HTqPCR normalization issues - third posting
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@alessandroguffantigenomniacom-4436
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Dear Heidi good morning ! We made some more work on HTqPCR (actually, mainly Elena..) and we have an update and a question for you: => update: seemingly with the latest HTqPCR it is not possible to import anymore datasets in a simple format, gene name, Ct, group for example, with readCtData(). Did you receive any observation from other groups on this ? => data normalization: now normalizeCtData with norm="geometric.mean" supports the possibility of choosing the sample (the reference) which otherwise is by default the first sample, as it is confirmed by themanual: > g.norm <- normalizeCtDataraw.cat, norm = "geometric.mean") Scaling Ct values Using geometric mean within each sample Scaling factors: *1.00* 1.06 1.05 1.02 1.04 1.02 Now, this was our initial doubt and still persists: why a normalization method which should operate an average Ct value for each sample, and scales all Ct values according to the ratio of these mean Ct values across samples, should take as a reference one of the samples (the first in the data matrix by default) and leave its values untouched ? * ==> *As far as scaling ranking is concerned, we noticed that even if we specified the reference as pseudo mean or median, apparently nothing was changed in term of scaling factor and reference column (always the first sample by default). Is it correct? Where the normalization is influenced by the reference? In general, which is the logic of selecting one sample as a 'reference' in the normalization step with these methods ? Many thanks in advance for any highlight on this, kind regards Alessandro & Elena -- Alessandro Guffanti Alessandro Guffanti Head, Bioinformatics *Genomnia srl* Via Nerviano, 31/B – 20020 Lainate (MI) Tel. +39-0293305.702 / Fax +39-0293305.777 www.genomnia.com <http: www.genomnia.com=""> alessandro.guffanti@genomnia.com <mailto:alessandro.guffanti@genomnia.com> *P* *Per cortesia, prima di stampare questa e-mail pensate all'ambiente.* * Please consider the environment before printing this mail note.* ----------------------------------------------------------- Il Contenuto del presente messaggio potrebbe contenere informazioni confidenziali a favore dei soli destinatari del messaggio stesso. Qualora riceviate per errore questo messaggio siete pregati di cancellarlo dalla memoria del computer e di contattare i numeri sopra indicati. Ogni utilizzo o ritrasmissione dei contenuti del messaggio da parte di soggetti diversi dai destinatari è da considerarsi vietato ed abusivo. The information transmitted is intended only for the per...{{dropped:10}}
Normalization HTqPCR Normalization HTqPCR • 1.4k views
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Levi Waldron ★ 1.1k
@levi-waldron-3429
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CUNY Graduate School of Public Health a…
Dear Alessandro & Elena, I have not used this package but have used the kinds of qPCR normalization methods you are talking about and can offer some comment, below. On Mon, Nov 11, 2013 at 8:17 AM, alessandro.guffanti at genomnia.com <alessandro.guffanti at="" genomnia.com=""> wrote: > Now, this was our initial doubt and still persists: why a normalization > method which should operate an average Ct value for each sample, and > scales all Ct values according to the ratio of these mean Ct values > across samples, should take as a reference one of the samples (the first > in the data matrix by default) and leave its values untouched ? I think that using average Ct value and a reference sample for HT qRT-PCR should work as follows, if you assume 100% efficiency for all primers: 1) for each sample, subtract the mean Ct value from the Ct value of each gene. 2) for each gene, subtract the Ct value of that gene in the reference sample. delta-delta-Ct for the reference sample is then zero for all genes. This is the method of Pfaffl 2001 (PMID 11328886) but with mean Ct value for the sample replacing use of a housekeeping gene. I wrote a function a few years ago to do this, with options to specify the efficiency of each primer or use one or more housekeeping genes, and to specify one or more control samples (in which case the mean delta Ct of these samples is used as reference, so that delta-delta-Ct values for each case sample can be directly interpreted as log2(FC) ). My function isn't packaged or documented, but if it might be useful to you I'd be glad to help. > ==> *As far as scaling ranking is concerned, we noticed that even if we > specified the reference as pseudo mean or median, apparently nothing was > changed in term of scaling factor and reference column (always the first > sample by default). Is it correct? Where the normalization is influenced > by the reference? > > In general, which is the logic of selecting one sample as a 'reference' > in the normalization step with these methods ? I think it is the same logic as described by Pfaffl. Delta-delta-Ct values can be interpreted directly as log2(Fold Change) relative to the reference sample. If you have primers with efficiencies other than 100%, then it also corrects for differences in the number of amplification cycles for a gene between samples. Hope this helps, Levi -- Levi Waldron Assistant Professor of Biostatistics Hunter College School of Urban Public Health City University of New York 2180 3rd Ave Rm 538 New York NY 10035-4003 212-396-7747
