[BioC] two pair dye-swap (replicates) conducted in different labs

Gordon K Smyth smyth at wehi.EDU.AU
Sat Sep 24 11:35:18 CEST 2005


> Date: Mon, 19 Sep 2005 15:39:04 -0500
> From: kevin Lin <khlin at odin.mdacc.tmc.edu>
> Subject: [BioC] two pair dye-swap (replicates) conducted in different
> 	labs
> To: bioconductor at stat.math.ethz.ch
> Message-ID: <a06002001bf54d1fd7091@[143.111.133.119]>
> Content-Type: text/plain; charset="us-ascii" ; format="flowed"
>
> Dear BioC,
>
> I have technical replicates are in dye-swap pairs with four hybs
> conducted in house and four hybs conducted in commercial lab. Same
> RNA samples were used.
>
> Please see belows, I did separated analyses using Limma pipeline
> commands. My Qs is
>
> 1)  For IN HOUSE analysis, the corfit$consensus is supposed to be a
> negative number according to limma usersguide (the technical
> replicates are dye-swap and should vary in opposite directions). Why
> am I getting a positive number in the dye-swap design? Is there
> something wrong with my design matrix

Yes.  Your experiment has four distinct RNA sources, and at least two fold different changes to
estimate for each gene.  How do you expect to be able to do this with a design matrix with only
one column?

