gProcessed Signal normalization using cyclic loess
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viritha kaza ▴ 580
@viritha-kaza-4318
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Hi Group, I am interested in performing normalization with the agilent data -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature Number version).The gProcessed signal has been deposited as a series matrix file in Geo. I wanted to know if one can directly normalize gProcossed signal or need any other parameters from feature extraction file before one can perform cyclic loess normalization? Thank you in advance, Viritha [[alternative HTML version deleted]]
Microarray Normalization Microarray Normalization • 1.1k views
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@martin-morgan-1513
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On 12/06/2010 01:44 PM, viritha kaza wrote: > Hi Group, > I am interested in performing normalization with the agilent data > -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature > Number version).The gProcessed signal has been deposited as a series > matrix file in Geo. I wanted to know if one can directly normalize > gProcossed signal or need any other parameters from feature extraction > file before one can perform cyclic loess normalization? > Thank you in advance, > Viritha Please ask on the Bioconductor mailing list https://stat.ethz.ch/mailman/listinfo/bioconductor -- Computational Biology Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: M1-B861 Telephone: 206 667-2793
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Dear Viritha: I think you can normalize the gProcessed signal directly. Also there are ~100 negative control genes on this microarray platform which might be useful for the background correction and normalization. The nec() function in limma can use these negative controls to perform a normexp background correction. After that, you will normalize your data using cyclic loess method or the quantile method. Hope this helps. Cheers, Wei On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: > On 12/06/2010 01:44 PM, viritha kaza wrote: >> Hi Group, >> I am interested in performing normalization with the agilent data >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature >> Number version).The gProcessed signal has been deposited as a series >> matrix file in Geo. I wanted to know if one can directly normalize >> gProcossed signal or need any other parameters from feature extraction >> file before one can perform cyclic loess normalization? >> Thank you in advance, >> Viritha > > Please ask on the Bioconductor mailing list > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > -- > Computational Biology > Fred Hutchinson Cancer Research Center > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 > > Location: M1-B861 > Telephone: 206 667-2793 > > _______________________________________________ > 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 ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:6}}
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On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: > Dear Viritha: > > I think you can normalize the gProcessed signal directly. > > Also there are ~100 negative control genes on this microarray > platform which might be useful for the background correction and > normalization. The nec() function in limma can use these negative controls > to perform a normexp background correction. After that, you will normalize > your data using cyclic loess method or the quantile method. > > Hi, Wei. I may be mistaken, but I think the gProcessedSignal is already 2D- loess background-corrected by the Agilent FE software. Sean > Hope this helps. > > > Cheers, > Wei > > On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: > > > On 12/06/2010 01:44 PM, viritha kaza wrote: > >> Hi Group, > >> I am interested in performing normalization with the agilent data > >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature > >> Number version).The gProcessed signal has been deposited as a series > >> matrix file in Geo. I wanted to know if one can directly normalize > >> gProcossed signal or need any other parameters from feature extraction > >> file before one can perform cyclic loess normalization? > >> Thank you in advance, > >> Viritha > > > > Please ask on the Bioconductor mailing list > > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > -- > > Computational Biology > > Fred Hutchinson Cancer Research Center > > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 > > > > Location: M1-B861 > > Telephone: 206 667-2793 > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@r-project.org > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:13}}
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Hi Sean: If my understanding is correct, the gProcessedSignal is a locally background corrected signal in that for each spot on the array the background intensity is used to correct the foreground intensity. But it is still possible that the background intensities are not completely removed. The negative controls on the array might be useful for checking this and further background correcting the data. Wei On Dec 7, 2010, at 10:03 AM, Sean Davis wrote: > > > On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: > Dear Viritha: > > I think you can normalize the gProcessed signal directly. > > Also there are ~100 negative control genes on this microarray platform which might be useful for the background correction and normalization. The nec() function in limma can use these negative controls to perform a normexp background correction. After that, you will normalize your data using