Closed:Limma outputs different results for same sample?

Question

[DareDevil]

in the limma user manual, page 41-43 it provides two ways of calculation of DEGs

Approach1

#Differentially expressed genes can be found by
> fit <- lmFit(eset, design)
> fit <- eBayes(fit)
> topTable(fit, coef="Grp1vsGrp2", adjust="BH")

Approach 2;

#Differentially expressed genes can be found by
> fit <- lmFit(eset, design)
> cont.matrix <- makeContrasts(Grp1vsGrp2=Grp1 - Grp2, levels=design)
> fit2 <- contrasts.fit(fit, cont.matrix)
> fit2 <- eBayes(fit2)
> topTable(fit2, adjust="BH")

But I get different output for these methods:

Approcah1: output

                 ID     logFC   AveExpr         t      P.Value    adj.P.Val          B
1   GE_BrightCorner 14.034775 14.142391 48.329231 1.062840e-06 5.771813e-06  5.9423764
2        DarkCorner  3.526284  3.681083 11.510530 3.202467e-04 4.538190e-04  1.0973086
3        DarkCorner  4.438930  4.005311 24.177036 1.695687e-05 4.254662e-05  4.0948249
4      A_23_P117082 12.930345 12.460447 67.487497 2.788662e-07 4.420224e-06  6.4346613
5     A_33_P3246448  4.475369  4.365248 24.873738 1.514011e-05 3.896822e-05  4.1925378
6     A_33_P3318220  4.395836  4.075980 22.628496 2.207786e-05 5.232768e-05  3.8608390
7     A_33_P3236322  4.103927  4.176582 23.902379 1.774740e-05 4.407702e-05  4.0550613

Approach 2: output

               ID       logFC   AveExpr           t     P.Value adj.P.Val         B
1   GE_BrightCorner -0.21523164 14.142391 -0.52407752 0.627849111 0.8757925 -6.501968
2        DarkCorner -0.30959707  3.681083 -0.71459481 0.514243534 0.8245442 -6.370012
3        DarkCorner  0.86723802  4.005311  3.34001263 0.028713468 0.3393645 -3.441232
4      A_23_P117082  0.93979506 12.460447  3.46841753 0.025505731 0.3271261 -3.309127
5     A_33_P3246448  0.22024196  4.365248  0.86556019 0.435430560 0.7847096 -6.243565
6     A_33_P3318220  0.63971250  4.075980  2.32854199 0.080197184 0.4662019 -4.575855

Expect AveExpr all other parameters are different