I think that once you can figure out how data connect in one or two datasets, it starts to make more sense overall.
SAMMD users can compare two or three datasets using the "Compare" menu option.
Especially the classifiers from five or six datasets select more than 100 genes.
In practice, the authors have taken the habit of running their methods on two or three datasets, verifying trend agreement.
Obviously, unique estimation can also be achieved when a combination of the 3 output equations are used (i.e., simultaneously fitting the model to two or three datasets from [ 55] and [ 61]), as was performed in [ 49].
For regions with ≥ 50 informative SNPs, 2% of the regions were unique to a single dataset, while 20%, 30%, and 48% overlapped regions called in one, two, or three other datasets.
For abnormal regions with ≥ 10 informative SNPs, 6% of the regions were unique to a single dataset, while 86%, 6%, and 2% overlapped regions called in one, two, or three other datasets.
T, P, or M linked with hyphens stands for two or three omics dataset are provided by users.
For two or three ChIP-chip datasets, a plot of overlapping comparison between datasets at different threshold levels is also provided.
As many STs were found rarely in only one or two of the datasets, only those STs with an average frequency of>1% (over the four datasets) were compared.
