liimma and Across Array Normalisation
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@saket-choudhary-6350
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I am analysing a proteomics microarray data set for a two group sample(Normal and Disease) using single color channel. The arrays have a set of pre-defined CONTROL points whose expression levels are supposed to be similar/same across all the arrays. I would like to 'normalise' the levels of all probes such that normalisation ends up with all CONTROL points having similar expression levels. If I understand it right, normalizebetweenarray does not allow this kind of normalisation. Is there a pre-implemented function to do this? If not, what would be a way to acheive this kind of normalisation? Code:
Microarray Proteomics Microarray Proteomics • 2.3k views
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@gordon-smyth
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WEHI, Melbourne, Australia
If 'x' is your background-corrected EList, then w <- rep(1,nrow(x)) w[controls] <- 100 y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) does what you want. For an example of this approach: http://rnajournal.cshlp.org/content/19/7/876 Best wishes Gordon --------- original message ---------- Saket Choudhary saketkc at gmail.com Thu Feb 6 06:59:42 CET 2014 I am analysing a proteomics microarray data set for a two group sample(Normal and Disease) using single color channel. The arrays have a set of pre-defined CONTROL points whose expression levels are supposed to be similar/same across all the arrays. I would like to 'normalise' the levels of all probes such that normalisation ends up with all CONTROL points having similar expression levels. If I understand it right, normalizebetweenarray does not allow this kind of normalisation. Is there a pre-implemented function to do this? If not, what would be a way to acheive this kind of normalisation? Code: ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
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Hello Gordon, Unfortunately I do not have access to this as of now. I will however get hold of it soon. After implementing this, I would expect the 'CONTROL' to have similar, if not same values, right? However some of the values for these Control genes after the normalisebetweenarray step have high variance. Is this behaviour normal or am I missing something? Saket On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > If 'x' is your background-corrected EList, then > > w <- rep(1,nrow(x)) > w[controls] <- 100 > y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) > > does what you want. > > For an example of this approach: > > http://rnajournal.cshlp.org/content/19/7/876 > > Best wishes > Gordon > > --------- original message ---------- > Saket Choudhary saketkc at gmail.com > Thu Feb 6 06:59:42 CET 2014 > > I am analysing a proteomics microarray data set for a two group > sample(Normal and Disease) using single color channel. The arrays have a set > of pre-defined CONTROL points whose expression levels are supposed to be > similar/same across all the arrays. > > I would like to 'normalise' the levels of all probes such that normalisation > ends up with all CONTROL points having similar expression levels. If I > understand it right, normalizebetweenarray does not allow this kind of > normalisation. > > Is there a pre-implemented function to do this? If not, what would be a way > to acheive this kind of normalisation? > > Code: > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:6}}
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Hello Gordon, I had a chance to go through the paper. I have a set of negative and positive controls, arising out of single channel Genepix platform. >From what I could gather, 'nec' method in limma performs backgroundcorrection using these negative control spots. However one of the inputs to 'nec' is also "detection.p", which the .gprs don't have. I could simply take a mean of all the negative controls E and Eb, and subtract it from each probe's E&Eb, doing it for all the arrays. Would this mimic what I want to acheive with the 'nec' function? Saket On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: > Hello Gordon, > > Unfortunately I do not have access to this as of now. I will however > get hold of it soon. > > After implementing this, I would expect the 'CONTROL' to have similar, > if not same values, right? > > However some of the values for these Control genes after the > normalisebetweenarray step have high variance. Is this behaviour > normal or am I missing something? > > Saket > > On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >> If 'x' is your background-corrected EList, then >> >> w <- rep(1,nrow(x)) >> w[controls] <- 100 >> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >> >> does what you want. >> >> For an example of this approach: >> >> http://rnajournal.cshlp.org/content/19/7/876 >> >> Best wishes >> Gordon >> >> --------- original message ---------- >> Saket Choudhary saketkc at gmail.com >> Thu Feb 6 06:59:42 CET 2014 >> >> I am analysing a proteomics microarray data set for a two group >> sample(Normal and Disease) using single color channel. The arrays have a set >> of pre-defined CONTROL points whose expression levels are supposed to be >> similar/same across all the arrays. >> >> I would like to 'normalise' the levels of all probes such that normalisation >> ends up with all CONTROL points having similar expression levels. If I >> understand it right, normalizebetweenarray does not allow this kind of >> normalisation. >> >> Is there a pre-implemented function to do this? If not, what would be a way >> to acheive this kind of normalisation? >> >> Code: >> >> ______________________________________________________________________ >> The information in this email is confidential and intended solely for the >> addressee. >> You must not disclose, forward, print or use it without the permission of >> the sender. >> ______________________________________________________________________
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On Sat, 8 Feb 2014, Saket Choudhary wrote: > Hello Gordon, > > I had a chance to go through the paper. I have a set of negative and > positive controls, arising out of single channel Genepix platform. > From what I could gather, 'nec' method in limma performs > backgroundcorrection using these negative control spots. Yes, but the negative controls are assumed to behave exactly like probes for unexpressed genes. This is true for Illumina Beadchips, but is often not the case for other platforms. If not, then you would be better to stick with normexp as you are already using. > However one of the inputs to 'nec' is also "detection.p", which the > .gprs don't have. detection.p is not a required argument. It is used only when negative controls are not available. > I could simply take a mean of all the negative controls E and Eb, and > subtract it from each probe's E&Eb, doing it for all the arrays. Would > this mimic what I want to acheive with the 'nec' function? No, that naive approach is not equivalent and typically performs poorly. Gordon > Saket > > On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >> Hello Gordon, >> >> Unfortunately I do not have access to this as of now. I will however >> get hold of it soon. >> >> After implementing this, I would expect the 'CONTROL' to have similar, >> if not same values, right? >> >> However some of the values for these Control genes after the >> normalisebetweenarray step have high variance. Is this behaviour >> normal or am I missing something? >> >> Saket >> >> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>> If 'x' is your background-corrected EList, then >>> >>> w <- rep(1,nrow(x)) >>> w[controls] <- 100 >>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>> >>> does what you want. >>> >>> For an example of this approach: >>> >>> http://rnajournal.cshlp.org/content/19/7/876 >>> >>> Best wishes >>> Gordon >>> >>> --------- original message ---------- >>> Saket Choudhary saketkc at gmail.com >>> Thu Feb 6 06:59:42 CET 2014 >>> >>> I am analysing a proteomics microarray data set for a two group >>> sample(Normal and Disease) using single color channel. The arrays have a set >>> of pre-defined CONTROL points whose expression levels are supposed to be >>> similar/same across all the arrays. >>> >>> I would like to 'normalise' the levels of all probes such that normalisation >>> ends up with all CONTROL points having similar expression levels. If I >>> understand it right, normalizebetweenarray does not allow this kind of >>> normalisation. >>> >>> Is there a pre-implemented function to do this? If not, what would be a way >>> to acheive this kind of normalisation? >>> >>> Code: >>> >>> ______________________________________________________________________ >>> The information in this email is confidential and intended solely for the >>> addressee. >>> You must not disclose, forward, print or use it without the permission of >>> the sender. >>> ______________________________________________________________________ > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