cyclic loess method or the quantile method. > > > Hi, Wei. > > I may be mistaken, but I think the gProcessedSignal is already 2D- loess background-corrected by the Agilent FE software. > > Sean > > Hope this helps. > > > Cheers, > Wei > > On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: > > > On 12/06/2010 01:44 PM, viritha kaza wrote: > >> Hi Group, > >> I am interested in performing normalization with the agilent data > >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature > >> Number version).The gProcessed signal has been deposited as a series > >> matrix file in Geo. I wanted to know if one can directly normalize > >> gProcossed signal or need any other parameters from feature extraction > >> file before one can perform cyclic loess normalization? > >> Thank you in advance, > >> Viritha > > > > Please ask on the Bioconductor mailing list > > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > -- > > Computational Biology > > Fred Hutchinson Cancer Research Center > > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 > > > > Location: M1-B861 > > Telephone: 206 667-2793 > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@r-project.org > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:20}}
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On Mon, Dec 6, 2010 at 6:15 PM, Wei Shi <shi@wehi.edu.au> wrote: > Hi Sean: > > If my understanding is correct, the gProcessedSignal is a locally > background corrected signal in that for each spot on the array the > background intensity is used to correct the foreground intensity. But it is > still possible that the background intensities are not completely removed. > The negative controls on the array might be useful for checking this and > further background correcting the data. > > Good point. Sean > Wei > > On Dec 7, 2010, at 10:03 AM, Sean Davis wrote: > > > > > > > On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: > > Dear Viritha: > > > > I think you can normalize the gProcessed signal directly. > > > > Also there are ~100 negative control genes on this microarray > platform which might be useful for the background correction and > normalization. The nec() function in limma can use these negative controls > to perform a normexp background correction. After that, you will normalize > your data using cyclic loess method or the quantile method. > > > > > > Hi, Wei. > > > > I may be mistaken, but I think the gProcessedSignal is already 2D- loess > background-corrected by the Agilent FE software. > > > > Sean > > > > Hope this helps. > > > > > > Cheers, > > Wei > > > > On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: > > > > > On 12/06/2010 01:44 PM, viritha kaza wrote: > > >> Hi Group, > > >> I am interested in performing normalization with the agilent data > > >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature > > >> Number version).The gProcessed signal has been deposited as a series > > >> matrix file in Geo. I wanted to know if one can directly normalize > > >> gProcossed signal or need any other parameters from feature extraction > > >> file before one can perform cyclic loess normalization? > > >> Thank you in advance, > > >> Viritha > > > > > > Please ask on the Bioconductor mailing list > > > > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > > > -- > > > Computational Biology > > > Fred Hutchinson Cancer Research Center > > > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 > > > > > > Location: M1-B861 > > > Telephone: 206 667-2793 > > > > > > _______________________________________________ > > > Bioconductor mailing list > > > Bioconductor@r-project.org > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > > > ______________________________________________________________________ > > The information in this email is confidential and inte...{{dropped:20}} > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]
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Hi Wei, I am new to r statistics.So how do I check for negative controls on the array for background correcting? I also wanted to know how I can get geometric mean of controls and what is Processed microarray data? I am asking this because in a paper I saw in Methods section:- "Microarray Data Analysis.Processed microarray data were log2-transformed and cyclic loess normalized (72), and then expressed as the difference of log of gProcessed Signal (Agilent Feature Extraction) and log of geometric mean of controls." Later SAM analysis was used for identifying differencially expressed genes. This was also agilent data with the same feature extraction version. Waiting for your reply, Thanks, Viritha On Mon, Dec 6, 2010 at 6:15 PM, Wei Shi <shi@wehi.edu.au> wrote: > Hi Sean: > > If my understanding is correct, the gProcessedSignal is a locally > background corrected signal in that for each spot on the array the > background intensity is used to correct the foreground intensity. But it is > still possible that the background intensities are not completely removed. > The negative controls on the array might be useful for checking this and > further background correcting the data. > > Wei > > On Dec 7, 2010, at 10:03 AM, Sean Davis wrote: > > > > On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: > >> Dear Viritha: >> >> I think you can normalize the gProcessed signal directly. >> >> Also there are ~100 negative control genes on this microarray >> platform which might be useful for the background correction and >> normalization. The nec() function in limma can use these negative controls >> to perform a normexp background correction. After that, you will normalize >> your data using cyclic loess method or the quantile