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Thanks a lot Levi! this is enlightening and we will use your function on our datasets, we will let you know how this goes. Just a final question, excuse me the sheer ignorance: if I want to associate a statistics to each final value (which is indeed the big added vakue of HTqPCR), of course in the case when I am a comparing a first group of samples vs a second group of samples, which could be a reasonable approach ? a simple rowwise t-test ? Keep in touch Alessandro On 11/12/2013 7:04 PM, Levi Waldron wrote: > Dear Alessandro & Elena, > > I have not used this package but have used the kinds of qPCR > normalization methods you are talking about and can offer some > comment, below. > > On Mon, Nov 11, 2013 at 8:17 AM, alessandro.guffanti@genomnia.com > <alessandro.guffanti@genomnia.com> wrote: >> Now, this was our initial doubt and still persists: why a normalization >> method which should operate an average Ct value for each sample, and >> scales all Ct values according to the ratio of these mean Ct values >> across samples, should take as a reference one of the samples (the first >> in the data matrix by default) and leave its values untouched ? > I think that using average Ct value and a reference sample for HT > qRT-PCR should work as follows, if you assume 100% efficiency for all > primers: > > 1) for each sample, subtract the mean Ct value from the Ct value of each gene. > 2) for each gene, subtract the Ct value of that gene in the reference > sample. delta-delta-Ct for the reference sample is then zero for all > genes. > > This is the method of Pfaffl 2001 (PMID 11328886) but with mean Ct > value for the sample replacing use of a housekeeping gene. I wrote a > function a few years ago to do this, with options to specify the > efficiency of each primer or use one or more housekeeping genes, and > to specify one or more control samples (in which case the mean delta > Ct of these samples is used as reference, so that delta-delta-Ct > values for each case sample can be directly interpreted as log2(FC) ). > My function isn't packaged or documented, but if it might be useful > to you I'd be glad to help. > >> ==> *As far as scaling ranking is concerned, we noticed that even if we >> specified the reference as pseudo mean or median, apparently nothing was >> changed in term of scaling factor and reference column (always the first >> sample by default). Is it correct? Where the normalization is influenced >> by the reference? >> >> In general, which is the logic of selecting one sample as a 'reference' >> in the normalization step with these methods ? > I think it is the same logic as described by Pfaffl. Delta-delta-Ct > values can be interpreted directly as log2(Fold Change) relative to > the reference sample. If you have primers with efficiencies other > than 100%, then it also corrects for differences in the number of > amplification cycles for a gene between samples. > > Hope this helps, > Levi > -- Alessandro Guffanti Alessandro Guffanti Head, Bioinformatics *Genomnia srl* Via Nerviano, 31/B -- 20020 Lainate (MI) Tel. +39-0293305.702 / Fax +39-0293305.777 www.genomnia.com <http: www.genomnia.com=""> alessandro.guffanti@genomnia.com <mailto:alessandro.guffanti@genomnia.com> *P* *Per cortesia, prima di stampare questa e-mail pensate all'ambiente.* * Please consider the environment before printing this mail note.* ----------------------------------------------------------- Il Contenuto del presente messaggio potrebbe contenere informazioni confidenziali a favore dei soli destinatari del messaggio stesso. Qualora riceviate per errore questo messaggio siete pregati di cancellarlo dalla memoria del computer e di contattare i numeri sopra indicati. Ogni utilizzo o ritrasmissione dei contenuti del messaggio da parte di soggetti diversi dai destinatari è da considerarsi vietato ed abusivo. The information transmitted is intended only for the per...{{dropped:10}}
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A t-test on the delta-delta-Ct values is reasonable. However if you have many more genes than samples, you might instead want to use the modified t-test implemented in the limma Bioconductor package. Follow its documentation for "single channel designs" and note that lmFit() will also accept matrix objects if you prefer not to create an ExpressionSet object. -Levi On Tue, Nov 12, 2013 at 1:10 PM, alessandro.guffanti@genomnia.com < alessandro.guffanti@genomnia.com> wrote: > Thanks a lot Levi! this is enlightening and we will use your function > on our datasets, we will let you know how this goes. > > Just a final question, excuse me the sheer ignorance: if I want to > associate > a statistics to each final value (which is indeed the big added vakue of > HTqPCR), > of course in the case