A simulated gene expression dataset for differential expression analysis.
The format is: Formal class 'ExpressionSet' [package "Biobase"] with expression levels of 100 probes for 20 samples.
The phenotype data contain 2 phenotype variables: sid (subject id) and grp (group indicator: 1 stands for case; 0 stands for control).
The feature data contain 4 feature variables: probeid (probe id), gene (fake gene symbol), chr (fake chromosome number), and memProbes (probe significance indicator: 1 stands for probes over-expressed (OE) in cases; -1 stands for probes under-expressed (UE) in cases; and 0 stands for non-differentially expressed (NE) probes). There are 3 OE probes, 2 UE probes, and 95 NE probes.
The dataset was generated based on the R code in the manual
of the function
lmFit of the R Bioconductor package
There are 100 probes and 20 samples (10 controls and 10 cases). The first 3 probes are over-expressed in cases. The 4-th and 5-th probes are under-expressed in cases. The remaining 95 probes are non-differentially expressed between cases and controls. Expression levels for 100 probes were first generated from normal distribution with mean 0 and standard deviation varying between probes (sd=0.3*sqrt(4/chi^2_4)). For the 3 OE probes, we add 2 to the expression levels of the 10 cases. For the 2 UE probes, we subtract 2 from the expression levels of the 10 cases.
Please see the example in the manual for the function
lmFit in the R Bioconductor package
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data(esSim) print(esSim) ### dat=exprs(esSim) print(dim(dat)) print(dat[1:2,]) ### pDat=pData(esSim) print(dim(pDat)) print(pDat) # subject group status print(table(esSim$grp)) ### fDat = fData(esSim) print(dim(fDat)) print(fDat[1:2, ]) # probe's status of differential expression print(table(fDat$memProbes))
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