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Related question: Similar to your case, my final topTable()'s output indicates some genes having a negative logFC, though literature expects them to have a positive logFC. I looked up the calculations and the transition from positive to negative logFC for these genes seems to happen after the normalizeBetweenArrays step (irrespective of the kind of normalisation I choose). This is a naive question again, but I am trying to understand what should be a good metric to decide which method tends to give the least false positives like this, given tham I have limited knowledge of which genes should be up or down regulated(unlike in your case, where you knew the kind of regulation[up/down] expected). Thanks, Saket On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > > On Sat, 8 Feb 2014, Saket Choudhary wrote: > >> Hello Gordon, >> >> I had a chance to go through the paper. I have a set of negative and >> positive controls, arising out of single channel Genepix platform. >> From what I could gather, 'nec' method in limma performs >> backgroundcorrection using these negative control spots. > > > Yes, but the negative controls are assumed to behave exactly like probes for > unexpressed genes. This is true for Illumina Beadchips, but is often not > the case for other platforms. If not, then you would be better to stick > with normexp as you are already using. > > >> However one of the inputs to 'nec' is also "detection.p", which the >> .gprs don't have. > > > detection.p is not a required argument. It is used only when negative > controls are not available. > > >> I could simply take a mean of all the negative controls E and Eb, and >> subtract it from each probe's E&Eb, doing it for all the arrays. Would >> this mimic what I want to acheive with the 'nec' function? > > > No, that naive approach is not equivalent and typically performs poorly. > > Gordon > > >> Saket >> >> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>> >>> Hello Gordon, >>> >>> Unfortunately I do not have access to this as of now. I will however >>> get hold of it soon. >>> >>> After implementing this, I would expect the 'CONTROL' to have similar, >>> if not same values, right? >>> >>> However some of the values for these Control genes after the >>> normalisebetweenarray step have high variance. Is this behaviour >>> normal or am I missing something? >>> >>> Saket >>> >>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>> >>>> If 'x' is your background-corrected EList, then >>>> >>>> w <- rep(1,nrow(x)) >>>> w[controls] <- 100 >>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>> >>>> does what you want. >>>> >>>> For an example of this approach: >>>> >>>> http://rnajournal.cshlp.org/content/19/7/876 >>>> >>>> Best wishes >>>> Gordon >>>> >>>> --------- original message ---------- >>>> Saket Choudhary saketkc at gmail.com >>>> Thu Feb 6 06:59:42 CET 2014 >>>> >>>> I am analysing a proteomics microarray data set for a two group >>>> sample(Normal and Disease) using single color channel. The arrays have a >>>> set >>>> of pre-defined CONTROL points whose expression levels are supposed to be >>>> similar/same across all the arrays. >>>> >>>> I would like to 'normalise' the levels of all probes such that >>>> normalisation >>>> ends up with all CONTROL points having similar expression levels. If I >>>> understand it right, normalizebetweenarray does not allow this kind of >>>> normalisation. >>>> >>>> Is there a pre-implemented function to do this? If not, what would be a >>>> way >>>> to acheive this kind of normalisation? >>>> >>>> Code: >>>> >>>> ______________________________________________________________________ >>>> The information in this email is confidential and intended solely for >>>> the >>>> addressee. >>>> You must not disclose, forward, print or use it without the permission >>>> of >>>> the sender. >>>> ______________________________________________________________________ >> >> > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:6}}
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Hello Gordon, Is there a reason to believe the MA plots should inherently be baseline shifted after normalisation? Raw MA: https://db.tt/kDBod1EJ background correction with 'nec': https://db.tt/0vVWeD21 background correction with nec followed by normalisation: https://db.tt/f0M0rWeg background correction with 'normexp: https://db.tt/OJO0zea5 background correction with normexp followed by normalisation: https://db.tt/rbLJmFBE The files are a bit heavy so might take some time to load into any pdf reader. Code: Saket On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: > Related question: Similar to your case, my final topTable()'s output > indicates some genes having a negative logFC, though literature > expects them to have a positive logFC. > > I looked up the calculations and the transition from positive to > negative logFC for these genes seems to happen after the > normalizeBetweenArrays step (irrespective of the kind of normalisation > I choose). > > This is a naive question again, but I am trying to understand what should be > a good metric to decide which method tends to give the least false > positives like this, given tham I have limited knowledge of which > genes should be up or down regulated(unlike in your case, where you > knew the kind of regulation[up/down] expected). > > Thanks, > Saket > > > > > On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >> >> On Sat, 8 Feb 2014, Saket Choudhary wrote: >> >>> Hello Gordon, >>> >>> I had a chance to go through the paper. I have a set of negative and >>> positive controls, arising out of single channel Genepix platform. >>> From what I could gather, 'nec' method in limma performs >>> backgroundcorrection using these negative control spots. >> >> >> Yes, but the negative controls are assumed to behave exactly like probes for >> unexpressed genes. This is true for Illumina Beadchips, but is often not >> the case for other platforms. If not, then you would be better to stick >> with normexp as you are already using. >> >> >>> However one of the inputs to 'nec' is also "detection.p", which the >>> .gprs don't have. >> >> >> detection.p is not a required argument. It is used only when negative >> controls are not available. >> >> >>> I could simply take a mean of all the negative controls E and Eb, and >>> subtract it from each probe's E&Eb, doing it for all the arrays. Would >>> this mimic what I want to acheive with the 'nec' function? >> >> >> No, that naive approach is not equivalent and typically performs poorly. >> >> Gordon >> >> >>> Saket >>> >>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>> >>>> Hello Gordon, >>>> >>>> Unfortunately I do not have access to this as of now. I will however >>>> get hold of it soon. >>>> >>>> After implementing this, I would expect the 'CONTROL' to have similar, >>>> if not same values, right? >>>> >>>> However some of the values for these Control genes after the >>>> normalisebetweenarray step have high variance. Is this behaviour >>>> normal or am I missing something? >>>> >>>> Saket >>>> >>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>> >>>>> If 'x' is your background-corrected EList, then >>>>> >>>>> w <- rep(1,nrow(x)) >>>>> w[controls] <- 100 >>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>> >>>>> does what you want. >>>>> >>>>> For an example of this approach: >>>>> >>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>> >>>>> Best wishes >>>>> Gordon >>>>> >>>>> --------- original message ---------- >>>>> Saket Choudhary saketkc at gmail.com >>>>> Thu Feb 6 06:59:42 CET 2014 >>>>> >>>>> I am analysing a proteomics microarray data set for a two group >>>>> sample(Normal and Disease) using single color channel. The arrays have a >>>>> set >>>>> of pre-defined CONTROL points whose expression levels are supposed to be >>>>> similar/same across all the arrays. >>>>> >>>>> I would like to 'normalise' the levels of all probes such that >>>>> normalisation >>>>> ends up with all CONTROL points having similar expression levels. If I >>>>> understand it right, normalizebetweenarray does not allow this kind of >>>>> normalisation. >>>>> >>>>> Is there a pre-implemented function to do this? If not, what would be a >>>>> way >>>>> to acheive this kind of normalisation? >>>>> >>>>> Code: >>>>> >>>>> ______________________________________________________________________ >>>>> The information in this email is confidential and intended solely for >>>>> the >>>>> addressee. >>>>> You must not disclose, forward, print or use it without the permission >>>>> of >>>>> the sender. >>>>> ______________________________________________________________________ >>> >>> >> >> ______________________________________________________________________ >> The information in this email is confidential and intended solely for the >> addressee. >> You must not disclose, forward, print or use it without the permission of >> the sender. >> ______________________________________________________________________
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Hi Saket, On 2/11/2014 4:52 AM, Saket Choudhary wrote: > Hello Gordon, > > Is there a reason to believe the MA plots should inherently be > baseline shifted after normalisation? > > Raw MA: https://db.tt/kDBod1EJ > background correction with 'nec': https://db.tt/0vVWeD21 > background correction with nec followed by normalisation: https://db.tt/f0M0rWeg > background correction with 'normexp: https://db.tt/OJO0zea5 > background correction with normexp followed by normalisation: > https://db.tt/rbLJmFBE > > > The files are a bit heavy so might take some time to load into any pdf reader. That's why you don't use a vector graphics format for plots with lots of points. Instead, use png or jpeg. Best, Jim > > Code: > > Saket > > On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >> Related question: Similar to your case, my final topTable()'s output >> indicates some genes having a negative logFC, though literature >> expects them to have a positive logFC. >> >> I looked up the calculations and the transition from positive to >> negative logFC for these genes seems to happen after the >> normalizeBetweenArrays step (irrespective of the kind of normalisation >> I choose). >> >> This is a naive question again, but I am trying to understand what should be >> a good metric to decide which method tends to give the least false >> positives like this, given tham I have limited knowledge of which >> genes should be up or down