using genomic DNA as universal reference
3
0
Entering edit mode
Jianping Jin ▴ 890
@jianping-jin-1212
Last seen 10.2 years ago
Dear list, I would like to ask comments and suggestions on how to normalize microarray data with genomic DNA as reference. The experiments were performed with bacterial RNA and genomic DNA samples. What I noticed was that the data were pretty consistent across all chips on both channels. But there exists a huge difference between the two channels in terms of the distribution of the probe intensities, although the average intensities were the same for the both channels. T statistics with non-normalized data showed that there were two thirds probes with p values <= 0.05 by comparing the hybridization intensities between red and green channels. Regarding to the huge difference described above the normalization methods people usually use may not be appropriate for the RNA/DNA data sets. What normalization algorithms would be useful if there is any? Does anyone have experience with this? Any comments or suggestions will be appreciated! Jianping Jin ################################## Jianping Jin Ph.D. Bioinformatics scientist Center for Bioinformatics Room 3133 Bioinformatics building CB# 7104 University of Chapel Hill Chapel Hill, NC 27599 Phone: (919)843-6105 FAX: (919)843-3103 E-Mail: jjin at email.unc.edu
probe probe • 1.9k views
ADD COMMENT •
0
Entering edit mode
@sean-davis-490
Last seen 3 months ago
United States
On Thu, Jun 5, 2008 at 12:31 PM, Jianping Jin <jjin at="" email.unc.edu=""> wrote: > Dear list, > > I would like to ask comments and suggestions on how to normalize microarray > data with genomic DNA as reference. > > The experiments were performed with bacterial RNA and genomic DNA samples. > What I noticed was that the data were pretty consistent across all chips on > both channels. But there exists a huge difference between the two channels > in terms of the distribution of the probe intensities, although the average > intensities were the same for the both channels. T statistics with > non-normalized data showed that there were two thirds probes with p values > <= 0.05 by comparing the hybridization intensities between red and green > channels. > > Regarding to the huge difference described above the normalization methods > people usually use may not be appropriate for the RNA/DNA data sets. What > normalization algorithms would be useful if there is any? Does anyone have > experience with this? While not ideal, this sounds like a common reference design. You could make use of normal two-channel normalization methods (centering, linear, or loess, etc.), use only single-channel data (and ignore the control), or use some of the single-channel normalization methods for two channel data described in the limma user guide. I'm not sure that the t-test results are that important in making a decision. Others might have more insight and (more importantly) more experience in this situation. Sean
ADD COMMENT •
0
Entering edit mode
Thanks Sean for your input! the T-test result was used just for estimation of how many probe expressions were significantly different between RNA/DNA samples. This is also related to my normalization questions. According to my understanding the basic assumption for loess normalization is that most of the probes on the array are not differentially expressed. This is Agilent two-color data. Is loess normalization appropriate for such a different data on each array? thanks again! Jianping --On Thursday, June 05, 2008 1:28 PM -0400 Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > On Thu, Jun 5, 2008 at 12:31 PM, Jianping Jin <jjin at="" email.unc.edu=""> wrote: >> Dear list, >> >> I would like to ask comments and suggestions on how to normalize >> microarray data with genomic DNA as reference. >> >> The experiments were performed with bacterial RNA and genomic DNA >> samples. What I noticed was that the data were pretty consistent across >> all chips on both channels. But there exists a huge difference between >> the two channels in terms of the distribution of the probe intensities, >> although the average intensities were the same for the both channels. T >> statistics with non-normalized data showed that there were two thirds >> probes with p values <= 0.05 by comparing the hybridization intensities >> between red and green channels. >> >> Regarding to the huge difference described above the normalization >> methods