when I am a comparing a first group of samples vs a > second > group of samples, which could be a reasonable approach ? a simple rowwise > t-test ? > > Keep in touch > > Alessandro > > > On 11/12/2013 7:04 PM, Levi Waldron wrote: > > Dear Alessandro & Elena, > > I have not used this package but have used the kinds of qPCR > normalization methods you are talking about and can offer some > comment, below. > > On Mon, Nov 11, 2013 at 8:17 AM, alessandro.guffanti@genomnia.com<alessandro.guffanti@genomnia.com> <alessandro.guffanti@genomnia.com> wrote: > > Now, this was our initial doubt and still persists: why a normalization > method which should operate an average Ct value for each sample, and > scales all Ct values according to the ratio of these mean Ct values > across samples, should take as a reference one of the samples (the first > in the data matrix by default) and leave its values untouched ? > > I think that using average Ct value and a reference sample for HT > qRT-PCR should work as follows, if you assume 100% efficiency for all > primers: > > 1) for each sample, subtract the mean Ct value from the Ct value of each gene. > 2) for each gene, subtract the Ct value of that gene in the reference > sample. delta-delta-Ct for the reference sample is then zero for all > genes. > > This is the method of Pfaffl 2001 (PMID 11328886) but with mean Ct > value for the sample replacing use of a housekeeping gene. I wrote a > function a few years ago to do this, with options to specify the > efficiency of each primer or use one or more housekeeping genes, and > to specify one or more control samples (in which case the mean delta > Ct of these samples is used as reference, so that delta-delta-Ct > values for each case sample can be directly interpreted as log2(FC) ). > My function isn't packaged or documented, but if it might be useful > to you I'd be glad to help. > > > ==> *As far as scaling ranking is concerned, we noticed that even if we > specified the reference as pseudo mean or median, apparently nothing was > changed in term of scaling factor and reference column (always the first > sample by default). Is it correct? Where the normalization is influenced > by the reference? > > In general, which is the logic of selecting one sample as a 'reference' > in the normalization step with these methods ? > > I think it is the same logic as described by Pfaffl. Delta-delta- Ct > values can be interpreted directly as log2(Fold Change) relative to > the reference sample. If you have primers with efficiencies other > than 100%, then it also corrects for differences in the number of > amplification cycles for a gene between samples. > > Hope this helps, > Levi > > > > > -- > > Alessandro Guffanti > > Head, Bioinformatics > > *Genomnia srl* > > Via Nerviano, 31/B – 20020 Lainate (MI) > > Tel. +39-0293305.702 / Fax +39-0293305.777 > > www.genomnia.com > > alessandro.guffanti@genomnia.com > > *P* *Per cortesia, prima di stampare questa e-mail pensate all'ambiente.* > > * Please consider the environment before printing this mail > note.* > > ----------------------------------------------------------- > Il Contenuto del presente messaggio potrebbe contenere informazioni > confidenziali a favore dei > soli destinatari del messaggio stesso. Qualora riceviate per errore questo > messaggio siete pregati > di cancellarlo dalla memoria del computer e di contattare i numeri sopra > indicati. Ogni utilizzo o > ritrasmissione dei contenuti del messaggio da parte di soggetti diversi > dai destinatari è da > considerarsi vietato ed abusivo. > > The information transmitted is intended only for the p...{{dropped:26}}
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Perfect thanks - indeed we don't have so many genes but we could give a try also at the lmFit() function Many thanks again, Alessandro On 11/12/2013 7:40 PM, Levi Waldron wrote: > A t-test on the delta-delta-Ct values is reasonable. However if you > have many more genes than samples, you might instead want to use the > modified t-test implemented in the limma Bioconductor package. Follow > its documentation for "single channel designs" and note that lmFit() > will also accept matrix objects if you prefer not to create an > ExpressionSet object. > > -Levi > > > > On Tue, Nov 12, 2013 at 1:10 PM, alessandro.guffanti@genomnia.com > <mailto:alessandro.guffanti@genomnia.com> > <alessandro.guffanti@genomnia.com> <mailto:alessandro.guffanti@genomnia.com>> wrote: > > Thanks a lot Levi! this is enlightening and we will use your function > on our datasets, we will let you know how this goes. > > Just a final question, excuse me the sheer ignorance: if I want to > associate > a statistics to each final value (which is indeed the big added > vakue of HTqPCR), > of course in the case when I am a comparing a first group of > samples vs a second > group of samples, which could be a reasonable approach ? a simple > rowwise > t-test ? > > Keep in touch > > Alessandro > > > On 11/12/2013 7:04 PM, Levi Waldron wrote: >> Dear Alessandro & Elena, >> >> I have not used this package but have used the kinds of qPCR >> normalization methods you are talking about and can offer some >> comment, below. >> >> On Mon, Nov 11, 2013 at 8:17 AM,alessandro.guffanti@genomnia.com <mailto:alessandro.guffanti@genomnia.com> >> <alessandro.guffanti@genomnia.com> <mailto:alessandro.guffanti@genomnia.com> wrote: >>> Now, this was our initial doubt and still persists: why a normalization >>> method which should operate an average Ct value for each sample, and >>> scales all Ct values according to the ratio of these mean Ct values >>> across samples, should take as a reference one of the samples (the first >>> in the data matrix by default) and leave its values untouched ? >> I think that using average Ct value and a reference sample for HT >> qRT-PCR should work as follows, if you assume 100% efficiency for all >> primers: >> >> 1) for each sample, subtract the mean Ct value from the Ct value of each gene. >> 2) for each gene, subtract the Ct value of that gene in the reference >> sample. delta-delta-Ct for the reference sample is then zero for all >> genes. >> >> This is the method of Pfaffl 2001 (PMID 11328886) but with mean Ct >> value for the sample replacing use of a housekeeping gene. I wrote a >> function a few years ago to do this, with options to specify the >> efficiency of each primer or use one or more housekeeping genes, and >> to specify one or more control samples (in which case the mean delta >> Ct of these samples is used as reference, so that delta-delta- Ct >> values for each case sample can be directly interpreted as log2(FC) ). >> My function isn't packaged or documented, but if it might be useful >> to you I'd be glad to help. >> >>> ==> *As far as scaling ranking is concerned, we noticed that even if we >>> specified the reference as pseudo mean or median, apparently nothing was >>> changed in term of scaling factor and reference column (always the first >>> sample by default). Is it correct? Where the normalization is influenced >>> by the reference? >>> >>> In general, which is the logic of selecting one sample as a 'reference' >>> in the normalization step with these methods ? >> I think it is the same logic as described by Pfaffl. Delta- delta-Ct >> values can be interpreted directly as log2(Fold Change) relative to >> the reference sample. If you have primers with efficiencies other >> than 100%, then it also corrects for differences in the number of >> amplification cycles for a gene between samples. >> >> Hope this helps, >> Levi >> > > > -- > > Alessandro Guffanti > > Head, Bioinformatics > > *Genomnia srl* > > Via Nerviano, 31/B – 20020 Lainate (MI) > > Tel. +39-0293305.702 <tel:%2b39-0293305.702> / Fax +39-0293305.777 > <tel:%2b39-0293305.777> > > www.genomnia.com <http: www.genomnia.com=""> > > alessandro.guffanti@genomnia.com > <mailto:alessandro.guffanti@genomnia.com> > > *P* *Per cortesia, prima di stampare questa e-mail pensate > all'ambiente.* > > * Please consider the environment before printing this > mail note.* > > > ----------------------------------------------------------- > Il Contenuto del presente messaggio potrebbe contenere > informazioni confidenziali a favore dei > soli destinatari del messaggio stesso. Qualora riceviate per > errore questo messaggio siete pregati > di cancellarlo dalla memoria del computer e di contattare i numeri > sopra indicati. Ogni utilizzo o > ritrasmissione dei contenuti del messaggio da parte di soggetti > diversi dai destinatari è da > considerarsi vietato ed abusivo. > > The information transmitted is intended only for the person or > entity to which it is addressed and > contains confidential and/or privileged material. Any review, > retransmission, dissemination or other > use of, or taking of any action in reliance upon, this information > by persons or entities other than > the intended recipient is prohibited. If you received this in > error, please contact the sender and > delete the material from any computer. > ----------------------------------------------------------- > > > > > -- > Levi Waldron > Assistant Professor of Biostatistics > Hunter College School of Urban Public Health > City University of New York > 2180 3rd Ave Rm 538 > New York NY 10035-4003 > 212-396-7747 <tel:212-396-7747> -- Alessandro Guffanti Alessandro Guffanti Head, Bioinformatics *Genomnia srl* Via Nerviano, 31/B – 20020 Lainate (MI) Tel. +39-0293305.702 / Fax +39-0293305.777 www.genomnia.com <http: www.genomnia.com=""> alessandro.guffanti@genomnia.com <mailto:alessandro.guffanti@genomnia.com> *P* *Per cortesia, prima di stampare questa e-mail pensate all'ambiente.* * Please consider the environment before printing this mail note.* ----------------------------------------------------------- Il Contenuto del presente messaggio potrebbe contenere informazioni confidenziali a favore dei soli destinatari del messaggio stesso. Qualora riceviate per errore questo messaggio siete pregati di cancellarlo dalla memoria del computer e di contattare i numeri sopra indicati. Ogni utilizzo o ritrasmissione dei contenuti del messaggio da parte di soggetti diversi dai destinatari è da considerarsi vietato ed abusivo. The information transmitted is intended only for the per...{{dropped:10}}
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