method. >> >> > Hi, Wei. > > I may be mistaken, but I think the gProcessedSignal is already 2D- loess > background-corrected by the Agilent FE software. > > Sean > > >> Hope this helps. >> >> >> Cheers, >> Wei >> >> On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: >> >> > On 12/06/2010 01:44 PM, viritha kaza wrote: >> >> Hi Group, >> >> I am interested in performing normalization with the agilent data >> >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature >> >> Number version).The gProcessed signal has been deposited as a series >> >> matrix file in Geo. I wanted to know if one can directly normalize >> >> gProcossed signal or need any other parameters from feature extraction >> >> file before one can perform cyclic loess normalization? >> >> Thank you in advance, >> >> Viritha >> > >> > Please ask on the Bioconductor mailing list >> > >> > https://stat.ethz.ch/mailman/listinfo/bioconductor >> > >> > -- >> > Computational Biology >> > Fred Hutchinson Cancer Research Center >> > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 >> > >> > Location: M1-B861 >> > Telephone: 206 667-2793 >> > >> > _______________________________________________ >> > Bioconductor mailing list >> > Bioconductor@r-project.org >> > https://stat.ethz.ch/mailman/listinfo/bioconductor >> > Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> >> ______________________________________________________________________ >> The information in this email is confidential and intend...{{dropped:6}} >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:10}}
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Hi Viritha: The ControlType column in your data file gives the type of probes on the array. The negative controls have a value of -1. Presumably, negative control intensities should have a normal distribution. You might use a histogram or a density plot to visually inspect if these controls are normally distributed. You will have to calculate the geometric mean of controls by yourself after you retrieve the control data. This wiki page tells you how to calculate the geometric mean: http://en.wikipedia.org/wiki/Geometric_mean Hope this helps. Cheers, Wei On Dec 18, 2010, at 7:51 AM, viritha kaza wrote: > Hi Wei, > I am new to r statistics.So how do I check for negative controls on the array for background correcting? I also wanted to know how I can get geometric mean of controls and what is Processed microarray data? I am asking this because in a paper I saw in Methods section:- > > "Microarray Data Analysis.Processed microarray data were log2-transformed and cyclic loess normalized (72), and then expressed as the difference of log of gProcessed Signal (Agilent Feature Extraction) and log of geometric mean of controls." > Later SAM analysis was used for identifying differencially expressed genes. > This was also agilent data with the same feature extraction version. > > Waiting for your reply, > > Thanks, > > Viritha > > > On Mon, Dec 6, 2010 at 6:15 PM, Wei Shi <shi@wehi.edu.au> wrote: > Hi Sean: > > If my understanding is correct, the gProcessedSignal is a locally background corrected signal in that for each spot on the array the background intensity is used to correct the foreground intensity. But it is still possible that the background intensities are not completely removed. The negative controls on the array might be useful for checking this and further background correcting the data. > > Wei > > On Dec 7, 2010, at 10:03 AM, Sean Davis wrote: > >> >> >> On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: >> Dear Viritha: >> >> I think you can normalize the gProcessed signal directly. >> >> Also there are ~100 negative control genes on this microarray platform which might be useful for the background correction and normalization. The nec() function in limma can use these negative controls to perform a normexp background correction. After that, you will normalize your data using cyclic loess method or the quantile method. >> >> >> Hi, Wei. >> >> I may be mistaken, but I think the gProcessedSignal is already 2D- loess background-corrected by the Agilent FE software. >> >> Sean >> >> Hope this helps. >> >> >> Cheers, >> Wei >> >> On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: >> >> > On 12/06/2010 01:44 PM, viritha kaza wrote: >> >> Hi Group, >> >> I am interested in performing normalization with the agilent data >> >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature >> >> Number version).The gProcessed signal has been deposited as a series >> >> matrix file in Geo. I wanted to know if one can directly normalize >> >> gProcossed signal or need any other parameters from feature extraction >> >> file before one can perform cyclic loess normalization? >> >> Thank you in advance, >> >> Viritha >> > >> > Please ask on the Bioconductor mailing list >> > >> > https://stat.ethz.ch/mailman/listinfo/bioconductor >> > >> > -- >> > Computational Biology >> > Fred Hutchinson Cancer Research Center >> > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 >> > >> > Location: M1-B861 >> > Telephone: 206 667-2793 >> > >> > _______________________________________________ >> > Bioconductor mailing list >> > Bioconductor@r-project.org >> > https://stat.ethz.ch/mailman/listinfo/bioconductor >> > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> >> ______________________________________________________________________ >> The information in this email is confidential and intend...{{dropped:6}} >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:17}}