people usually use may not be appropriate for the RNA/DNA data >> sets. What normalization algorithms would be useful if there is any? >> Does anyone have experience with this? > > While not ideal, this sounds like a common reference design. You > could make use of normal two-channel normalization methods (centering, > linear, or loess, etc.), use only single-channel data (and ignore the > control), or use some of the single-channel normalization methods for > two channel data described in the limma user guide. I'm not sure that > the t-test results are that important in making a decision. Others > might have more insight and (more importantly) more experience in this > situation. > > Sean ################################## Jianping Jin Ph.D. Bioinformatics scientist Center for Bioinformatics Room 3133 Bioinformatics building CB# 7104 University of Chapel Hill Chapel Hill, NC 27599 Phone: (919)843-6105 FAX: (919)843-3103 E-Mail: jjin at email.unc.edu
ADD REPLY •
0
Entering edit mode
Actually, the requirement for loess normalization is that differential expression is not dependent on expression level, and that up and down regulation are symmetric. --Naomi At 03:27 PM 6/5/2008, Jianping Jin wrote: >Thanks Sean for your input! > >the T-test result was used just for estimation of how many probe >expressions were significantly different between RNA/DNA samples. >This is also related to my normalization questions. According to my >understanding the basic assumption for loess normalization is that >most of the probes on the array are not differentially expressed. >This is Agilent two-color data. Is loess normalization appropriate >for such a different data on each array? > >thanks again! > >Jianping > >--On Thursday, June 05, 2008 1:28 PM -0400 Sean Davis ><sdavis2 at="" mail.nih.gov=""> wrote: > >>On Thu, Jun 5, 2008 at 12:31 PM, Jianping Jin <jjin at="" email.unc.edu=""> wrote: >>>Dear list, >>> >>>I would like to ask comments and suggestions on how to normalize >>>microarray data with genomic DNA as reference. >>> >>>The experiments were performed with bacterial RNA and genomic DNA >>>samples. What I noticed was that the data were pretty consistent across >>>all chips on both channels. But there exists a huge difference between >>>the two channels in terms of the distribution of the probe intensities, >>>although the average intensities were the same for the both channels. T >>>statistics with non-normalized data showed that there were two thirds >>>probes with p values <= 0.05 by comparing the hybridization intensities >>>between red and green channels. >>> >>>Regarding to the huge difference described above the normalization >>>methods people usually use may not be appropriate for the RNA/DNA data >>>sets. What normalization algorithms would be useful if there is any? >>>Does anyone have experience with this? >> >>While not ideal, this sounds like a common reference design. You >>could make use of normal two-channel normalization methods (centering, >>linear, or loess, etc.), use only single-channel data (and ignore the >>control), or use some of the single-channel normalization methods for >>two channel data described in the limma user guide. I'm not sure that >>the t-test results are that important in making a decision. Others >>might have more insight and (more importantly) more experience in this >>situation. >> >>Sean > > > >################################## >Jianping Jin Ph.D. >Bioinformatics scientist >Center for Bioinformatics >Room 3133 Bioinformatics building >CB# 7104 >University of Chapel Hill >Chapel Hill, NC 27599 >Phone: (919)843-6105 >FAX: (919)843-3103 >E-Mail: jjin at email.unc.edu > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111
ADD REPLY •
0
Entering edit mode
Thanks Naomi for your reply to correct me! I checked the data set on M values. The typical numbers are that 14K (even less) genes are larger than 0 and 28K genes are less than 0 before loess normalization. For this data set what you think about loess normalization? best, Jianping --On Thursday, June 05, 2008 4:06 PM -0400 Naomi Altman <naomi at="" stat.psu.edu=""> wrote: > Actually, the requirement for loess normalization is that differential > expression is not dependent on expression level, and that up and down > regulation are symmetric. > > --Naomi > > > At 03:27 PM 6/5/2008, Jianping Jin wrote: >> Thanks Sean for your input! >> >> the T-test result was used just for estimation of how