Gordon

> or I misunderstood points in
> the usersguide?
>
> 2) The result from IN HOUSE data seems to be nice, there are some DE
> genes in the listing. Instead, there is nothing differencially
> expressed genes for the data from OS LAB. So is it POSSIBLE?
> Assuming it is possible 'cause variations  which came from operations
> from different lab, what should I do from now on?
>
> 3) What do I expect to get if I pull those 8 arrays ( yet, I have not
> done it)  and do the analysis together? Even though, two results have
> so much difference in separate analyses (if all this based on correct
> analysis).
>
> I am confused.
>
> I appreciate if anyone could point me out.
>
> Kevin
>
> ***** commands and outputs below ******
>
> #####################################################################
> ## IN HOUSE: Two pairs of dye-swap experiments conducted in FG
> ## 1st, 2nd and 3rd, 4th are dye-swap pairs.
> #####################################################################
> library(limma)
> TargetsGenePix <- readTargets("FGtargets.txt")
>
>>  TargetsGenePix
>
>                                      SlideNumber    FileName
> Cy3                 Cy5
> FG WT rep1 Sep2005    3215L              FG WT rep1 Sep2005.gpr  WT
> control      WT UV
> FG WT rep2 Sep2005    3202OL           FG WT rep2 Sep2005.gpr   WT UV
> WT control
> FG KO rep2 Sep2005    3215OL            FG KO rep2 Sep2005.gpr   KO
> control      KO UV
> FG KO rep1 Sep2005    3202L              FG KO rep1 Sep2005.gpr   KO
> UV            KO control
>
> SpotTypes <- readSpotTypes()
> RGgpr <- read.maimages(TargetsGenePix$FileName,
> source="genepix",wt.fun=wtflags(w=0),annotation=c("Block","Row","Column","Name",
> "controltype"))
> RGgpr$printer <- getLayout(RGgpr$genes)
> RGgpr$genes$Status <- controlStatus(SpotTypes, RGgpr$genes)
> MAgpr <- normalizeWithinArrays(RGgpr,method="loess")
> design <- c(1,-1,1,-1)
> corfit <- duplicateCorrelation(MAgpr, design, ndups=1, block=c(1,1,2,2))
>
> ## > corfit$consensus
> ## [1] 0.3108369
>
> fit <- lmFit(MAgpr, design, block=c(1,1,2,2), correlation=corfit$consensus)
> fit <- eBayes(fit)
> top200 <- topTable(fit,n=200,adjust="fdr")
>
>        Block Row Column    Name controltype Status     M     A      t
> P.Value    B
> 7828     41  15      4  793067       false   cDNA  1.87  8.24  13.23
> 0.0341 4.80
> 2919     16   1      7 1228244       false   cDNA -1.89  9.80 -12.42
> 0.0341 4.45
> 5178     27  20      2  443884      ignore ignore  1.76 11.55  11.77
> 0.0341 4.15
> 2035     11  13      3 1265839       false   cDNA -2.52  8.32 -10.92
> 0.0413 3.73
> 8538     45   8      2  335555       false   cDNA  1.45 10.24  10.10
> 0.0440 3.27
> 10978    57  22      2  463982       false   cDNA  1.45 13.32   9.78
> 0.0440 3.08
> 834       5   7      2  335736       false   cDNA  1.46 12.89   9.53
> 0.0440 2.93
> 6258     33  11      2  334906       false   cDNA -1.34 10.56  -9.45
> 0.0440 2.87
> 11112    58  14      8  314112       false   cDNA  1.33 11.48   9.24
> 0.0440 2.74
> 6651     35  12      3 1264958       false   cDNA -1.99  8.27  -9.22
> 0.0440 2.73
> 490       3  12      2  334575       false   cDNA -1.33 10.15  -9.22
> 0.0440 2.73
> 567       3  21      7  480745      ignore ignore -1.48 11.24  -9.07
> 0.0446 2.63
> 9662     51   2      6 1111090       false   cDNA  1.31  6.09   8.77
> 0.0508 2.43
> 6394     34   4      2 1197400       false   cDNA -1.28  8.58  -8.66
> 0.0509 2.35
> 2407     13   9      7 1197119       false   cDNA -1.36 10.43  -8.33
> 0.0553 2.12
> 9303     49   7      7 1196439      ignore ignore -1.25 10.91  -8.25
> 0.0553 2.06
> 8365     44  10      5  440614       false   cDNA  1.34  7.89   8.23
> 0.0553 2.05
> 3547     19   8      3 1382084       false   cDNA  1.18  8.25   8.21
> 0.0553 2.03
> 1929     10  24      1  790765       false   cDNA -1.26  7.36  -7.98
> 0.0581 1.85
> 9803     51  20      3 1096050       false   cDNA  1.25 10.02   7.90
> 0.0581 1.80
>
> #############################################################################
> ## OS LAB: Two pairs of dye-swap experiments conducted
> #############################################################################
> TargetsGenePix <- readTargets("OStargets.txt")
>
>>TargetsGenePix
>
>                      SlideNumber               FileName        Cy3        Cy5
> OS WT rep1 Sep2005       2630L OS WT rep1 Sep2005.gpr WT control      WT UV
> OS WT rep2 Sep2005      2630OL OS WT rep2 Sep2005.gpr      WT UV WT control
> OS KO rep1 Sep2005      2631OL OS KO rep1 Sep2005.gpr      KO UV KO control
> OS KO rep2 Sep2005       2631L OS KO rep2 Sep2005.gpr KO control      KO UV
>
> snip...
>
> design <- c(1,-1,1,-1)
> corfit <- duplicateCorrelation(MAgpr, design, ndups=1, block=c(1,1,2,2))
> ## > corfit$consensus.correlation
> ## [1] -0.2315571
> fit <- lmFit(MAgpr, design, block=c(1,1,2,2), correlation=corfit$consensus)
> fit <- eBayes(fit)
> top200 <- topTable(fit,n=200,adjust="fdr")
>
>        Block Row Column    Name controltype Status      M    A     t
> P.Value     B
> 895       5  14      7 1023591       false   cDNA  1.378 5.45  4.67
> 1 -3.67
> 10580    55  20      4  750226       false   cDNA  1.091 6.03  4.49
> 1 -3.69
> 7293     38  20      5  944409       false   cDNA  1.039 6.39  4.44
> 1 -3.70
> 10117    53  10      5  336470       false   cDNA -1.046 8.07 -4.02
> 1 -3.77
> 3790     20  14      6  894195       false   cDNA -0.786 7.14 -3.90
> 1 -3.79
> 10077    53   5      5  718360       false   cDNA  2.410 5.63  7.22
> 1 -3.79
> 6469     34  13      5  573770       false   cDNA  0.982 6.86  3.74
> 1 -3.81
> 7476     39  19      4  765454       false   cDNA -1.191 6.15 -3.69
> 1 -3.82
> 1981     11   6      5  719193       false   cDNA -0.850 8.22 -3.69
> 1 -3.82
> 6796     36   6      4  905129       false   cDNA -0.748 6.25 -3.63
> 1 -3.84
> 7015     37   9      7 1197111       false   cDNA  0.748 7.90  3.61
> 1 -3.84
> 6361     33  24      1  538420       false   cDNA  1.122 6.14  3.55
> 1 -3.85
> 252       2   7      4  948928       false   cDNA -0.759 7.71 -3.54
> 1 -3.85
> 9467     50   3      3 1449999       false   cDNA  1.321 6.08  5.54
> 1 -3.85
> 2053     11  15      5  622182       false   cDNA  1.414 6.22  5.16
> 1 -3.88
> 10450    55   4      2  634988       false   cDNA  0.815 5.97  3.41
> 1 -3.88
> 940       5  20      4  762123       false   cDNA -0.699 6.28 -3.40
> 1 -3.88
> 9575     50  16      7 1020833       false   cDNA  0.752 7.69  3.39
> 1 -3.88
> 4335     23  10      7 1149905       false   cDNA  1.215 6.47  4.98
> 1 -3.89
> 3292     17  24      4  777258       false   cDNA  0.770 6.25  3.36
> 1 -3.89



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