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Hi Wei, Thanks for your reply!!!.I understood which are negative controls, but I am not sure which intensity you are talking about is it the gprocessed signal of these negative controls? The geometric mean of control here means the geometric mean of the gProcessed Signal of the control samples in the microrray experiment or the geometric mean of controls(negative control probe) intensities? waiting for ur reply, Thanks, Viritha On Sun, Dec 19, 2010 at 6:33 PM, Wei Shi <shi@wehi.edu.au> wrote: > Hi Viritha: > > The ControlType column in your data file gives the type of probes on the > array. The negative controls have a value of -1. > > Presumably, negative control intensities should have a normal distribution. > You might use a histogram or a density plot to visually inspect if these > controls are normally distributed. > > You will have to calculate the geometric mean of controls by yourself after > you retrieve the control data. This wiki page tells you how to calculate the > geometric mean: http://en.wikipedia.org/wiki/Geometric_mean > > Hope this helps. > > Cheers, > Wei > > On Dec 18, 2010, at 7:51 AM, viritha kaza wrote: > > Hi Wei, > I am new to r statistics.So how do I check for negative controls on the > array for background correcting? I also wanted to know how I can get > geometric mean of controls and what is Processed microarray data? I am > asking this because in a paper I saw in Methods section:- > > "Microarray Data Analysis.Processed microarray data were log2-transformed > and cyclic loess normalized (72), and then expressed as the difference of > log of gProcessed Signal (Agilent Feature Extraction) and log of geometric > mean of controls." > Later SAM analysis was used for identifying differencially expressed genes. > > This was also agilent data with the same feature extraction version. > > Waiting for your reply, > > Thanks, > > Viritha > > On Mon, Dec 6, 2010 at 6:15 PM, Wei Shi <shi@wehi.edu.au> wrote: > >> Hi Sean: >> >> If my understanding is correct, the gProcessedSignal is a locally >> background corrected signal in that for each spot on the array the >> background intensity is used to correct the foreground intensity. But it is >> still possible that the background intensities are not completely removed. >> The negative controls on the array might be useful for checking this and >> further background correcting the data. >> >> Wei >> >> On Dec 7, 2010, at 10:03 AM, Sean Davis wrote: >> >> >> >> On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: >> >>> Dear Viritha: >>> >>> I think you can normalize the gProcessed signal directly. >>> >>> Also there are ~100 negative control genes on this microarray >>> platform which might be useful for the background correction and >>> normalization. The nec() function in limma can use these negative controls >>> to perform a normexp background correction. After that, you will normalize >>> your data using cyclic loess method or the quantile method. >>> >>> >> Hi, Wei. >> >> I may be mistaken, but I think the gProcessedSignal is already 2D- loess >> background-corrected by the Agilent FE software. >> >> Sean >> >> >>> Hope this helps. >>> >>> >>> Cheers, >>> Wei >>> >>> On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: >>> >>> > On 12/06/2010 01:44 PM, viritha kaza wrote: >>> >> Hi Group, >>> >> I am interested in performing normalization with the agilent data >>> >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature >>> >> Number version).The gProcessed signal has been deposited as a series >>> >> matrix file in Geo. I wanted to know if one can directly normalize >>> >> gProcossed signal or need any other parameters from feature extraction >>> >> file before one can perform cyclic loess normalization? >>> >> Thank you in advance, >>> >> Viritha >>> > >>> > Please ask on the Bioconductor mailing list >>> > >>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>> > >>> > -- >>> > Computational Biology >>> > Fred Hutchinson Cancer Research Center >>> > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 >>> > >>> > Location: M1-B861 >>> > Telephone: 206 667-2793 >>> > >>> > _______________________________________________ >>> > Bioconductor mailing list >>> > Bioconductor@r-project.org >>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>> > Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >>> >>> ______________________________________________________________________ >>> The information in this email is confidential and intend...{{dropped:6}} >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor@r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> >> >> >> ______________________________________________________________________ >> 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:10}}
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Hi Viritha: Yes, I'm talking about the gprocessed signal of the negative controls. As for the geometric mean of controls, my understanding is that that paper used geometric mean of the negative control probes, although I do not think this is the best way to analyze this type of data. Cheers, Wei On Dec 21, 2010, at 3:01 AM, viritha kaza wrote: > Hi Wei, > Thanks for your reply!!!.I understood which are negative controls, but I am not sure which intensity you are talking about is it the gprocessed signal of these negative controls? > The geometric mean of control here means the geometric mean of the gProcessed Signal of the control samples in the microrray experiment or the geometric mean of controls(negative control probe) intensities? > waiting for ur reply, > Thanks, > Viritha > > On Sun, Dec 19, 2010 at 6:33 PM, Wei Shi <shi@wehi.edu.au> wrote: > Hi Viritha: > > The ControlType column in your data file gives the type of probes on the array. The negative controls have