regulated(unlike in your case, where you >> knew the kind of regulation[up/down] expected). >> >> Thanks, >> Saket >> >> >> >> >> On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>> On Sat, 8 Feb 2014, Saket Choudhary wrote: >>> >>>> Hello Gordon, >>>> >>>> I had a chance to go through the paper. I have a set of negative and >>>> positive controls, arising out of single channel Genepix platform. >>>> From what I could gather, 'nec' method in limma performs >>>> backgroundcorrection using these negative control spots. >>> >>> Yes, but the negative controls are assumed to behave exactly like probes for >>> unexpressed genes. This is true for Illumina Beadchips, but is often not >>> the case for other platforms. If not, then you would be better to stick >>> with normexp as you are already using. >>> >>> >>>> However one of the inputs to 'nec' is also "detection.p", which the >>>> .gprs don't have. >>> >>> detection.p is not a required argument. It is used only when negative >>> controls are not available. >>> >>> >>>> I could simply take a mean of all the negative controls E and Eb, and >>>> subtract it from each probe's E&Eb, doing it for all the arrays. Would >>>> this mimic what I want to acheive with the 'nec' function? >>> >>> No, that naive approach is not equivalent and typically performs poorly. >>> >>> Gordon >>> >>> >>>> Saket >>>> >>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>> Hello Gordon, >>>>> >>>>> Unfortunately I do not have access to this as of now. I will however >>>>> get hold of it soon. >>>>> >>>>> After implementing this, I would expect the 'CONTROL' to have similar, >>>>> if not same values, right? >>>>> >>>>> However some of the values for these Control genes after the >>>>> normalisebetweenarray step have high variance. Is this behaviour >>>>> normal or am I missing something? >>>>> >>>>> Saket >>>>> >>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>> If 'x' is your background-corrected EList, then >>>>>> >>>>>> w <- rep(1,nrow(x)) >>>>>> w[controls] <- 100 >>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>>> >>>>>> does what you want. >>>>>> >>>>>> For an example of this approach: >>>>>> >>>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>>> >>>>>> Best wishes >>>>>> Gordon >>>>>> >>>>>> --------- original message ---------- >>>>>> Saket Choudhary saketkc at gmail.com >>>>>> Thu Feb 6 06:59:42 CET 2014 >>>>>> >>>>>> I am analysing a proteomics microarray data set for a two group >>>>>> sample(Normal and Disease) using single color channel. The arrays have a >>>>>> set >>>>>> of pre-defined CONTROL points whose expression levels are supposed to be >>>>>> similar/same across all the arrays. >>>>>> >>>>>> I would like to 'normalise' the levels of all probes such that >>>>>> normalisation >>>>>> ends up with all CONTROL points having similar expression levels. If I >>>>>> understand it right, normalizebetweenarray does not allow this kind of >>>>>> normalisation. >>>>>> >>>>>> Is there a pre-implemented function to do this? If not, what would be a >>>>>> way >>>>>> to acheive this kind of normalisation? >>>>>> >>>>>> Code: >>>>>> >>>>>> ______________________________________________________________________ >>>>>> The information in this email is confidential and intended solely for >>>>>> the >>>>>> addressee. >>>>>> You must not disclose, forward, print or use it without the permission >>>>>> of >>>>>> the sender. >>>>>> ______________________________________________________________________ >>>> >>> ______________________________________________________________________ >>> The information in this email is confidential and intended solely for the >>> addressee. >>> You must not disclose, forward, print or use it without the permission of >>> the sender. >>> ______________________________________________________________________ > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099
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On 11 February 2014 14:03, James W. MacDonald <jmacdon at="" uw.edu=""> wrote: > Hi Saket, > > > On 2/11/2014 4:52 AM, Saket Choudhary wrote: >> >> Hello Gordon, >> >> Is there a reason to believe the MA plots should inherently be >> baseline shifted after normalisation? >> >> Raw MA: https://db.tt/kDBod1EJ >> background correction with 'nec': https://db.tt/0vVWeD21 >> background correction with nec followed by normalisation: >> https://db.tt/f0M0rWeg >> background correction with 'normexp: https://db.tt/OJO0zea5 >> background correction with normexp followed by normalisation: >> https://db.tt/rbLJmFBE >> >> >> The files are a bit heavy so might take some time to load into any pdf >> reader. > > > That's why you don't use a vector graphics format for plots with lots of > points. Instead, use png or jpeg. > The motivation for this was to combine everything using Sweave into a high-res report. Here are the low resolution version: Raw MA: https://db.tt/keqtevVR background correction with 'nec': https://db.tt/0vVWeD21 background correction with nec followed by normalisation: https://db.tt/3eFFJXKk background correction with 'normexp: https://db.tt/TNb5CHMc background correction with normexp followed by normalisation: https://db.tt/FBw5NLAN > Best, > > Jim > > >> >> Code: >> >> Saket >> >> On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>> >>> Related question: Similar to your case, my final topTable()'s output >>> indicates some genes having a negative logFC, though literature >>> expects them to have a positive logFC. >>> >>> I looked up the calculations and the transition from positive to >>> negative logFC for these genes seems to happen after the >>> normalizeBetweenArrays step (irrespective of the kind of normalisation >>> I choose). >>> >>> This is a naive question again, but I am trying to understand what should >>> be >>> a good metric to decide which method tends to give the least false >>> positives like this, given tham I have limited knowledge of which >>> genes should be up or down regulated(unlike in your case, where you >>> knew the kind of regulation[up/down] expected). >>> >>> Thanks, >>> Saket >>> >>> >>> >>> >>> On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>> >>>> On Sat, 8 Feb 2014, Saket Choudhary wrote: >>>> >>>>> Hello Gordon, >>>>> >>>>> I had a chance to go through the paper. I have a set of negative and >>>>> positive controls, arising out of single channel Genepix platform. >>>>> From what I could gather, 'nec' method in limma performs >>>>> backgroundcorrection using these negative control spots. >>>> >>>> >>>> Yes, but the negative controls are assumed to behave exactly like probes >>>> for >>>> unexpressed genes. This is true for Illumina Beadchips, but is often >>>> not >>>> the case for other platforms. If not, then you would be better to stick >>>> with normexp as you are already using. >>>> >>>> >>>>> However one of the inputs to 'nec' is also "detection.p", which the >>>>> .gprs don't have. >>>> >>>> >>>> detection.p is not a required argument. It is used only when negative >>>> controls are not available. >>>> >>>> >>>>> I could simply take a mean of all the negative controls E and Eb, and >>>>> subtract it from each probe's E&Eb, doing it for all the arrays. Would >>>>> this mimic what I want to acheive with the 'nec' function? >>>> >>>> >>>> No, that naive approach is not equivalent and typically performs poorly. >>>> >>>> Gordon >>>> >>>> >>>>> Saket >>>>> >>>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>>> >>>>>> Hello Gordon, >>>>>> >>>>>> Unfortunately I do not have access to this as of now. I will however >>>>>> get hold of it soon. >>>>>> >>>>>> After implementing this, I would expect the 'CONTROL' to have similar, >>>>>> if not same values, right? >>>>>> >>>>>> However some of the values for these Control genes after the >>>>>> normalisebetweenarray step have high variance. Is this behaviour >>>>>> normal or am I missing something? >>>>>> >>>>>> Saket >>>>>> >>>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>>> >>>>>>> If 'x' is your background-corrected EList, then >>>>>>> >>>>>>> w <- rep(1,nrow(x)) >>>>>>> w[controls] <- 100 >>>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>>>> >>>>>>> does what you want. >>>>>>> >>>>>>> For an example of this approach: >>>>>>> >>>>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>>>> >>>>>>> Best wishes >>>>>>> Gordon >>>>>>> >>>>>>> --------- original message ---------- >>>>>>> Saket Choudhary saketkc at gmail.com >>>>>>> Thu Feb 6 06:59:42 CET 2014 >>>>>>> >>>>>>> I am analysing a proteomics microarray data set for a two group >>>>>>> sample(Normal and Disease) using single color channel. The arrays >>>>>>> have a >>>>>>> set >>>>>>> of pre-defined CONTROL points whose expression levels are supposed to >>>>>>> be >>>>>>> similar/same across all the arrays. >>>>>>> >>>>>>> I would like to 'normalise' the levels of all probes such that >>>>>>> normalisation >>>>>>> ends up with all CONTROL points having similar expression levels. If >>>>>>> I >>>>>>> understand it right, normalizebetweenarray does not allow this kind >>>>>>> of >>>>>>> normalisation. >>>>>>> >>>>>>> Is there a pre-implemented function to do this? If not, what would be >>>>>>> a >>>>>>> way >>>>>>> to acheive this kind of normalisation? >>>>>>> >>>>>>> Code: >>>>>>> >>>>>>> >>>>>>> ______________________________________________________________________ >>>>>>> The information in this email is confidential and intended solely for >>>>>>> the >>>>>>> addressee. >>>>>>> You must not disclose, forward, print or use it without the >>>>>>> permission >>>>>>> of >>>>>>> the sender. >>>>>>> >>>>>>> ______________________________________________________________________ >>>>> >>>>> >>>> ______________________________________________________________________ >>>> The information in this email is confidential and intended solely for >>>> the >>>> addressee. >>>> You must not disclose, forward, print or use it without the permission >>>> of >>>> the sender. >>>> ______________________________________________________________________ >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 >
