Hi Franklin,

Replies below,


On 28 February 2013 20:36, Franklin Johnson [guest]
<guest@bioconductor.org>wrote:

>
> Dear Maintainer,
>
> I have created my.qPCRBatch object read into R using the ReadqPCR package:
> > rownames(exprs(qPCRBatch))
>  [1] "actin_14d_TechReps.1" "actin_14d_TechReps.2" "actin_1d_TechReps.1"
>  "actin_1d_TechReps.2"  "actin_3d_TechReps.1"  "actin_3d_TechReps.2"
>  [7] "actin_7d_TechReps.1"  "actin_7d_TechReps.2"  "lox22_14d_TechReps.1"
> "lox22_14d_TechReps.2" "lox22_1d_TechReps.1"  "lox22_1d_TechReps.2"
> [13] "lox22_3d_TechReps.1"  "lox22_3d_TechReps.2"  "lox22_7d_TechReps.1"
>  "lox22_7d_TechReps.2"
> > combinedTechReps=combineTechReps(qPCRBatch)
> > combinedTechReps
> qPCRBatch (storageMode: lockedEnvironment)
> assayData: 8 features, 2 samples
>   element names: exprs
> protocolData: none
> phenoData
>   sampleNames: MeJa Triton.X
>   varLabels: sample
>   varMetadata: labelDescription
> featureData: none
> experimentData: use 'experimentData(object)'
> Annotation:
>
> I have also generated the deltaCq values using:
> > hkgs="actin_1d"
> > qPCRBatch.norm=deltaCq(combinedTechReps, hkgs=hkgs, calc="arith")
> > head(exprs(qPCRBatch.norm))
>             MeJa Triton.X
> actin_14d  0.655    0.260
> actin_1d   0.000    0.000
> actin_3d  -0.120    0.095
> actin_7d   0.465    0.145
> lox22_14d  2.755    3.530
> lox22_1d   0.735    2.740
>
> Now, to advance to Section 5.3, do I need to use qPCRBatch.norm. Or, did R
> populate qPCRBatch object with this deltaCq data? In other words, for
> Section 5.3, does the deltaDeltaCq function calculate deltaDeltaCq using
> deltaCq obtained in 5.1, or does it calculate deltaDeltaCq from the raw Cq
> data?
>


>From the raw data


>
> However, I get error message when advancing from Section 5.1 to 5.3 using
> qPCRBatch.norm:
> > contM<-cbind(c(0,0,0,0,1,1,1,1), c(1,1,1,1,0,0,0,0))
> > colnames(contM)=c("MeJa", "Triton.X")
> > rownames(contM)=rownames(exprs(qPCRBatch.norm))
> > contM
>           MeJa Triton.X
> actin_14d    0        1
> actin_1d     0        1
> actin_3d     0        1
> actin_7d     0        1
> lox22_14d    1        0
> lox22_1d     1        0
> lox22_3d     1        0
> lox22_7d     1        0
> > hkgs<-"actin_1d"
> > ddCq.norm=deltaDeltaCq(qPCRBatch.norm, maxNACase=0, maxNAControl=0,
> hkgs=hkgs, contrastM=contM, case="MeJa", control="Triton.X",
> statCalc="geom")
> Error in .local(qPCRBatch, ...) : subscript out of bounds
>
> I tried many attempts to alter the subscripts to no success. So, I
> attempted to use the vignette to see how to work with the data. However, I
> got the same result:
> > head(exprs(qPCRBatch.norm))
>                      fp1.day3.v fp2.day3.v fp5.day3.mia fp6.day3.mia
> fp.3.day.3.v fp.4.day.3.v fp.7.day.3.mia fp.8.day.3.mia
> Actb.Rn00667869_m1     0.000000   0.000000     0.000000     0.000000
> 0.000000     0.000000       0.000000       0.000000
> Adipoq.Rn00595250_m1   0.016052  -0.116520     2.933523     2.540987
>  -0.178971    -0.563263       2.458509       2.736475
> Adrbk1.Rn00562822_m1         NA         NA     6.566628     6.642561
>     NA           NA       3.737100       6.873568
> Agtrl1.Rn00580252_s1   4.899380   5.035841     6.397364     5.680837
> 5.220796     4.425364       4.794776       5.345202
> Alpl.Rn00564931_m1    12.531942  11.808657    13.035166    12.239549
>  12.394802    11.772896      12.110000      12.255186
> B2m.Rn00560865_m1      0.741558   0.890717     2.040470     2.234605
> 0.505516     0.877598       1.927563       1.903269
> > contM<-cbind(c(0,0,1,1,0,0,1,1), c(1,1,0,0,1,1,0,0))
> > colnames(contM)<-c("interestingPhenotype", "wildTypePhenotype")
> > rownames(contM)=sampleNames(qPCRBatch.taqman)
> > contM
>                interestingPhenotype wildTypePhenotype
> fp1.day3.v                        0                 1
> fp2.day3.v                        0                 1
> fp5.day3.mia                      1                 0
> fp6.day3.mia                      1                 0
> fp.3.day.3.v                      0                 1
> fp.4.day.3.v                      0                 1
> fp.7.day.3.mia                    1                 0
> fp.8.day.3.mia                    1                 0
> > hkg="Actb-Rn00667869_m1"
> > ddCq.grr<-deltaDeltaCq(qPCRBatch.norm, hkgs=hkg, contrastM=contM,
> case="interestingPhentype", control="wildTypePhenotype",
> + statCalc="arith")
> Error in .local(qPCRBatch, ...) : subscript out of bounds
>
>
It appears you are trying to run deltaDeltaCq() on already normalised data.