a value of -1. > > Presumably, negative control intensities should have a normal distribution. You might use a histogram or a density plot to visually inspect if these controls are normally distributed. > > You will have to calculate the geometric mean of controls by yourself after you retrieve the control data. This wiki page tells you how to calculate the geometric mean: http://en.wikipedia.org/wiki/Geometric_mean > > Hope this helps. > > Cheers, > Wei > > On Dec 18, 2010, at 7:51 AM, viritha kaza wrote: > >> Hi Wei, >> I am new to r statistics.So how do I check for negative controls on the array for background correcting? I also wanted to know how I can get geometric mean of controls and what is Processed microarray data? I am asking this because in a paper I saw in Methods section:- >> >> "Microarray Data Analysis.Processed microarray data were log2-transformed and cyclic loess normalized (72), and then expressed as the difference of log of gProcessed Signal (Agilent Feature Extraction) and log of geometric mean of controls." >> Later SAM analysis was used for identifying differencially expressed genes. >> This was also agilent data with the same feature extraction version. >> >> Waiting for your reply, >> >> Thanks, >> >> Viritha >> >> >> On Mon, Dec 6, 2010 at 6:15 PM, Wei Shi <shi@wehi.edu.au> wrote: >> Hi Sean: >> >> If my understanding is correct, the gProcessedSignal is a locally background corrected signal in that for each spot on the array the background intensity is used to correct the foreground intensity. But it is still possible that the background intensities are not completely removed. The negative controls on the array might be useful for checking this and further background correcting the data. >> >> Wei >> >> On Dec 7, 2010, at 10:03 AM, Sean Davis wrote: >> >>> >>> >>> On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: >>> Dear Viritha: >>> >>> I think you can normalize the gProcessed signal directly. >>> >>> Also there are ~100 negative control genes on this microarray platform which might be useful for the background correction and normalization. The nec() function in limma can use these negative controls to perform a normexp background correction. After that, you will normalize your data using cyclic loess method or the quantile method. >>> >>> >>> Hi, Wei. >>> >>> I may be mistaken, but I think the gProcessedSignal is already 2D- loess background-corrected by the Agilent FE software. >>> >>> Sean >>> >>> Hope this helps. >>> >>> >>> Cheers, >>> Wei >>> >>> On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: >>> >>> > On 12/06/2010 01:44 PM, viritha kaza wrote: >>> >> Hi Group, >>> >> I am interested in performing normalization with the agilent data >>> >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature >>> >> Number version).The gProcessed signal has been deposited as a series >>> >> matrix file in Geo. I wanted to know if one can directly normalize >>> >> gProcossed signal or need any other parameters from feature extraction >>> >> file before one can perform cyclic loess normalization? >>> >> Thank you in advance, >>> >> Viritha >>> > >>> > Please ask on the Bioconductor mailing list >>> > >>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>> > >>> > -- >>> > Computational Biology >>> > Fred Hutchinson Cancer Research Center >>> > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 >>> > >>> > Location: M1-B861 >>> > Telephone: 206 667-2793 >>> > >>> > _______________________________________________ >>> > Bioconductor mailing list >>> > Bioconductor@r-project.org >>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>> > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >>> >>> ______________________________________________________________________ >>> The information in this email is confidential and intend...{{dropped:6}} >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor@r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> >> >> ______________________________________________________________________ >> 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:17}}
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Hi Wei, Then what about the processed microarray data that they are referring to? Is it the difference of the log of gprocessed data and geometric mean of control or any other parameter? Thanks, Viritha On Mon, Dec 20, 2010 at 5:30 PM, Wei Shi <shi@wehi.edu.au> wrote: > Hi Viritha: > > Yes, I'm talking about the gprocessed signal of the negative controls. > > As for the geometric mean of controls, my understanding is that that paper > used geometric mean of the negative control probes, although I do not think > this is the best way to analyze this type of data. > > Cheers, > Wei > > On Dec 21, 2010, at 3:01 AM, viritha kaza wrote: > > Hi Wei, > Thanks for your reply!!!.I understood which are negative controls, but I am > not sure which intensity you are talking about is it the gprocessed signal > of these negative controls? > The geometric mean of control here means the geometric mean of the > gProcessed Signal of the control samples in the microrray experiment or the > geometric mean of controls(negative control probe) intensities? > waiting for ur reply, > Thanks, > Viritha > > On Sun, Dec 19, 2010 at 6:33 PM, Wei Shi <shi@wehi.edu.au> wrote: > >> Hi Viritha: >> >> The ControlType column in your data file gives the type of probes on the >> array. The negative controls have a value of -1. >> >> Presumably, negative control intensities should have a normal >> distribution. You might use a histogram or a density plot to visually >> inspect if these controls are normally distributed. >> >> You will have to calculate the