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Yes, obviously there'll be a baseline shift when you subtract background, then add an offset and log transform. You plots do not appear to be a valid MA plots. Gordon On Tue, 11 Feb 2014, Saket Choudhary wrote: > Hello Gordon, > > Is there a reason to believe the MA plots should inherently be > baseline shifted after normalisation? > > Raw MA: https://db.tt/kDBod1EJ > background correction with 'nec': https://db.tt/0vVWeD21 > background correction with nec followed by normalisation: https://db.tt/f0M0rWeg > background correction with 'normexp: https://db.tt/OJO0zea5 > background correction with normexp followed by normalisation: > https://db.tt/rbLJmFBE > > > The files are a bit heavy so might take some time to load into any pdf reader. > > Code: > > Saket > > On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >> Related question: Similar to your case, my final topTable()'s output >> indicates some genes having a negative logFC, though literature >> expects them to have a positive logFC. >> >> I looked up the calculations and the transition from positive to >> negative logFC for these genes seems to happen after the >> normalizeBetweenArrays step (irrespective of the kind of normalisation >> I choose). >> >> This is a naive question again, but I am trying to understand what should be >> a good metric to decide which method tends to give the least false >> positives like this, given tham I have limited knowledge of which >> genes should be up or down regulated(unlike in your case, where you >> knew the kind of regulation[up/down] expected). >> >> Thanks, >> Saket >> >> >> >> >> On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>> >>> On Sat, 8 Feb 2014, Saket Choudhary wrote: >>> >>>> Hello Gordon, >>>> >>>> I had a chance to go through the paper. I have a set of negative and >>>> positive controls, arising out of single channel Genepix platform. >>>> From what I could gather, 'nec' method in limma performs >>>> backgroundcorrection using these negative control spots. >>> >>> >>> Yes, but the negative controls are assumed to behave exactly like probes for >>> unexpressed genes. This is true for Illumina Beadchips, but is often not >>> the case for other platforms. If not, then you would be better to stick >>> with normexp as you are already using. >>> >>> >>>> However one of the inputs to 'nec' is also "detection.p", which the >>>> .gprs don't have. >>> >>> >>> detection.p is not a required argument. It is used only when negative >>> controls are not available. >>> >>> >>>> I could simply take a mean of all the negative controls E and Eb, and >>>> subtract it from each probe's E&Eb, doing it for all the arrays. Would >>>> this mimic what I want to acheive with the 'nec' function? >>> >>> >>> No, that naive approach is not equivalent and typically performs poorly. >>> >>> Gordon >>> >>> >>>> Saket >>>> >>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>> >>>>> Hello Gordon, >>>>> >>>>> Unfortunately I do not have access to this as of now. I will however >>>>> get hold of it soon. >>>>> >>>>> After implementing this, I would expect the 'CONTROL' to have similar, >>>>> if not same values, right? >>>>> >>>>> However some of the values for these Control genes after the >>>>> normalisebetweenarray step have high variance. Is this behaviour >>>>> normal or am I missing something? >>>>> >>>>> Saket >>>>> >>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>> >>>>>> If 'x' is your background-corrected EList, then >>>>>> >>>>>> w <- rep(1,nrow(x)) >>>>>> w[controls] <- 100 >>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>>> >>>>>> does what you want. >>>>>> >>>>>> For an example of this approach: >>>>>> >>>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>>> >>>>>> Best wishes >>>>>> Gordon >>>>>> >>>>>> --------- original message ---------- >>>>>> Saket Choudhary saketkc at gmail.com >>>>>> Thu Feb 6 06:59:42 CET 2014 >>>>>> >>>>>> I am analysing a proteomics microarray data set for a two group >>>>>> sample(Normal and Disease) using single color channel. The arrays have a >>>>>> set >>>>>> of pre-defined CONTROL points whose expression levels are supposed to be >>>>>> similar/same across all the arrays. >>>>>> >>>>>> I would like to 'normalise' the levels of all probes such that >>>>>> normalisation >>>>>> ends up with all CONTROL points having similar expression levels. If I >>>>>> understand it right, normalizebetweenarray does not allow this kind of >>>>>> normalisation. >>>>>> >>>>>> Is there a pre-implemented function to do this? If not, what would be a >>>>>> way >>>>>> to acheive this kind of normalisation? >>>>>> >>>>>> Code: >>>>>> >>>>>> ______________________________________________________________________ >>>>>> The information in this email is confidential and intended solely for >>>>>> the >>>>>> addressee. >>>>>> You must not disclose, forward, print or use it without the permission >>>>>> of >>>>>> the sender. >>>>>> ______________________________________________________________________ >>>> >>>> >>> >>> ______________________________________________________________________ >>> The information in this email is confidential and intended solely for the >>> addressee. >>> You must not disclose, forward, print or use it without the permission of >>> the sender. >>> ______________________________________________________________________ > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
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On 11-Feb-2014, at 10:31 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > Yes, obviously there'll be a baseline shift when you subtract background, then add an offset and log transform. > > You plots do not appear to be a valid MA plots. > Could you please point out the error? I understand a base line shoft is expected, but I cant figure out what is going wrong otherwise. Thanks, Saket > Gordon > > On Tue, 11 Feb 2014, Saket Choudhary wrote: > >> Hello Gordon, >> >> Is there a reason to believe the MA plots should inherently be >> baseline shifted after normalisation? >> >> Raw MA: https://db.tt/kDBod1EJ >> background correction with 'nec': https://db.tt/0vVWeD21 >> background correction with nec followed by normalisation: https://db.tt/f0M0rWeg >> background correction with 'normexp: https://db.tt/OJO0zea5 >> background correction with normexp followed by normalisation: >> https://db.tt/rbLJmFBE >> >> >> The files are a bit heavy so might take some time to load into any pdf reader. >> >> Code: >> >> Saket >> >> On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>> Related question: Similar to your case, my final topTable()'s output >>> indicates some genes having a negative logFC, though literature >>> expects them to have a positive logFC. >>> >>> I looked up the calculations and the transition from positive to >>> negative logFC for these genes seems to happen after the >>> normalizeBetweenArrays step (irrespective of the kind of normalisation >>> I choose). >>> >>> This is a naive question again, but I am trying to understand what should be >>> a good metric to decide which method tends to give the least false >>> positives like this, given tham I have limited knowledge of which >>> genes should be up or down regulated(unlike in your case, where you >>> knew the kind of regulation[up/down] expected). >>> >>> Thanks, >>> Saket >>> >>> >>> >>> >>> On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>> >>>> On Sat, 8 Feb 2014, Saket Choudhary wrote: >>>> >>>>> Hello Gordon, >>>>> >>>>> I had a chance to go through the paper. I have a set of negative and >>>>> positive controls, arising out of single channel Genepix platform. >>>>> From what I could gather, 'nec' method in limma performs >>>>> backgroundcorrection using these negative control spots. >>>> >>>> >>>> Yes, but the negative controls are assumed to behave exactly like probes for >>>> unexpressed genes. This is true for Illumina Beadchips, but is often not >>>> the case for other platforms. If not, then you would be better to stick >>>> with normexp as you are already using. >>>> >>>> >>>>> However one of the inputs to 'nec' is also "detection.p", which the >>>>> .gprs don't have. >>>> >>>> >>>> detection.p is not a required argument. It is used only when negative >>>> controls are not available. >>>> >>>> >>>>> I could simply take a mean of all the negative controls E and Eb, and >>>>> subtract it from each probe's E&Eb, doing it for all the arrays. Would >>>>> this mimic what I want to acheive with the 'nec' function? >>>> >>>> >>>> No, that naive approach is not equivalent and typically performs poorly. >>>> >>>> Gordon >>>> >>>> >>>>> Saket >>>>> >>>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>>> >>>>>> Hello Gordon, >>>>>> >>>>>> Unfortunately I do not have access to this as of now. I will however >>>>>> get hold of it soon. >>>>>> >>>>>> After implementing this, I would expect the 'CONTROL' to have similar, >>>>>> if not same values, right? >>>>>> >>>>>> However some of the values for these Control genes after the >>>>>> normalisebetweenarray step have high variance. Is this behaviour >>>>>> normal or am I missing something? >>>>>> >>>>>> Saket >>>>>> >>>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>>> >>>>>>> If 'x' is your background-corrected EList, then >>>>>>> >>>>>>> w <- rep(1,nrow(x)) >>>>>>> w[controls] <- 100 >>>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>>>> >>>>>>> does what you want. >>>>>>> >>>>>>> For an example of this approach: >>>>>>> >>>>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>>>> >>>>>>> Best wishes >>>>>>> Gordon >>>>>>> >>>>>>> --------- original