The example in the vignette runsdeltaDeltaCq() on raw data:

qPCRBatch.norm <- deltaCq(qPCRBatch =  qPCRBatch.taqman, h = hkgs,
calc="arith")

Where did you get the example above where you run deltaDeltaCq() on
qPCRBatch.norm? It *shouldn't* be in the vignette, but let me know if you
find it somewhere so I can eliminate it.


> I also made attempts to change this subscript as well, e.g. hkg<=>hkgs,
> removed hkgCalc since only using one hkg, but to no success.
>
> Also, I see for the qPCR.TechReps.txt.example there is a control gene for
> each the case detector, respectively.
> Unfortunately, you geNorm and NormFinder with example.taqman. However, for
> the qPCRBatch.TechReps.example-type of datafile-, which detector do you
> choose to normalize the data? In other words, how do you set up your
> contrast matrix to match caseA vs. caseB vs. control?
> I assume I could do this with the contrast matrix using cbind(c(caseA),
> (caseB), (control)), but would NormqPCR::deltaDeltaCq understand this
> language?
>


No - deltaDeltaCq is only for single contrast e.g. caseA - control or caseB
- control. You could perhaps use deltaCq normalisation on everything and
then build a more complex model.


>
> Lastly, how to deal with various timepoints:
> I have 3 different biological collections:
> 1) 1.5, 2) 2, and 3) 3.5.
> Two samples per collection (control, treated), and testing only 12
> individual genes plus actin as control gene in each sample and biological
> rep, which data for each gene has 4 (1day, 3day, 7day, and 14day)
> timepoints (in both control and treated samples as well as for target and
> control genes).
>             Case     Control
> actin_14d    0        1
> actin_1d     0        1
> actin_3d     0        1
> actin_7d     0        1
> lox22_14d    1        0
> lox22_1d     1        0
> lox22_3d     1        0
> lox22_7d     1        0
>
> Right now, I'm only utilizing one data file(one sample): Biological
> collection at 1.5. In this file I have both reference genes and the
> respective target genes for each timepoint(as shown above). For
> deltaDeltaCq function, do I need a second file to compare 1.5 with (e.g. 2
> and/or 3.5), because I'm currently using the featureNames() and not the
> sampleNames()?
> So, getting an error? This is not logical since I got same error with the
> NormqPCR vignette.
>
>
Again, the deltaDeltaCq method employed here is fairly simple, subtracting
the housekeeping gene value(s) from the Cq values and then subtracting
control from case (with appropriate 2^ transformations).

Cheers,

Jim


> Any suggestions is greatly appreciated.
> Regards,
> Franklin
>
>  -- output of sessionInfo():
>
> > sessionInfo()
> R version 2.15.1 (2012-06-22)
> Platform: x86_64-pc-mingw32/x64 (64-bit)
>
> locale:
> [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
> States.1252    LC_MONETARY=English_United States.1252
> [4] LC_NUMERIC=C                           LC_TIME=English_United
> States.1252
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] NormqPCR_1.4.0     RColorBrewer_1.0-5 ReadqPCR_1.4.0     affy_1.36.1
>      Biobase_2.18.0     BiocGenerics_0.4.0
>
> loaded via a namespace (and not attached):
> [1] affyio_1.26.0         BiocInstaller_1.8.3   preprocessCore_1.20.0
> zlibbioc_1.4.0
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>