many probe >> expressions were significantly different between RNA/DNA samples. >> This is also related to my normalization questions. According to my >> understanding the basic assumption for loess normalization is that >> most of the probes on the array are not differentially expressed. >> This is Agilent two-color data. Is loess normalization appropriate >> for such a different data on each array? >> >> thanks again! >> >> Jianping >> >> --On Thursday, June 05, 2008 1:28 PM -0400 Sean Davis >> <sdavis2 at="" mail.nih.gov=""> wrote: >> >>> On Thu, Jun 5, 2008 at 12:31 PM, Jianping Jin <jjin at="" email.unc.edu=""> >>> wrote: >>>> Dear list, >>>> >>>> I would like to ask comments and suggestions on how to normalize >>>> microarray data with genomic DNA as reference. >>>> >>>> The experiments were performed with bacterial RNA and genomic DNA >>>> samples. What I noticed was that the data were pretty consistent across >>>> all chips on both channels. But there exists a huge difference between >>>> the two channels in terms of the distribution of the probe intensities, >>>> although the average intensities were the same for the both channels. T >>>> statistics with non-normalized data showed that there were two thirds >>>> probes with p values <= 0.05 by comparing the hybridization intensities >>>> between red and green channels. >>>> >>>> Regarding to the huge difference described above the normalization >>>> methods people usually use may not be appropriate for the RNA/DNA data >>>> sets. What normalization algorithms would be useful if there is any? >>>> Does anyone have experience with this? >>> >>> While not ideal, this sounds like a common reference design. You >>> could make use of normal two-channel normalization methods (centering, >>> linear, or loess, etc.), use only single-channel data (and ignore the >>> control), or use some of the single-channel normalization methods for >>> two channel data described in the limma user guide. I'm not sure that >>> the t-test results are that important in making a decision. Others >>> might have more insight and (more importantly) more experience in this >>> situation. >>> >>> Sean >> >> >> >> ################################## >> Jianping Jin Ph.D. >> Bioinformatics scientist >> Center for Bioinformatics >> Room 3133 Bioinformatics building >> CB# 7104 >> University of Chapel Hill >> Chapel Hill, NC 27599 >> Phone: (919)843-6105 >> FAX: (919)843-3103 >> E-Mail: jjin at email.unc.edu >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > ################################## Jianping Jin Ph.D. Bioinformatics scientist Center for Bioinformatics Room 3133 Bioinformatics building CB# 7104 University of Chapel Hill Chapel Hill, NC 27599 Phone: (919)843-6105 FAX: (919)843-3103 E-Mail: jjin at email.unc.edu
ADD REPLY •
0
Entering edit mode
Actually, I do not think you can determine the suitability of loess normalization from a summary like this. You need to look at the MA plots and see if the expression values are evenly spread around the central trend. You might want to use hexbin to do the plots, because the central trend is usually very dense. Personally, I do a lot of graphics before doing any analysis of microarray data. --Naomi At 05:01 PM 6/5/2008, Jianping Jin wrote: >Thanks Naomi for your reply to correct me! > >I checked the data set on M values. The typical numbers are that 14K >(even less) genes are larger than 0 and 28K genes are less than 0 >before loess normalization. For this data set what you think about >loess normalization? > >best, > >Jianping > > >--On Thursday, June 05, 2008 4:06 PM -0400 Naomi Altman ><naomi at="" stat.psu.edu=""> wrote: > >>Actually, the requirement for loess normalization is that differential >>expression is not dependent on expression level, and that up and down >>regulation are symmetric. >> >>--Naomi >> >> >>At 03:27 PM 6/5/2008, Jianping Jin wrote: >>>Thanks Sean for your input! >>> >>>the T-test result was used just for estimation of how many probe >>>expressions were significantly different between RNA/DNA samples. >>>This is also related to my normalization questions. According to my >>>understanding the basic assumption for loess normalization is that >>>most of the probes on the array are not differentially expressed. >>>This is Agilent two-color data. Is loess normalization appropriate >>>for such a different data on each array? >>> >>>thanks again! >>> >>>Jianping >>> >>>--On Thursday, June 