message ---------- >>>>>>> Saket Choudhary saketkc at gmail.com >>>>>>> Thu Feb 6 06:59:42 CET 2014 >>>>>>> >>>>>>> I am analysing a proteomics microarray data set for a two group >>>>>>> sample(Normal and Disease) using single color channel. The arrays have a >>>>>>> set >>>>>>> of pre-defined CONTROL points whose expression levels are supposed to be >>>>>>> similar/same across all the arrays. >>>>>>> >>>>>>> I would like to 'normalise' the levels of all probes such that >>>>>>> normalisation >>>>>>> ends up with all CONTROL points having similar expression levels. If I >>>>>>> understand it right, normalizebetweenarray does not allow this kind of >>>>>>> normalisation. >>>>>>> >>>>>>> Is there a pre-implemented function to do this? If not, what would be a >>>>>>> way >>>>>>> to acheive this kind of normalisation? >>>>>>> >>>>>>> Code: >>>>>>> >>>>>>> ______________________________________________________________________ >>>>>>> The information in this email is confidential and intended solely for >>>>>>> the >>>>>>> addressee. >>>>>>> You must not disclose, forward, print or use it without the permission >>>>>>> of >>>>>>> the sender. >>>>>>> ______________________________________________________________________ >>>> >>>> ______________________________________________________________________ >>>> The information in this email is confidential and intended solely for the >>>> addressee. >>>> You must not disclose, forward, print or use it without the permission of >>>> the sender. >>>> ______________________________________________________________________ > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:4}}
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On Tue, 11 Feb 2014, Saket Choudhary wrote: > On 11-Feb-2014, at 10:31 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > >> Yes, obviously there'll be a baseline shift when you subtract background, then add an offset and log transform. >> >> You plots do not appear to be a valid MA plots. >> > > Could you please point out the error? > I understand a base line shoft is expected, but I cant figure out what > is going wrong otherwise. Well, you manually create an MAList object from your single channel data, even though an MAList is strictly for two colour data. If you deceive limma as to the true nature of your data, it's not surprising that the resulting plot might not be correct. I am not clear why you need to make so many variations on the standard limma single channel analysis pipeline. Gordon > > Thanks, > Saket > > >> Gordon >> >> On Tue, 11 Feb 2014, Saket Choudhary wrote: >> >>> Hello Gordon, >>> >>> Is there a reason to believe the MA plots should inherently be >>> baseline shifted after normalisation? >>> >>> Raw MA: https://db.tt/kDBod1EJ >>> background correction with 'nec': https://db.tt/0vVWeD21 >>> background correction with nec followed by normalisation: https://db.tt/f0M0rWeg >>> background correction with 'normexp: https://db.tt/OJO0zea5 >>> background correction with normexp followed by normalisation: >>> https://db.tt/rbLJmFBE >>> >>> >>> The files are a bit heavy so might take some time to load into any pdf reader. >>> >>> Code: >>> >>> Saket >>> >>> On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>> Related question: Similar to your case, my final topTable()'s output >>>> indicates some genes having a negative logFC, though literature >>>> expects them to have a positive logFC. >>>> >>>> I looked up the calculations and the transition from positive to >>>> negative logFC for these genes seems to happen after the >>>> normalizeBetweenArrays step (irrespective of the kind of normalisation >>>> I choose). >>>> >>>> This is a naive question again, but I am trying to understand what should be >>>> a good metric to decide which method tends to give the least false >>>> positives like this, given tham I have limited knowledge of which >>>> genes should be up or down regulated(unlike in your case, where you >>>> knew the kind of regulation[up/down] expected). >>>> >>>> Thanks, >>>> Saket >>>> >>>> >>>> >>>> >>>> On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>> >>>>> On Sat, 8 Feb 2014, Saket Choudhary wrote: >>>>> >>>>>> Hello Gordon, >>>>>> >>>>>> I had a chance to go through the paper. I have a set of negative and >>>>>> positive controls, arising out of single channel Genepix platform. >>>>>> From what I could gather, 'nec' method in limma performs >>>>>> backgroundcorrection using these negative control spots. >>>>> >>>>> >>>>> Yes, but the negative controls are assumed to behave exactly like probes for >>>>> unexpressed genes. This is true for Illumina Beadchips, but is often not >>>>> the case for other platforms. If not, then you would be better to stick >>>>> with normexp as you are already using. >>>>> >>>>> >>>>>> However one of the inputs to 'nec' is also "detection.p", which the >>>>>> .gprs don't have. >>>>> >>>>> >>>>> detection.p is not a required argument. It is used only when negative >>>>> controls are not available. >>>>> >>>>> >>>>>> I could simply take a mean of all the negative controls E and Eb, and >>>>>> subtract it from each probe's E&Eb, doing it for all the arrays. Would >>>>>> this mimic what I want to acheive with the 'nec' function? >>>>> >>>>> >>>>> No, that naive approach is not equivalent and typically performs poorly. >>>>> >>>>> Gordon >>>>> >>>>> >>>>>> Saket >>>>>> >>>>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>>>> >>>>>>> Hello Gordon, >>>>>>> >>>>>>> Unfortunately I do not have access to this as of now. I will however >>>>>>> get hold of it soon. >>>>>>> >>>>>>> After implementing this, I would expect the 'CONTROL' to have similar, >>>>>>> if not same values, right? >>>>>>> >>>>>>> However some of the values for these Control genes after the >>>>>>> normalisebetweenarray step have high variance. Is this behaviour >>>>>>> normal or am I missing something? >>>>>>> >>>>>>> Saket >>>>>>> >>>>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>>>> >>>>>>>> If 'x' is your background-corrected EList, then >>>>>>>> >>>>>>>> w <- rep(1,nrow(x)) >>>>>>>> w[controls] <- 100 >>>>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>>>>> >>>>>>>> does what you want. >>>>>>>> >>>>>>>> For an example of this approach: >>>>>>>> >>>>>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>>>>> >>>>>>>> Best wishes >>>>>>>> Gordon >>>>>>>> >>>>>>>> --------- original message ---------- >>>>>>>> Saket Choudhary saketkc at gmail.com >>>>>>>> Thu Feb 6 06:59:42 CET 2014 >>>>>>>> >>>>>>>> I am analysing a proteomics microarray data set for a two group >>>>>>>> sample(Normal and Disease) using single color channel. The arrays have a >>>>>>>> set >>>>>>>> of pre-defined CONTROL points whose expression levels are supposed to be >>>>>>>> similar/same across all the arrays. >>>>>>>> >>>>>>>> I would like to 'normalise' the levels of all probes such that >>>>>>>> normalisation >>>>>>>> ends up with all CONTROL points having similar expression levels. If I >>>>>>>> understand it right, normalizebetweenarray does not allow this kind of >>>>>>>> normalisation. >>>>>>>> >>>>>>>> Is there a pre-implemented function to do this? If not, what would be a >>>>>>>> way >>>>>>>> to acheive this kind of normalisation? >>>>>>>> >>>>>>>> Code: >>>>>>>> >>>>>>>> ______________________________________________________________________ >>>>>>>> The information in this email is confidential and intended solely for >>>>>>>> the >>>>>>>> addressee. >>>>>>>> You must not disclose, forward, print or use it without the permission >>>>>>>> of >>>>>>>> the sender. >>>>>>>> ______________________________________________________________________ >>>>> >>>>> ______________________________________________________________________ >>>>> The information in this email is confidential and intended solely for the >>>>> addressee. >>>>> You must not disclose, forward, print or use it without the permission of >>>>> the sender. >>>>> ______________________________________________________________________ >> >> ______________________________________________________________________ >> The information in this email is confidential and intended solely for the addressee. >> You must not disclose, forward, print or use it without the permission of the sender. >> ______________________________________________________________________ > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