geometric mean of controls by yourself >> after you retrieve the control data. This wiki page tells you how to >> calculate the geometric mean: >> http://en.wikipedia.org/wiki/Geometric_mean >> >> Hope this helps. >> >> Cheers, >> Wei >> >> On Dec 18, 2010, at 7:51 AM, viritha kaza wrote: >> >> Hi Wei, >> I am new to r statistics.So how do I check for negative controls on the >> array for background correcting? I also wanted to know how I can get >> geometric mean of controls and what is Processed microarray data? I am >> asking this because in a paper I saw in Methods section:- >> >> "Microarray Data Analysis.Processed microarray data were log2-transformed >> and cyclic loess normalized (72), and then expressed as the difference of >> log of gProcessed Signal (Agilent Feature Extraction) and log of >> geometric mean of controls." >> Later SAM analysis was used for identifying differencially expressed >> genes. >> >> This was also agilent data with the same feature extraction version. >> >> Waiting for your reply, >> >> Thanks, >> >> Viritha >> >> On Mon, Dec 6, 2010 at 6:15 PM, Wei Shi <shi@wehi.edu.au> wrote: >> >>> Hi Sean: >>> >>> If my understanding is correct, the gProcessedSignal is a locally >>> background corrected signal in that for each spot on the array the >>> background intensity is used to correct the foreground intensity. But it is >>> still possible that the background intensities are not completely removed. >>> The negative controls on the array might be useful for checking this and >>> further background correcting the data. >>> >>> Wei >>> >>> On Dec 7, 2010, at 10:03 AM, Sean Davis wrote: >>> >>> >>> >>> On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: >>> >>>> Dear Viritha: >>>> >>>> I think you can normalize the gProcessed signal directly. >>>> >>>> Also there are ~100 negative control genes on this microarray >>>> platform which might be useful for the background correction and >>>> normalization. The nec() function in limma can use these negative controls >>>> to perform a normexp background correction. After that, you will normalize >>>> your data using cyclic loess method or the quantile method. >>>> >>>> >>> Hi, Wei. >>> >>> I may be mistaken, but I think the gProcessedSignal is already 2D- loess >>> background-corrected by the Agilent FE software. >>> >>> Sean >>> >>> >>>> Hope this helps. >>>> >>>> >>>> Cheers, >>>> Wei >>>> >>>> On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: >>>> >>>> > On 12/06/2010 01:44 PM, viritha kaza wrote: >>>> >> Hi Group, >>>> >> I am interested in performing normalization with the agilent data >>>> >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature >>>> >> Number version).The gProcessed signal has been deposited as a series >>>> >> matrix file in Geo. I wanted to know if one can directly normalize >>>> >> gProcossed signal or need any other parameters from feature >>>> extraction >>>> >> file before one can perform cyclic loess normalization? >>>> >> Thank you in advance, >>>> >> Viritha >>>> > >>>> > Please ask on the Bioconductor mailing list >>>> > >>>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> > >>>> > -- >>>> > Computational Biology >>>> > Fred Hutchinson Cancer Research Center >>>> > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 >>>> > >>>> > Location: M1-B861 >>>> > Telephone: 206 667-2793 >>>> > >>>> > _______________________________________________ >>>> > Bioconductor mailing list >>>> > Bioconductor@r-project.org >>>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> > Search the archives: >>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>> >>>> >>>> ______________________________________________________________________ >>>> The information in this email is confidential and intend...{{dropped:6}} >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor@r-project.org >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: >>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>> >>> >>> >>> >>> ______________________________________________________________________ >>> 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:10}}
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Hi Viritha: I think you are right. They should refer to the difference of the log of gprocessed data and log of geometric mean of gprocessed negative control probe intensities. The negative controls measured the background intensities and I guess what they did was just to subtract the probe intensities by the background intensity. I guess a lot of probes had negative values as a result of this subtraction. If you want to have a thorough understanding of how they performed the analysis, maybe it is better to ask them directly. Hope this helps. Cheers, Wei On Dec 21, 2010, at 11:44 AM, viritha kaza wrote: > Hi Wei, > Then what about the processed microarray data that they are referring to? Is it the difference of the log of gprocessed data and geometric mean of control or any other parameter? > Thanks, > Viritha > > On Mon, Dec 20, 2010 at 5:30 PM, Wei Shi <shi@wehi.edu.au> wrote: > Hi Viritha: > > Yes, I'm talking about the gprocessed signal of the negative controls. > > As for the geometric mean of controls, my understanding is that that paper used geometric mean of the negative control probes, although I do not think this is the best way to analyze this type of data. > > Cheers, > Wei > > On Dec 21, 2010, at 3:01 AM, viritha kaza wrote: > >> Hi Wei, >> Thanks for your reply!!!.I understood which are negative controls, but I am not sure which intensity you are talking about is it the gprocessed signal of these negative controls? >> The geometric mean of control here means the geometric mean of the gProcessed