05, 2008 1:28 PM -0400 Sean Davis >>><sdavis2 at="" mail.nih.gov=""> wrote: >>> >>>>On Thu, Jun 5, 2008 at 12:31 PM, Jianping Jin <jjin at="" email.unc.edu=""> >>>>wrote: >>>>>Dear list, >>>>> >>>>>I would like to ask comments and suggestions on how to normalize >>>>>microarray data with genomic DNA as reference. >>>>> >>>>>The experiments were performed with bacterial RNA and genomic DNA >>>>>samples. What I noticed was that the data were pretty consistent across >>>>>all chips on both channels. But there exists a huge difference between >>>>>the two channels in terms of the distribution of the probe intensities, >>>>>although the average intensities were the same for the both channels. T >>>>>statistics with non-normalized data showed that there were two thirds >>>>>probes with p values <= 0.05 by comparing the hybridization intensities >>>>>between red and green channels. >>>>> >>>>>Regarding to the huge difference described above the normalization >>>>>methods people usually use may not be appropriate for the RNA/DNA data >>>>>sets. What normalization algorithms would be useful if there is any? >>>>>Does anyone have experience with this? >>>> >>>>While not ideal, this sounds like a common reference design. You >>>>could make use of normal two-channel normalization methods (centering, >>>>linear, or loess, etc.), use only single-channel data (and ignore the >>>>control), or use some of the single-channel normalization methods for >>>>two channel data described in the limma user guide. I'm not sure that >>>>the t-test results are that important in making a decision. Others >>>>might have more insight and (more importantly) more experience in this >>>>situation. >>>> >>>>Sean >>> >>> >>> >>>################################## >>>Jianping Jin Ph.D. >>>Bioinformatics scientist >>>Center for Bioinformatics >>>Room 3133 Bioinformatics building >>>CB# 7104 >>>University of Chapel Hill >>>Chapel Hill, NC 27599 >>>Phone: (919)843-6105 >>>FAX: (919)843-3103 >>>E-Mail: jjin at email.unc.edu >>> >>>_______________________________________________ >>>Bioconductor mailing list >>>Bioconductor at stat.math.ethz.ch >>>https://stat.ethz.ch/mailman/listinfo/bioconductor >>>Search the archives: >>>http://news.gmane.org/gmane.science.biology.informatics.conductor >> >>Naomi S. Altman 814-865-3791 (voice) >>Associate Professor >>Dept. of Statistics 814-863-7114 (fax) >>Penn State University 814-865-1348 (Statistics) >>University Park, PA 16802-2111 > > > >################################## >Jianping Jin Ph.D. >Bioinformatics scientist >Center for Bioinformatics >Room 3133 Bioinformatics building >CB# 7104 >University of Chapel Hill >Chapel Hill, NC 27599 >Phone: (919)843-6105 >FAX: (919)843-3103 >E-Mail: jjin at email.unc.edu > Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111
ADD REPLY •
0
Entering edit mode
@juan-c-oliveros-collazos-2665
Last seen 10.2 years ago
Dear Jin, I used to work with this kind of data in the past: RNA in one channel and genomic DNA (gDNA) in the other. We used the gDNA as a reference value for each gene to quantify the amount of DNA present in each spot. I also noticed that the distribution of the intensities was different in both types of samples. In fact this is expectable as the amount of mRNA molecules in one sample has nothing to do with the amount of gDNA for the same gene in other sample. So I normalized the data separately: -I created two tables, one with all RNA values and other with all gDNA values. -I adjusted the quantiles of each table separately. -Then I calculated the ratio RNA intensity / gDNA intensity and I used this ratio RNA/gDNA as the expression value of the genes. In further analysis steps I treated them as data coming from single channel hybridizations. I hope that helps. best, Juan Carlos Oliveros Head of BioinfoGP Unit at CNB-CSIC Madrid, Spain http://bioinfogp.cnb.csic.es > Dear list, > > I would like to ask comments and suggestions on how to normalize > microarray > data with genomic DNA as reference. > > The experiments were performed with bacterial RNA and genomic DNA samples. > What I noticed was that the data were pretty consistent across all chips > on > both channels. But there exists a huge difference between the two > channels > in terms of the distribution of the probe intensities, although the > average > intensities were the same for the both channels. T statistics with > non-normalized data showed that there were two