-- 
Dr James R Perkins
Institute of Structural and Molecular Biology
Division of Biosciences
University College London
Gower Street
London, WC1E 6BT
UK

email: j.perkins@ucl.ac.uk



-- 
Dr James R Perkins
Institute of Structural and Molecular Biology
Division of Biosciences
University College London
Gower Street
London, WC1E 6BT
UK

email: j.perkins@ucl.ac.uk


On 28 February 2013 20:36, Franklin Johnson [guest]
<guest@bioconductor.org>wrote:

>
> Dear Maintainer,
>
> I have created my.qPCRBatch object read into R using the ReadqPCR package:
> > rownames(exprs(qPCRBatch))
>  [1] "actin_14d_TechReps.1" "actin_14d_TechReps.2" "actin_1d_TechReps.1"
>  "actin_1d_TechReps.2"  "actin_3d_TechReps.1"  "actin_3d_TechReps.2"
>  [7] "actin_7d_TechReps.1"  "actin_7d_TechReps.2"  "lox22_14d_TechReps.1"
> "lox22_14d_TechReps.2" "lox22_1d_TechReps.1"  "lox22_1d_TechReps.2"
> [13] "lox22_3d_TechReps.1"  "lox22_3d_TechReps.2"  "lox22_7d_TechReps.1"
>  "lox22_7d_TechReps.2"
> > combinedTechReps=combineTechReps(qPCRBatch)
> > combinedTechReps
> qPCRBatch (storageMode: lockedEnvironment)
> assayData: 8 features, 2 samples
>   element names: exprs
> protocolData: none
> phenoData
>   sampleNames: MeJa Triton.X
>   varLabels: sample
>   varMetadata: labelDescription
> featureData: none
> experimentData: use 'experimentData(object)'
> Annotation:
>
> I have also generated the deltaCq values using:
> > hkgs="actin_1d"
> > qPCRBatch.norm=deltaCq(combinedTechReps, hkgs=hkgs, calc="arith")
> > head(exprs(qPCRBatch.norm))
>             MeJa Triton.X
> actin_14d  0.655    0.260
> actin_1d   0.000    0.000
> actin_3d  -0.120    0.095
> actin_7d   0.465    0.145
> lox22_14d  2.755    3.530
> lox22_1d   0.735    2.740
>
> Now, to advance to Section 5.3, do I need to use qPCRBatch.norm. Or, did R
> populate qPCRBatch object with this deltaCq data? In other words, for
> Section 5.3, does the deltaDeltaCq function calculate deltaDeltaCq using
> deltaCq obtained in 5.1, or does it calculate deltaDeltaCq from the raw Cq
> data?
>
> However, I get error message when advancing from Section 5.1 to 5.3 using
> qPCRBatch.norm:
> > contM<-cbind(c(0,0,0,0,1,1,1,1), c(1,1,1,1,0,0,0,0))
> > colnames(contM)=c("MeJa", "Triton.X")
> > rownames(contM)=rownames(exprs(qPCRBatch.norm))
> > contM
>           MeJa Triton.X
> actin_14d    0        1
> actin_1d     0        1
> actin_3d     0        1
> actin_7d     0        1
> lox22_14d    1        0
> lox22_1d     1        0
> lox22_3d     1        0
> lox22_7d     1        0
> > hkgs<-"actin_1d"
> > ddCq.norm=deltaDeltaCq(qPCRBatch.norm, maxNACase=0, maxNAControl=0,
> hkgs=hkgs, contrastM=contM, case="MeJa", control="Triton.X",
> statCalc="geom")
> Error in .local(qPCRBatch, ...) : subscript out of bounds
>
> I tried many attempts to alter the subscripts to no success. So, I
> attempted to use the vignette to see how to work with the data. However, I
> got the same result:
> > head(exprs(qPCRBatch.norm))
>                      fp1.day3.v fp2.day3.v fp5.day3.mia fp6.day3.mia
> fp.3.day.3.v fp.4.day.3.v fp.7.day.3.mia fp.8.day.3.mia
> Actb.Rn00667869_m1     0.000000   0.000000     0.000000     0.000000
> 0.000000     0.000000       0.000000       0.000000
> Adipoq.Rn00595250_m1   0.016052  -0.116520     2.933523     2.540987
>  -0.178971    -0.563263       2.458509       2.736475
> Adrbk1.Rn00562822_m1         NA         NA     6.566628     6.642561
>     NA           NA       3.737100       6.873568
> Agtrl1.Rn00580252_s1   4.899380   5.035841     6.397364     5.680837
> 5.220796     4.425364       4.794776       5.345202
> Alpl.Rn00564931_m1    12.531942  11.808657    13.035166    12.239549
>  12.394802    11.772896      12.110000      12.255186
> B2m.Rn00560865_m1      0.741558   0.890717     2.040470     2.234605