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On 11-Feb-2014, at 10:52 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > On Tue, 11 Feb 2014, Saket Choudhary wrote: > >> On 11-Feb-2014, at 10:31 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >> >>> Yes, obviously there'll be a baseline shift when you subtract background, then add an offset and log transform. >>> >>> You plots do not appear to be a valid MA plots. >> >> Could you please point out the error? >> I understand a base line shoft is expected, but I cant figure out what >> is going wrong otherwise. > > Well, you manually create an MAList object from your single channel data, even though an MAList is strictly for two colour data. > > If you deceive limma as to the true nature of your data, it's not surprising that the resulting plot might not be correct. > > I am not clear why you need to make so many variations on the standard limma single channel analysis pipeline. > Is there any other way to visualise MA plots for single channel data? > Gordon > > >> >> Thanks, >> Saket >> >> >>> Gordon >>> >>> On Tue, 11 Feb 2014, Saket Choudhary wrote: >>> >>>> Hello Gordon, >>>> >>>> Is there a reason to believe the MA plots should inherently be >>>> baseline shifted after normalisation? >>>> >>>> Raw MA: https://db.tt/kDBod1EJ >>>> background correction with 'nec': https://db.tt/0vVWeD21 >>>> background correction with nec followed by normalisation: https://db.tt/f0M0rWeg >>>> background correction with 'normexp: https://db.tt/OJO0zea5 >>>> background correction with normexp followed by normalisation: >>>> https://db.tt/rbLJmFBE >>>> >>>> >>>> The files are a bit heavy so might take some time to load into any pdf reader. >>>> >>>> Code: >>>> >>>> Saket >>>> >>>> On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>> Related question: Similar to your case, my final topTable()'s output >>>>> indicates some genes having a negative logFC, though literature >>>>> expects them to have a positive logFC. >>>>> >>>>> I looked up the calculations and the transition from positive to >>>>> negative logFC for these genes seems to happen after the >>>>> normalizeBetweenArrays step (irrespective of the kind of normalisation >>>>> I choose). >>>>> >>>>> This is a naive question again, but I am trying to understand what should be >>>>> a good metric to decide which method tends to give the least false >>>>> positives like this, given tham I have limited knowledge of which >>>>> genes should be up or down regulated(unlike in your case, where you >>>>> knew the kind of regulation[up/down] expected). >>>>> >>>>> Thanks, >>>>> Saket >>>>> >>>>> >>>>> >>>>> >>>>> On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>> >>>>>> On Sat, 8 Feb 2014, Saket Choudhary wrote: >>>>>> >>>>>>> Hello Gordon, >>>>>>> >>>>>>> I had a chance to go through the paper. I have a set of negative and >>>>>>> positive controls, arising out of single channel Genepix platform. >>>>>>> From what I could gather, 'nec' method in limma performs >>>>>>> backgroundcorrection using these negative control spots. >>>>>> >>>>>> >>>>>> Yes, but the negative controls are assumed to behave exactly like probes for >>>>>> unexpressed genes. This is true for Illumina Beadchips, but is often not >>>>>> the case for other platforms. If not, then you would be better to stick >>>>>> with normexp as you are already using. >>>>>> >>>>>> >>>>>>> However one of the inputs to 'nec' is also "detection.p", which the >>>>>>> .gprs don't have. >>>>>> >>>>>> >>>>>> detection.p is not a required argument. It is used only when negative >>>>>> controls are not available. >>>>>> >>>>>> >>>>>>> I could simply take a mean of all the negative controls E and Eb, and >>>>>>> subtract it from each probe's E&Eb, doing it for all the arrays. Would >>>>>>> this mimic what I want to acheive with the 'nec' function? >>>>>> >>>>>> >>>>>> No, that naive approach is not equivalent and typically performs poorly. >>>>>> >>>>>> Gordon >>>>>> >>>>>> >>>>>>> Saket >>>>>>> >>>>>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>>>>> >>>>>>>> Hello Gordon, >>>>>>>> >>>>>>>> Unfortunately I do not have access to this as of now. I will however >>>>>>>> get hold of it soon. >>>>>>>> >>>>>>>> After implementing this, I would expect the 'CONTROL' to have similar, >>>>>>>> if not same values, right? >>>>>>>> >>>>>>>> However some of the values for these Control genes after the >>>>>>>> normalisebetweenarray step have high variance. Is this behaviour >>>>>>>> normal or am I missing something? >>>>>>>> >>>>>>>> Saket >>>>>>>> >>>>>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>>>>> >>>>>>>>> If 'x' is your background-corrected EList, then >>>>>>>>> >>>>>>>>> w <- rep(1,nrow(x)) >>>>>>>>> w[controls] <- 100 >>>>>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>>>>>> >>>>>>>>> does what you want. >>>>>>>>> >>>>>>>>> For an example of this approach: >>>>>>>>> >>>>>>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>>>>>> >>>>>>>>> Best wishes >>>>>>>>> Gordon >>>>>>>>> >>>>>>>>> --------- original message ---------- >>>>>>>>> Saket Choudhary saketkc at gmail.com >>>>>>>>> Thu Feb 6 06:59:42 CET 2014 >>>>>>>>> >>>>>>>>> I am analysing a proteomics microarray data set for a two group >>>>>>>>> sample(Normal and Disease) using single color channel. The arrays have a >>>>>>>>> set >>>>>>>>> of pre-defined CONTROL points whose expression levels are supposed to be >>>>>>>>> similar/same across all the arrays. >>>>>>>>> >>>>>>>>> I would like to 'normalise' the levels of all probes such that >>>>>>>>> normalisation >>>>>>>>> ends up with all CONTROL points having similar expression levels. If I >>>>>>>>> understand it right, normalizebetweenarray does not allow this kind of >>>>>>>>> normalisation. >>>>>>>>> >>>>>>>>> Is there a pre-implemented function to do this? If not, what would be a >>>>>>>>> way >>>>>>>>> to acheive this kind of normalisation? >>>>>>>>> >>>>>>>>> Code: >>>>>>>>> >>>>>>>>> ______________________________________________________________________ >>>>>>>>> The information in this email is confidential and intended solely for >>>>>>>>> the >>>>>>>>> addressee. >>>>>>>>> You must not disclose, forward, print or use it without the permission >>>>>>>>> of >>>>>>>>> the sender. >>>>>>>>> ______________________________________________________________________ >>>>>> >>>>>> ______________________________________________________________________ >>>>>> The information in this email is confidential and intended solely for the >>>>>> addressee. >>>>>> You must not disclose, forward, print or use it without the permission of >>>>>> the sender. >>>>>> ______________________________________________________________________ >>> >>> ______________________________________________________________________ >>> The information in this email is confidential and intended solely for the addressee. >>> You must not disclose, forward, print or use it without the permission of the sender. >>> ______________________________________________________________________ > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:4}}
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--------------------------------------------- Professor Gordon K Smyth, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Vic 3052, Australia. Tel: (03) 9345 2326, Fax (03) 9347 0852, http://www.statsci.org/smyth On Tue, 11 Feb 2014, Saket Choudhary wrote: > On 11-Feb-2014, at 10:52 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > >> On Tue, 11 Feb 2014, Saket Choudhary wrote: >> >>> On 11-Feb-2014, at 10:31 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>> >>>> Yes, obviously there'll be a baseline shift when you subtract background, then add an offset and log transform. >>>> >>>> You plots do not appear to be a valid MA plots. >>> >>> Could you please point out the error? >>> I understand a base line shoft is expected, but I cant figure out what >>> is going wrong otherwise. >> >> Well, you manually create an MAList object from your single channel data, even though an MAList is strictly for two colour data. >> >> If you deceive limma as to the true nature of your data, it's not surprising that the resulting plot might not be correct. >> >> I am not clear why you need to make so many variations on the standard limma single channel analysis pipeline. >> > > Is there any other way to visualise MA plots for single channel data? plotMA() already works directly on any data object: x <- read.maimages(targets$FileName,source="genepix",green.only=TRUE) plotMA(x) What could be easier than that? Gordon > >> Gordon >> >> >>> >>> Thanks, >>> Saket >>> >>> >>>> Gordon >>>> >>>> On Tue, 11 Feb 2014, Saket Choudhary wrote: >>>> >>>>> Hello Gordon, >>>>> >>>>> Is there a reason to believe the MA plots should inherently be >>>>> baseline shifted after normalisation? >>>>> >>>>> Raw MA: https://db.tt/kDBod1EJ >>>>> background correction with 'nec': https://db.tt/0vVWeD21 >>>>> background correction with nec followed by normalisation: https://db.tt/f0M0rWeg >>>>> background correction with 'normexp: https://db.tt/OJO0zea5 >>>>> background correction with normexp followed by normalisation: >>>>> https://db.tt/rbLJmFBE >>>>> >>>>> >>>>> The files are a bit heavy so might take some time to load into any pdf reader. >>>>> >>>>> Code: >>>>> >>>>> Saket >>>>> >>>>> On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>>> Related question: Similar to your case, my final topTable()'s output >>>>>> indicates some genes having a negative logFC, though literature >>>>>> expects them to have a positive logFC. >>>>>> >>>>>> I looked up the calculations and the transition from positive to >>>>>> negative logFC for these genes seems to happen after the >>>>>> normalizeBetweenArrays step (irrespective of the kind of normalisation >>>>>> I choose). >>>>>> >>>>>> This is a naive question again, but I am trying to understand what should be >>>>>> a good metric to decide which method tends to give the least false >>>>>> positives like this, given tham I have limited knowledge of which >>>>>> genes should be up or down regulated(unlike in your case, where you >>>>>> knew the kind of regulation[up/down] expected). >>>>>> >>>>>> Thanks, >>>>>> Saket >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>>> >>>>>>> On Sat, 8 Feb 2014, Saket Choudhary wrote: >>>>>>> >>>>>>>> Hello Gordon, >>>>>>>> >>>>>>>> I had a chance to go through the paper. I have a set of negative and >>>>>>>> positive controls, arising out of single channel Genepix platform. >>>>>>>> From what I could gather, 'nec' method in limma performs >>>>>>>> backgroundcorrection using these negative control spots. >>>>>>> >>>>>>> >>>>>>> Yes, but the negative controls are assumed to behave exactly like probes for >>>>>>> unexpressed genes. This is true for Illumina Beadchips, but is often not >>>>>>> the case for other platforms. If not, then you would be better to stick >>>>>>> with normexp as you are already using. >>>>>>> >>>>>>> >>>>>>>> However one of the inputs to 'nec' is also "detection.p", which the >>>>>>>> .gprs don't have. >>>>>>> >>>>>>> >>>>>>> detection.p is not a required argument. It is used only when negative >>>>>>> controls are not available. >>>>>>> >>>>>>> >>>>>>>> I could simply take a mean of all the negative controls E and Eb, and >>>>>>>> subtract it from each probe's