Signal of the control samples in the microrray experiment or the geometric mean of controls(negative control probe) intensities? >> waiting for ur reply, >> Thanks, >> Viritha >> >> On Sun, Dec 19, 2010 at 6:33 PM, Wei Shi <shi@wehi.edu.au> wrote: >> Hi Viritha: >> >> The ControlType column in your data file gives the type of probes on the array. The negative controls have a value of -1. >> >> Presumably, negative control intensities should have a normal distribution. You might use a histogram or a density plot to visually inspect if these controls are normally distributed. >> >> You will have to calculate the geometric mean of controls by yourself after you retrieve the control data. This wiki page tells you how to calculate the geometric mean: http://en.wikipedia.org/wiki/Geometric_mean >> >> Hope this helps. >> >> Cheers, >> Wei >> >> On Dec 18, 2010, at 7:51 AM, viritha kaza wrote: >> >>> Hi Wei, >>> I am new to r statistics.So how do I check for negative controls on the array for background correcting? I also wanted to know how I can get geometric mean of controls and what is Processed microarray data? I am asking this because in a paper I saw in Methods section:- >>> >>> "Microarray Data Analysis.Processed microarray data were log2-transformed and cyclic loess normalized (72), and then expressed as the difference of log of gProcessed Signal (Agilent Feature Extraction) and log of geometric mean of controls." >>> Later SAM analysis was used for identifying differencially expressed genes. >>> This was also agilent data with the same feature extraction version. >>> >>> Waiting for your reply, >>> >>> Thanks, >>> >>> Viritha >>> >>> >>> On Mon, Dec 6, 2010 at 6:15 PM, Wei Shi <shi@wehi.edu.au> wrote: >>> Hi Sean: >>> >>> If my understanding is correct, the gProcessedSignal is a locally background corrected signal in that for each spot on the array the background intensity is used to correct the foreground intensity. But it is still possible that the background intensities are not completely removed. The negative controls on the array might be useful for checking this and further background correcting the data. >>> >>> Wei >>> >>> On Dec 7, 2010, at 10:03 AM, Sean Davis wrote: >>> >>>> >>>> >>>> On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: >>>> Dear Viritha: >>>> >>>> I think you can normalize the gProcessed signal directly. >>>> >>>> Also there are ~100 negative control genes on this microarray platform which might be useful for the background correction and normalization. The nec() function in limma can use these negative controls to perform a normexp background correction. After that, you will normalize your data using cyclic loess method or the quantile method. >>>> >>>> >>>> Hi, Wei. >>>> >>>> I may be mistaken, but I think the gProcessedSignal is already 2D-loess background-corrected by the Agilent FE software. >>>> >>>> Sean >>>> >>>> Hope this helps. >>>> >>>> >>>> Cheers, >>>> Wei >>>> >>>> On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: >>>> >>>> > On 12/06/2010 01:44 PM, viritha kaza wrote: >>>> >> Hi Group, >>>> >> I am interested in performing normalization with the agilent data >>>> >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature >>>> >> Number version).The gProcessed signal has been deposited as a series >>>> >> matrix file in Geo. I wanted to know if one can directly normalize >>>> >> gProcossed signal or need any other parameters from feature extraction >>>> >> file before one can perform cyclic loess normalization? >>>> >> Thank you in advance, >>>> >> Viritha >>>> > >>>> > Please ask on the Bioconductor mailing list >>>> > >>>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> > >>>> > -- >>>> > Computational Biology >>>> > Fred Hutchinson Cancer Research Center >>>> > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 >>>> > >>>> > Location: M1-B861 >>>> > Telephone: 206 667-2793 >>>> > >>>> > _______________________________________________ >>>> > Bioconductor mailing list >>>> > Bioconductor@r-project.org >>>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >>>> >>>> >>>> ______________________________________________________________________ >>>> The information in this email is confidential and intend...{{dropped:6}} >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor@r-project.org >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >>>> >>> >>> >>> ______________________________________________________________________ >>> 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:17}}
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Hi Wei, I tried contacting, but since it is holiday season not getting response.Thanks for all the suggestions. Thanks, Viritha On Mon, Dec 20, 2010 at 7:51 PM, Wei Shi <shi@wehi.edu.au> wrote: > Hi Viritha: > > I think you are right. They should refer to the difference of the log of > gprocessed data and log of geometric mean of gprocessed negative control > probe intensities. The negative controls measured the background intensities > and I guess what they did was just to subtract the probe intensities by the > background intensity. I guess a lot of probes had negative values as a > result of this subtraction. If you want to have a thorough understanding of > how they performed the analysis, maybe it is better to ask them directly. > > Hope this helps. > > Cheers, > Wei > > On Dec 21, 2010, at 11:44 AM, viritha kaza wrote: > > Hi Wei, > Then what about the processed microarray data that they are referring to? > Is it the difference of the log of gprocessed data and geometric mean of > control or any other parameter? > Thanks, > Viritha > > On Mon, Dec 20, 2010 at 5:30 PM, Wei Shi <shi@wehi.edu.au> wrote: > >> Hi Viritha: >> >> Yes, I'm talking about the gprocessed signal of the negative controls. >> >> As for the geometric mean of controls, my understanding is that that paper >> used geometric mean of the negative control probes, although I do not think >> this is the best way to analyze this type of data. >> >> Cheers, >> Wei >> >> On Dec 21, 2010, at 3:01 AM, viritha kaza wrote: >> >> Hi Wei, >> Thanks for your reply!!!.I understood which are negative controls, but I >> am not sure which intensity you are talking about is it the gprocessed >> signal of these negative controls? >> The geometric mean of control here means the geometric mean of the >> gProcessed Signal of the control samples in the microrray experiment or the >> geometric mean of controls(negative control probe) intensities? >> waiting for ur reply, >> Thanks, >> Viritha >> >> On Sun, Dec 19, 2010 at 6:33 PM, Wei Shi <shi@wehi.edu.au> wrote: >> >>> Hi Viritha: >>> >>> The ControlType column in your data file gives the type of probes on the >>> array. The negative controls have a value of -1. >>> >>> Presumably, negative control intensities should have a normal >>> distribution. You might use a histogram or a density plot to visually >>> inspect if these controls are normally distributed. >>> >>> You will have to calculate the geometric mean of controls by yourself >>> after you retrieve the control data. This wiki page tells you how to >>> calculate the geometric mean: >>> http://en.wikipedia.org/wiki/Geometric_mean >>> >>> Hope this helps. >>> >>> Cheers, >>> Wei >>> >>> On Dec 18, 2010, at 7:51 AM, viritha kaza wrote: >>> >>> Hi Wei, >>> I am new to r statistics.So how do I check for negative controls on the >>> array for background correcting? I also wanted to know how I can get >>> geometric mean of controls and what is Processed microarray data? I am >>> asking this because in a paper I saw in Methods section:- >>> >>> "Microarray Data Analysis.Processed microarray data were log2-transformed >>> and cyclic loess normalized (72), and then expressed as the difference >>> of log of gProcessed Signal (Agilent Feature Extraction) and log of >>> geometric mean of controls." >>> Later SAM analysis was used for identifying differencially expressed >>> genes. >>> >>> This was also agilent data with the same feature extraction version. >>> >>> Waiting for your reply, >>> >>> Thanks, >>> >>> Viritha >>> >>> On Mon, Dec 6, 2010 at 6:15 PM, Wei Shi <shi@wehi.edu.au> wrote: >>> >>>> Hi Sean: >>>> >>>> If my understanding is correct, the gProcessedSignal is a locally >>>> background corrected signal in that for each spot on the array the >>>> background intensity is used to correct the foreground intensity. But it is >>>> still possible that the background intensities are not completely removed. >>>> The negative controls on the array might be useful for checking this and >>>> further background correcting the data. >>>> >>>> Wei >>>> >>>> On Dec 7, 2010, at 10:03 AM, Sean Davis wrote: >>>> >>>> >>>> >>>> On Mon, Dec 6, 2010 at 6:00 PM, Wei Shi <shi@wehi.edu.au> wrote: >>>> >>>>> Dear Viritha: >>>>> >>>>> I think you can normalize the gProcessed signal directly. >>>>> >>>>> Also there are ~100 negative control genes on this microarray >>>>> platform which might be useful for the background correction and >>>>> normalization. The nec() function in limma can use these negative controls >>>>> to perform a normexp background correction. After that, you will normalize >>>>> your data using cyclic loess method or the quantile method. >>>>> >>>>> >>>> Hi, Wei. >>>> >>>> I may be mistaken, but I think the gProcessedSignal is already 2D-loess >>>> background-corrected by the Agilent FE software. >>>> >>>> Sean >>>> >>>> >>>>> Hope this helps. >>>>> >>>>> >>>>> Cheers, >>>>> Wei >>>>> >>>>> On Dec 7, 2010, at 9:47 AM, Martin Morgan wrote: >>>>> >>>>> > On 12/06/2010 01:44 PM, viritha kaza wrote: >>>>> >> Hi Group, >>>>> >> I am interested in performing normalization with the agilent data >>>>> >> -Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature >>>>> >> Number version).The gProcessed signal has been deposited as a series >>>>> >> matrix file in Geo. I wanted to know if one can directly normalize >>>>> >> gProcossed signal or need any other parameters from feature >>>>> extraction >>>>> >> file before one can perform cyclic loess normalization? >>>>> >> Thank you in advance, >>>>> >> Viritha >>>>> > >>>>> > Please ask on the Bioconductor mailing list >>>>> > >>>>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>> > >>>>> > -- >>>>> > Computational Biology >>>>> > Fred Hutchinson Cancer Research Center >>>>> > 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 >>>>> > >>>>> > Location: M1-B861 >>>>> > Telephone: 206 667-2793 >>>>> > >>>>> > _______________________________________________ >>>>> > Bioconductor mailing list >>>>> > Bioconductor@r-project.org >>>>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>> > Search the archives: >>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>>> >>>>> >>>>> ______________________________________________________________________ >>>>> The information in this email is confidential and >>>>> intend...{{dropped:6}} >>>>> >>>>> _______________________________________________ >>>>> Bioconductor mailing list >>>>> Bioconductor@r-project.org >>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>> Search the archives: >>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>>> >>>> >>>> >>>> >>>> ______________________________________________________________________ >>>> 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:10}}
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