
In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies.
Finally, using differentially expressed genes found in lung cancer microarray data as a case study, we discovered some potential cancer genes for further experimental investigation.
In practice, it is difficult, if not impossible, to identify, by microarray data alone, which specific cases of apparent co-expression are solely due to cross-hybridization.
In some cases, microarray data were not available for one or both copies.
The qRT-PCR data agree well with microarray data for the three cases that were tested (see Methods and Additional file 4).
A further evolution can be seen in studies in which two omics data sources are simultaneously considered, in most cases microarray data combined with proteomics or array CGH data.