thirds probes with p values > <= 0.05 by comparing the hybridization intensities between red and green > channels. > > Regarding to the huge difference described above the normalization methods > people usually use may not be appropriate for the RNA/DNA data sets. What > normalization algorithms would be useful if there is any? Does anyone have > experience with this? > > Any comments or suggestions will be appreciated! > > Jianping Jin > > > ################################## > Jianping Jin Ph.D. > Bioinformatics scientist > Center for Bioinformatics > Room 3133 Bioinformatics building > CB# 7104 > University of Chapel Hill > Chapel Hill, NC 27599 > Phone: (919)843-6105 > FAX: (919)843-3103 > E-Mail: jjin at email.unc.edu > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >
ADD COMMENT •
0
Entering edit mode
Dear Juan, Thanks for sharing your experience with me! It is helpful. So when you are comparing data sets of interest that are collected from different experiments or at different time your assumption is that gDNA empirical distribution should be the same. The method, like "normalizeBetweenArrays" with method=Gquantile, can be applied to normalize all arrays. Is that correct? best, Jianping --On Thursday, June 05, 2008 7:52 PM +0200 oliveros at cnb.csic.es wrote: > Dear Jin, > > I used to work with this kind of data in the past: RNA in one channel and > genomic DNA (gDNA) in the other. We used the gDNA as a reference value for > each gene to quantify the amount of DNA present in each spot. > > I also noticed that the distribution of the intensities was different in > both types of samples. In fact this is expectable as the amount of mRNA > molecules in one sample has nothing to do with the amount of gDNA for the > same gene in other sample. > > So I normalized the data separately: > > -I created two tables, one with all RNA values and other with all gDNA > values. > > -I adjusted the quantiles of each table separately. > > -Then I calculated the ratio RNA intensity / gDNA intensity and I used > this ratio RNA/gDNA as the expression value of the genes. In further > analysis steps I treated them as data coming from single channel > hybridizations. > > > I hope that helps. > > best, > > Juan Carlos Oliveros > Head of BioinfoGP Unit at CNB-CSIC > Madrid, Spain > http://bioinfogp.cnb.csic.es > > >> Dear list, >> >> I would like to ask comments and suggestions on how to normalize >> microarray >> data with genomic DNA as reference. >> >> The experiments were performed with bacterial RNA and genomic DNA >> samples. What I noticed was that the data were pretty consistent across >> all chips on >> both channels. But there exists a huge difference between the two >> channels >> in terms of the distribution of the probe intensities, although the >> average >> intensities were the same for the both channels. T statistics with >> non-normalized data showed that there were two thirds probes with p >> values <= 0.05 by comparing the hybridization intensities between red >> and green channels. >> >> Regarding to the huge difference described above the normalization >> methods people usually use may not be appropriate for the RNA/DNA data >> sets. What normalization algorithms would be useful if there is any? >> Does anyone have experience with this? >> >> Any comments or suggestions will be appreciated! >> >> Jianping Jin >> >> >> ################################## >> Jianping Jin Ph.D. >> Bioinformatics scientist >> Center for Bioinformatics >> Room 3133 Bioinformatics building >> CB# 7104 >> University of Chapel Hill >> Chapel Hill, NC 27599 >> Phone: (919)843-6105 >> FAX: (919)843-3103 >> E-Mail: jjin at email.unc.edu >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > ################################## Jianping Jin Ph.D. Bioinformatics scientist Center for Bioinformatics Room 3133 Bioinformatics building CB# 7104 University of Chapel Hill Chapel Hill, NC 27599 Phone: (919)843-6105 FAX: (919)843-3103 E-Mail: jjin at email.unc.edu
ADD REPLY •
0
Entering edit mode