E&Eb, doing it for all the arrays. Would >>>>>>>> this mimic what I want to acheive with the 'nec' function? >>>>>>> >>>>>>> >>>>>>> No, that naive approach is not equivalent and typically performs poorly. >>>>>>> >>>>>>> Gordon >>>>>>> >>>>>>> >>>>>>>> Saket >>>>>>>> >>>>>>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>>>>>> >>>>>>>>> Hello Gordon, >>>>>>>>> >>>>>>>>> Unfortunately I do not have access to this as of now. I will however >>>>>>>>> get hold of it soon. >>>>>>>>> >>>>>>>>> After implementing this, I would expect the 'CONTROL' to have similar, >>>>>>>>> if not same values, right? >>>>>>>>> >>>>>>>>> However some of the values for these Control genes after the >>>>>>>>> normalisebetweenarray step have high variance. Is this behaviour >>>>>>>>> normal or am I missing something? >>>>>>>>> >>>>>>>>> Saket >>>>>>>>> >>>>>>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>>>>>> >>>>>>>>>> If 'x' is your background-corrected EList, then >>>>>>>>>> >>>>>>>>>> w <- rep(1,nrow(x)) >>>>>>>>>> w[controls] <- 100 >>>>>>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>>>>>>> >>>>>>>>>> does what you want. >>>>>>>>>> >>>>>>>>>> For an example of this approach: >>>>>>>>>> >>>>>>>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>>>>>>> >>>>>>>>>> Best wishes >>>>>>>>>> Gordon >>>>>>>>>> >>>>>>>>>> --------- original message ---------- >>>>>>>>>> Saket Choudhary saketkc at gmail.com >>>>>>>>>> Thu Feb 6 06:59:42 CET 2014 >>>>>>>>>> >>>>>>>>>> I am analysing a proteomics microarray data set for a two group >>>>>>>>>> sample(Normal and Disease) using single color channel. The arrays have a >>>>>>>>>> set >>>>>>>>>> of pre-defined CONTROL points whose expression levels are supposed to be >>>>>>>>>> similar/same across all the arrays. >>>>>>>>>> >>>>>>>>>> I would like to 'normalise' the levels of all probes such that >>>>>>>>>> normalisation >>>>>>>>>> ends up with all CONTROL points having similar expression levels. If I >>>>>>>>>> understand it right, normalizebetweenarray does not allow this kind of >>>>>>>>>> normalisation. >>>>>>>>>> >>>>>>>>>> Is there a pre-implemented function to do this? If not, what would be a >>>>>>>>>> way >>>>>>>>>> to acheive this kind of normalisation? >>>>>>>>>> >>>>>>>>>> Code: >>>>>>>>>> >>>>>>>>>> ______________________________________________________________________ >>>>>>>>>> The information in this email is confidential and intended solely for >>>>>>>>>> the >>>>>>>>>> addressee. >>>>>>>>>> You must not disclose, forward, print or use it without the permission >>>>>>>>>> of >>>>>>>>>> the sender. >>>>>>>>>> ______________________________________________________________________ >>>>>>> >>>>>>> ______________________________________________________________________ >>>>>>> The information in this email is confidential and intended solely for the >>>>>>> addressee. >>>>>>> You must not disclose, forward, print or use it without the permission of >>>>>>> the sender. >>>>>>> ______________________________________________________________________ >>>> >>>> ______________________________________________________________________ >>>> The information in this email is confidential and intended solely for the addressee. >>>> You must not disclose, forward, print or use it without the permission of the sender. >>>> ______________________________________________________________________ >> >> ______________________________________________________________________ >> The information in this email is confidential and intended solely for the addressee. >> You must not disclose, forward, print or use it without the permission of the sender. >> ______________________________________________________________________ > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
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Although not documented, you can actually shorten the read command further to: x <- read.maimages(targets,source="genepix",green.only=TRUE) The function will automatically look for a column called "FileName" in targets. Gordon On Wed, 12 Feb 2014, Gordon K Smyth wrote: > On Tue, 11 Feb 2014, Saket Choudhary wrote: > >> On 11-Feb-2014, at 10:52 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >> >>> On Tue, 11 Feb 2014, Saket Choudhary wrote: >>> >>>> On 11-Feb-2014, at 10:31 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>> >>>>> Yes, obviously there'll be a baseline shift when you subtract >>>>> background, then add an offset and log transform. >>>>> >>>>> You plots do not appear to be a valid MA plots. >>>> >>>> Could you please point out the error? >>>> I understand a base line shoft is expected, but I cant figure out what >>>> is going wrong otherwise. >>> >>> Well, you manually create an MAList object from your single channel data, >>> even though an MAList is strictly for two colour data. >>> >>> If you deceive limma as to the true nature of your data, it's not >>> surprising that the resulting plot might not be correct. >>> >>> I am not clear why you need to make so many variations on the standard >>> limma single channel analysis pipeline. >>> >> >> Is there any other way to visualise MA plots for single channel data? > > plotMA() already works directly on any data object: > > x <- read.maimages(targets$FileName,source="genepix",green.only=TRUE) > plotMA(x) > > What could be easier than that? > > Gordon > >> >>> Gordon >>> >>> >>>> >>>> Thanks, >>>> Saket >>>> >>>> >>>>> Gordon >>>>> >>>>> On Tue, 11 Feb 2014, Saket Choudhary wrote: >>>>> >>>>>> Hello Gordon, >>>>>> >>>>>> Is there a reason to believe the MA plots should inherently be >>>>>> baseline shifted after normalisation? >>>>>> >>>>>> Raw MA: https://db.tt/kDBod1EJ >>>>>> background correction with 'nec': https://db.tt/0vVWeD21 >>>>>> background correction with nec followed by normalisation: >>>>>> https://db.tt/f0M0rWeg >>>>>> background correction with 'normexp: https://db.tt/OJO0zea5 >>>>>> background correction with normexp followed by normalisation: >>>>>> https://db.tt/rbLJmFBE >>>>>> >>>>>> >>>>>> The files are a bit heavy so might take some time to load into any pdf >>>>>> reader. >>>>>> >>>>>> Code: >>>>>> >>>>>> Saket >>>>>> >>>>>> On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>>>> Related question: Similar to your case, my final topTable()'s output >>>>>>> indicates some genes having a negative logFC, though literature >>>>>>> expects them to have a positive logFC. >>>>>>> >>>>>>> I looked up the calculations and the transition from positive to >>>>>>> negative logFC for these genes seems to happen after the >>>>>>> normalizeBetweenArrays step (irrespective of the kind of normalisation >>>>>>> I choose). >>>>>>> >>>>>>> This is a naive question again, but I am trying to understand what >>>>>>> should be >>>>>>> a good metric to decide which method tends to give the least false >>>>>>> positives like this, given tham I have limited knowledge of which >>>>>>> genes should be up or down regulated(unlike in your case, where you >>>>>>> knew the kind of regulation[up/down] expected). >>>>>>> >>>>>>> Thanks, >>>>>>> Saket >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>>>> >>>>>>>> On Sat, 8 Feb 2014, Saket Choudhary wrote: >>>>>>>> >>>>>>>>> Hello Gordon, >>>>>>>>> >>>>>>>>> I had a chance to go through the paper. I have a set of negative and >>>>>>>>> positive controls, arising out of single channel Genepix platform. >>>>>>>>> From what I could gather, 'nec' method in limma performs >>>>>>>>> backgroundcorrection using these negative control spots. >>>>>>>> >>>>>>>> >>>>>>>> Yes, but the negative controls are assumed to behave exactly like >>>>>>>> probes for >>>>>>>> unexpressed genes. This is true for Illumina Beadchips, but is often >>>>>>>> not >>>>>>>> the case for other platforms. If not, then you would be better to >>>>>>>> stick >>>>>>>> with normexp as you are already using. >>>>>>>> >>>>>>>> >>>>>>>>> However one of the inputs to 'nec' is also "detection.p", which the >>>>>>>>> .gprs don't have. >>>>>>>> >>>>>>>> >>>>>>>> detection.p is not a required argument. It is used only when >>>>>>>> negative >>>>>>>> controls are not available. >>>>>>>> >>>>>>>> >>>>>>>>> I could simply take a mean of all the negative controls E and Eb, >>>>>>>>> and >>>>>>>>> subtract it from each probe's E&Eb, doing it for all the arrays. >>>>>>>>> Would >>>>>>>>> this mimic what I want to acheive with the 'nec' function? >>>>>>>> >>>>>>>> >>>>>>>> No, that naive approach is not equivalent and typically performs >>>>>>>> poorly. >>>>>>>> >>>>>>>> Gordon >>>>>>>> >>>>>>>> >>>>>>>>> Saket >>>>>>>>> >>>>>>>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>>>>>>> >>>>>>>>>> Hello Gordon, >>>>>>>>>> >>>>>>>>>> Unfortunately I do not have access to this as of now. I will >>>>>>>>>> however >>>>>>>>>> get hold of it soon. >>>>>>>>>> >>>>>>>>>> After implementing this, I would expect the 'CONTROL' to have >>>>>>>>>> similar, >>>>>>>>>> if not same values, right? >>>>>>>>>> >>>>>>>>>> However some of the values for these Control genes after the >>>>>>>>>> normalisebetweenarray step have high variance. Is this behaviour >>>>>>>>>> normal or am I missing something? >>>>>>>>>> >>>>>>>>>> Saket >>>>>>>>>> >>>>>>>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>>>>>>> >>>>>>>>>>> If 'x' is your background-corrected EList, then >>>>>>>>>>> >>>>>>>>>>> w <- rep(1,nrow(x)) >>>>>>>>>>> w[controls] <- 100 >>>>>>>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>>>>>>>> >>>>>>>>>>> does what you want. >>>>>>>>>>> >>>>>>>>>>> For an example of this approach: >>>>>>>>>>> >>>>>>>>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>>>>>>>> >>>>>>>>>>> Best wishes >>>>>>>>>>> Gordon >>>>>>>>>>> >>>>>>>>>>> --------- original message ---------- >>>>>>>>>>> Saket Choudhary saketkc at gmail.com >>>>>>>>>>> Thu Feb 6 06:59:42 CET 2014 >>>>>>>>>>> >>>>>>>>>>> I am analysing a proteomics microarray data set for a two group >>>>>>>>>>> sample(Normal and Disease) using single color channel. The arrays >>>>>>>>>>> have a >>>>>>>>>>> set >>>>>>>>>>> of pre-defined CONTROL points whose expression levels are supposed >>>>>>>>>>> to be >>>>>>>>>>> similar/same across all the arrays. >>>>>>>>>>> >>>>>>>>>>> I would like to 'normalise' the levels of all probes such that >>>>>>>>>>> normalisation >>>>>>>>>>> ends up with all CONTROL points having similar expression levels. >>>>>>>>>>> If I >>>>>>>>>>> understand it right, normalizebetweenarray does not allow this >>>>>>>>>>> kind of >>>>>>>>>>> normalisation. >>>>>>>>>>> >>>>>>>>>>> Is there a pre-implemented function to do this? If not, what would >>>>>>>>>>> be a >>>>>>>>>>> way >>>>>>>>>>> to acheive this kind of normalisation? >>>>>>>>>>> >>>>>>>>>>> Code: ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