> 0.505516     0.877598       1.927563       1.903269
> > contM<-cbind(c(0,0,1,1,0,0,1,1), c(1,1,0,0,1,1,0,0))
> > colnames(contM)<-c("interestingPhenotype", "wildTypePhenotype")
> > rownames(contM)=sampleNames(qPCRBatch.taqman)
> > contM
>                interestingPhenotype wildTypePhenotype
> fp1.day3.v                        0                 1
> fp2.day3.v                        0                 1
> fp5.day3.mia                      1                 0
> fp6.day3.mia                      1                 0
> fp.3.day.3.v                      0                 1
> fp.4.day.3.v                      0                 1
> fp.7.day.3.mia                    1                 0
> fp.8.day.3.mia                    1                 0
> > hkg="Actb-Rn00667869_m1"
> > ddCq.grr<-deltaDeltaCq(qPCRBatch.norm, hkgs=hkg, contrastM=contM,
> case="interestingPhentype", control="wildTypePhenotype",
> + statCalc="arith")
> Error in .local(qPCRBatch, ...) : subscript out of bounds
>
> I also made attempts to change this subscript as well, e.g. hkg<=>hkgs,
> removed hkgCalc since only using one hkg, but to no success.
>
> Also, I see for the qPCR.TechReps.txt.example there is a control gene for
> each the case detector, respectively.
> Unfortunately, you geNorm and NormFinder with example.taqman. However, for
> the qPCRBatch.TechReps.example-type of datafile-, which detector do you
> choose to normalize the data? In other words, how do you set up your
> contrast matrix to match caseA vs. caseB vs. control?
> I assume I could do this with the contrast matrix using cbind(c(caseA),
> (caseB), (control)), but would NormqPCR::deltaDeltaCq understand this
> language?
>
> Lastly, how to deal with various timepoints:
> I have 3 different biological collections:
> 1) 1.5, 2) 2, and 3) 3.5.
> Two samples per collection (control, treated), and testing only 12
> individual genes plus actin as control gene in each sample and biological
> rep, which data for each gene has 4 (1day, 3day, 7day, and 14day)
> timepoints (in both control and treated samples as well as for target and
> control genes).
>             Case     Control
> actin_14d    0        1
> actin_1d     0        1
> actin_3d     0        1
> actin_7d     0        1
> lox22_14d    1        0
> lox22_1d     1        0
> lox22_3d     1        0
> lox22_7d     1        0
>
> Right now, I'm only utilizing one data file(one sample): Biological
> collection at 1.5. In this file I have both reference genes and the
> respective target genes for each timepoint(as shown above). For
> deltaDeltaCq function, do I need a second file to compare 1.5 with (e.g. 2
> and/or 3.5), because I'm currently using the featureNames() and not the
> sampleNames()?
> So, getting an error? This is not logical since I got same error with the
> NormqPCR vignette.
>
> Any suggestions is greatly appreciated.
> Regards,
> Franklin
>
>  -- output of sessionInfo():
>
> > sessionInfo()
> R version 2.15.1 (2012-06-22)
> Platform: x86_64-pc-mingw32/x64 (64-bit)
>
> locale:
> [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
> States.1252    LC_MONETARY=English_United States.1252
> [4] LC_NUMERIC=C                           LC_TIME=English_United
> States.1252
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] NormqPCR_1.4.0     RColorBrewer_1.0-5 ReadqPCR_1.4.0     affy_1.36.1
>      Biobase_2.18.0     BiocGenerics_0.4.0
>
> loaded via a namespace (and not attached):
> [1] affyio_1.26.0         BiocInstaller_1.8.3   preprocessCore_1.20.0
> zlibbioc_1.4.0
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>



-- 
Dr James R Perkins
Institute of Structural and Molecular Biology
Division of Biosciences
University College London
Gower Street
London, WC1E 6BT
UK

email: j.perkins@ucl.ac.uk

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