Entering edit mode
Mary Kindall
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70
@mary-kindall-5600
Last seen 10.4 years ago
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?
--
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Mary Kindall
Yorktown Heights, NY
USA
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