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I wanted it to visualise it in 3x2 style. On 11 February 2014 23:20, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > Although not documented, you can actually shorten the read command further > to: > > x <- read.maimages(targets,source="genepix",green.only=TRUE) > > The function will automatically look for a column called "FileName" in > targets. > > Gordon > > > On Wed, 12 Feb 2014, Gordon K Smyth wrote: > >> On Tue, 11 Feb 2014, Saket Choudhary wrote: >> >>> On 11-Feb-2014, at 10:52 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>> >>>> On Tue, 11 Feb 2014, Saket Choudhary wrote: >>>> >>>>> On 11-Feb-2014, at 10:31 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>> >>>>>> Yes, obviously there'll be a baseline shift when you subtract >>>>>> background, then add an offset and log transform. >>>>>> >>>>>> You plots do not appear to be a valid MA plots. >>>>> >>>>> >>>>> Could you please point out the error? >>>>> I understand a base line shoft is expected, but I cant figure out what >>>>> is going wrong otherwise. >>>> >>>> >>>> Well, you manually create an MAList object from your single channel >>>> data, even though an MAList is strictly for two colour data. >>>> >>>> If you deceive limma as to the true nature of your data, it's not >>>> surprising that the resulting plot might not be correct. >>>> >>>> I am not clear why you need to make so many variations on the standard >>>> limma single channel analysis pipeline. >>>> >>> >>> Is there any other way to visualise MA plots for single channel data? >> >> >> plotMA() already works directly on any data object: >> >> x <- read.maimages(targets$FileName,source="genepix",green.only=TRUE) >> plotMA(x) >> >> What could be easier than that? >> >> Gordon >> >>> >>>> Gordon >>>> >>>> >>>>> >>>>> Thanks, >>>>> Saket >>>>> >>>>> >>>>>> Gordon >>>>>> >>>>>> On Tue, 11 Feb 2014, Saket Choudhary wrote: >>>>>> >>>>>>> Hello Gordon, >>>>>>> >>>>>>> Is there a reason to believe the MA plots should inherently be >>>>>>> baseline shifted after normalisation? >>>>>>> >>>>>>> Raw MA: https://db.tt/kDBod1EJ >>>>>>> background correction with 'nec': https://db.tt/0vVWeD21 >>>>>>> background correction with nec followed by normalisation: >>>>>>> https://db.tt/f0M0rWeg >>>>>>> background correction with 'normexp: https://db.tt/OJO0zea5 >>>>>>> background correction with normexp followed by normalisation: >>>>>>> https://db.tt/rbLJmFBE >>>>>>> >>>>>>> >>>>>>> The files are a bit heavy so might take some time to load into any >>>>>>> pdf reader. >>>>>>> >>>>>>> Code: >>>>>>> >>>>>>> Saket >>>>>>> >>>>>>> On 9 February 2014 20:45, Saket Choudhary <saketkc at="" gmail.com=""> wrote: >>>>>>>> >>>>>>>> Related question: Similar to your case, my final topTable()'s output >>>>>>>> indicates some genes having a negative logFC, though literature >>>>>>>> expects them to have a positive logFC. >>>>>>>> >>>>>>>> I looked up the calculations and the transition from positive to >>>>>>>> negative logFC for these genes seems to happen after the >>>>>>>> normalizeBetweenArrays step (irrespective of the kind of >>>>>>>> normalisation >>>>>>>> I choose). >>>>>>>> >>>>>>>> This is a naive question again, but I am trying to understand what >>>>>>>> should be >>>>>>>> a good metric to decide which method tends to give the least false >>>>>>>> positives like this, given tham I have limited knowledge of which >>>>>>>> genes should be up or down regulated(unlike in your case, where you >>>>>>>> knew the kind of regulation[up/down] expected). >>>>>>>> >>>>>>>> Thanks, >>>>>>>> Saket >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> On 9 February 2014 04:00, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >>>>>>>>> >>>>>>>>> >>>>>>>>> On Sat, 8 Feb 2014, Saket Choudhary wrote: >>>>>>>>> >>>>>>>>>> Hello Gordon, >>>>>>>>>> >>>>>>>>>> I had a chance to go through the paper. I have a set of negative >>>>>>>>>> and >>>>>>>>>> positive controls, arising out of single channel Genepix platform. >>>>>>>>>> From what I could gather, 'nec' method in limma performs >>>>>>>>>> backgroundcorrection using these negative control spots. >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Yes, but the negative controls are assumed to behave exactly like >>>>>>>>> probes for >>>>>>>>> unexpressed genes. This is true for Illumina Beadchips, but is >>>>>>>>> often not >>>>>>>>> the case for other platforms. If not, then you would be better to >>>>>>>>> stick >>>>>>>>> with normexp as you are already using. >>>>>>>>> >>>>>>>>> >>>>>>>>>> However one of the inputs to 'nec' is also "detection.p", which >>>>>>>>>> the >>>>>>>>>> .gprs don't have. >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> detection.p is not a required argument. It is used only when >>>>>>>>> negative >>>>>>>>> controls are not available. >>>>>>>>> >>>>>>>>> >>>>>>>>>> I could simply take a mean of all the negative controls E and Eb, >>>>>>>>>> and >>>>>>>>>> subtract it from each probe's E&Eb, doing it for all the arrays. >>>>>>>>>> Would >>>>>>>>>> this mimic what I want to acheive with the 'nec' function? >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> No, that naive approach is not equivalent and typically performs >>>>>>>>> poorly. >>>>>>>>> >>>>>>>>> Gordon >>>>>>>>> >>>>>>>>> >>>>>>>>>> Saket >>>>>>>>>> >>>>>>>>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at="" gmail.com=""> >>>>>>>>>> wrote: >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> Hello Gordon, >>>>>>>>>>> >>>>>>>>>>> Unfortunately I do not have access to this as of now. I will >>>>>>>>>>> however >>>>>>>>>>> get hold of it soon. >>>>>>>>>>> >>>>>>>>>>> After implementing this, I would expect the 'CONTROL' to have >>>>>>>>>>> similar, >>>>>>>>>>> if not same values, right? >>>>>>>>>>> >>>>>>>>>>> However some of the values for these Control genes after the >>>>>>>>>>> normalisebetweenarray step have high variance. Is this behaviour >>>>>>>>>>> normal or am I missing something? >>>>>>>>>>> >>>>>>>>>>> Saket >>>>>>>>>>> >>>>>>>>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at="" wehi.edu.au=""> >>>>>>>>>>> wrote: >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> If 'x' is your background-corrected EList, then >>>>>>>>>>>> >>>>>>>>>>>> w <- rep(1,nrow(x)) >>>>>>>>>>>> w[controls] <- 100 >>>>>>>>>>>> y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w) >>>>>>>>>>>> >>>>>>>>>>>> does what you want. >>>>>>>>>>>> >>>>>>>>>>>> For an example of this approach: >>>>>>>>>>>> >>>>>>>>>>>> http://rnajournal.cshlp.org/content/19/7/876 >>>>>>>>>>>> >>>>>>>>>>>> Best wishes >>>>>>>>>>>> Gordon >>>>>>>>>>>> >>>>>>>>>>>> --------- original message ---------- >>>>>>>>>>>> Saket Choudhary saketkc at gmail.com >>>>>>>>>>>> Thu Feb 6 06:59:42 CET 2014 >>>>>>>>>>>> >>>>>>>>>>>> I am analysing a proteomics microarray data set for a two group >>>>>>>>>>>> sample(Normal and Disease) using single color channel. The >>>>>>>>>>>> arrays have a >>>>>>>>>>>> set >>>>>>>>>>>> of pre-defined CONTROL points whose expression levels are >>>>>>>>>>>> supposed to be >>>>>>>>>>>> similar/same across all the arrays. >>>>>>>>>>>> >>>>>>>>>>>> I would like to 'normalise' the levels of all probes such that >>>>>>>>>>>> normalisation >>>>>>>>>>>> ends up with all CONTROL points having similar expression >>>>>>>>>>>> levels. If I >>>>>>>>>>>> understand it right, normalizebetweenarray does not allow this >>>>>>>>>>>> kind of >>>>>>>>>>>> normalisation. >>>>>>>>>>>> >>>>>>>>>>>> Is there a pre-implemented function to do this? If not, what >>>>>>>>>>>> would be a >>>>>>>>>>>> way >>>>>>>>>>>> to acheive this kind of normalisation? >>>>>>>>>>>> >>>>>>>>>>>> Code: > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:6}}
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Hi Saket, On Tue, Feb 11, 2014 at 3:49 PM, Saket Choudhary <saketkc at="" gmail.com=""> wrote: > I wanted it to visualise it in 3x2 style. Whipping up your own MA plot isn't very difficult from the raw data. Given a set of measurements A from the transcriptome, and another set of measurements B from the transcriptome, the x,y coordinate for each gene_i is: * x: mean expression of gene_i between A and B, ie: (A_i + B_i) / 2 * y: fold change of gene_i A vs B, ie: A_i / B_i (vectorize these calculations and be sure to convert to logs where appropriate for your data -- that is to say, if it's not log already) Given that you already have the data on hand that you want to plot (data from several single channel arrays), and the details of constructing a basic MA plot are quite straightforward, you can take your data + your favorite plotting package (base, lattice, ggplot2) and plot it in any number of ways that you see fit. It'd be an informative exercise and ultimately give you the most flexibility in the data presentation, which seems like what you are after, so perhaps you can try that. HTH, -steve -- Steve Lianoglou Computational Biologist Genentech
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