Given a design matrix, how can I know that the logFC in the output of
ebayesFit is WT/KO  or KO/WT

    > Group
    [1] KO WT WT WT KO KO
    Levels: KO WT
    > design<- model.matrix(~Group)
    > design
      (Intercept) GroupWT
    1           1       0
    2           1       1
    3           1       1
    4           1       1
    5           1       0
    6           1       0
    attr(,"assign")
    [1] 0 1
    attr(,"contrasts")
    attr(,"contrasts")$Group
    [1] "contr.treatment"

    > fit <-lmFit(myEset,design)
    > fit<-eBayes(fit)
    > fit
    An object of class "MArrayLM"
    $coefficients
                 (Intercept)  GroupWT
    1415670_at          10.0 -0.11544
    1415671_at          10.9  0.41568
    1415672_at          11.6  0.00933
    1415673_at          10.4 -0.16966
    1415674_a_at        10.5 -0.01095
    18937 more rows ...

    $rank
    [1] 2

    $assign
    [1] 0 1

    $qr
    $qr
      (Intercept) GroupWT
    1      -2.449  -1.225
    2       0.408  -1.225
    3       0.408   0.527
    4       0.408   0.527
    5       0.408  -0.290
    6       0.408  -0.290
    attr(,"assign")
    [1] 0 1
    attr(,"contrasts")
    attr(,"contrasts")$Group
    [1] "contr.treatment"


    $qraux
    [1] 1.41 1.53

    $pivot
    [1] 1 2

    $tol
    [1] 1e-07

    $rank
    [1] 2


    $df.residual
    [1] 4 4 4 4 4
    18937 more elements ...

    $sigma
      1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at
          0.0736       0.1407       0.0627       0.3145       0.1100
    18937 more elements ...

    $cov.coefficients
                (Intercept) GroupWT
    (Intercept)       0.333  -0.333
    GroupWT          -0.333   0.667

    $stdev.unscaled
                 (Intercept) GroupWT
    1415670_at         0.577   0.816
    1415671_at         0.577   0.816
    1415672_at         0.577   0.816
    1415673_at         0.577   0.816
    1415674_a_at       0.577   0.816
    18937 more rows ...

    $pivot
    [1] 1 2

    $Amean
      1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at
            9.95        11.12        11.57        10.34        10.53
    18937 more elements ...

    $method
    [1] "ls"

    $design
      (Intercept) GroupWT
    1           1       0
    2           1       1
    3           1       1
    4           1       1
    5           1       0
    6           1       0
    attr(,"assign")
    [1] 0 1
    attr(,"contrasts")
    attr(,"contrasts")$Group
    [1] "contr.treatment"


    $df.prior
    [1] 2.41

    $s2.prior
    [1] 0.0378

    $var.prior
    [1] 422.8  49.9

    $proportion
    [1] 0.01

    $s2.post
      1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at
          0.0176       0.0266       0.0167       0.0759       0.0218
    18937 more elements ...

    $t
                 (Intercept) GroupWT
    1415670_at         130.6 -1.0653
    1415671_at         115.9  3.1225
    1415672_at         155.0  0.0885
    1415673_at          65.5 -0.7540
    1415674_a_at       123.6 -0.0909
    18937 more rows ...

    $df.total
    [1] 6.41 6.41 6.41 6.41 6.41
    18937 more elements ...

    $p.value
                 (Intercept) GroupWT
    1415670_at      3.16e-12  0.3252
    1415671_at      6.78e-12  0.0188
    1415672_at      1.05e-12  0.9322
    1415673_at      2.62e-10  0.4776
    1415674_a_at    4.49e-12  0.9304
    18937 more rows ...

    $lods
                 (Intercept) GroupWT
    1415670_at          16.9   -6.16
    1415671_at          16.6   -3.41
    1415672_at          17.2   -6.75
    1415673_at          14.4   -6.45
    1415674_a_at        16.7   -6.75
    18937 more rows ...

    $F
    [1] 16860 13961 24050  4225 15270
    18937 more elements ...

    $F.p.value
    [1] 1.17e-12 2.15e-12 3.75e-13 9.89e-11 1.61e-12
    18937 more elements ...

    > x <- topTable(fit,n=nrow(exprs(myEset)),adjust="BH",coef=2)
    > head(x)
                 logFC AveExpr    t  P.Value adj.P.Val    B
    1452666_a_at  5.36    4.93 45.0 2.92e-09  5.54e-05 8.50
    1441054_at    4.82    4.67 33.0 2.12e-08  2.01e-04 7.94
    1424722_at    4.53    4.75 28.0 5.95e-08  3.62e-04 7.53
    1435378_at    3.91    4.59 26.5 8.52e-08  3.62e-04 7.37
    1428391_at    4.78    4.97 26.0 9.54e-08  3.62e-04 7.31
    1419311_at    4.79    5.94 21.8 2.98e-07  9.42e-04 6.71



Is all these genes here upregulated as compared to WT?

-- 
-------------
Mary Kindall
Yorktown Heights, NY
USA

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