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. 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