Jin, Yes, I assume that gDNA distributions are comparable between arrays. I also assume that the RNA channel distributions are similar too. BUT I can not assume that both types are also similar (this is the main point). I am not sure that Gquantile will be correct here because that method modifies also the Red channel (in order to leave logRatios unchanged)... I just make two separate tables, one for each type of sample, and normalize them separately (I use normalize.quantiles from Affy package with each table). Regards, Juan Carlos Oliveros http://bioinfogp.cnb.csic.es > Dear Juan, > > Thanks for sharing your experience with me! It is helpful. > > So when you are comparing data sets of interest that are collected from > different experiments or at different time your assumption is that gDNA > empirical distribution should be the same. The method, like > "normalizeBetweenArrays" with method=Gquantile, can be applied to > normalize > all arrays. Is that correct? > > best, > > Jianping > > > --On Thursday, June 05, 2008 7:52 PM +0200 oliveros at cnb.csic.es wrote: > >> Dear Jin, >> >> I used to work with this kind of data in the past: RNA in one channel >> and >> genomic DNA (gDNA) in the other. We used the gDNA as a reference value >> for >> each gene to quantify the amount of DNA present in each spot. >> >> I also noticed that the distribution of the intensities was different in >> both types of samples. In fact this is expectable as the amount of mRNA >> molecules in one sample has nothing to do with the amount of gDNA for >> the >> same gene in other sample. >> >> So I normalized the data separately: >> >> -I created two tables, one with all RNA values and other with all gDNA >> values. >> >> -I adjusted the quantiles of each table separately. >> >> -Then I calculated the ratio RNA intensity / gDNA intensity and I used >> this ratio RNA/gDNA as the expression value of the genes. In further >> analysis steps I treated them as data coming from single channel >> hybridizations. >> >> >> I hope that helps. >> >> best, >> >> Juan Carlos Oliveros >> Head of BioinfoGP Unit at CNB-CSIC >> Madrid, Spain >> http://bioinfogp.cnb.csic.es >> >> >>> Dear list, >>> >>> I would like to ask comments and suggestions on how to normalize >>> microarray >>> data with genomic DNA as reference. >>> >>> The experiments were performed with bacterial RNA and genomic DNA >>> samples. What I noticed was that the data were pretty consistent across >>> all chips on >>> both channels. But there exists a huge difference between the two >>> channels >>> in terms of the distribution of the probe intensities, although the >>> average >>> intensities were the same for the both channels. T statistics with >>> non-normalized data showed that there were two thirds probes with p >>> values <= 0.05 by comparing the hybridization intensities between red >>> and green channels. >>> >>> Regarding to the huge difference described above the normalization >>> methods people usually use may not be appropriate for the RNA/DNA data >>> sets. What normalization algorithms would be useful if there is any? >>> Does anyone have experience with this? >>> >>> Any comments or suggestions will be appreciated! >>> >>> Jianping Jin >>> >>> >>> ################################## >>> Jianping Jin Ph.D. >>> Bioinformatics scientist >>> Center for Bioinformatics >>> Room 3133 Bioinformatics building >>> CB# 7104 >>> University of Chapel Hill >>> Chapel Hill, NC 27599 >>> Phone: (919)843-6105 >>> FAX: (919)843-3103 >>> E-Mail: jjin at email.unc.edu >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at stat.math.ethz.ch >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> >> > > > > ################################## > Jianping Jin Ph.D. > Bioinformatics scientist > Center for Bioinformatics > Room 3133 Bioinformatics building > CB# 7104 > University of Chapel Hill > Chapel Hill, NC 27599 > Phone: (919)843-6105 > FAX: (919)843-3103 > E-Mail: jjin at email.unc.edu > >
ADD REPLY •
0
Entering edit mode
@wolfgang-huber-3550
Last seen 12 weeks ago
EMBL European Molecular Biology Laborat…
Dear Jianping, I think the following paper (method is implemented in the function normalizeByReference of the tilingArray package) describes a setup very similar to yours (using Affymetrix 25mer tiling arrays and with yeast): Section 2.3 of Huber W., Toedling J., Steinmetz L. M. (2006) Transcript mapping with high-density oligonucleotide tiling arrays. Bioinformatics 22(16): 1963-1970. http://bioinformatics.oxfordjournals.org/cgi/reprint/22/16/1963.pdf Best wishes Wolfgang ------------------------------------------------------------------ Wolfgang Huber EBI/EMBL Cambridge UK http://www.ebi.ac.uk/huber 05/06/2008 17:31 Jianping Jin scripsit > Dear list, > > I would like to ask comments and suggestions on how to normalize > microarray data with genomic DNA as reference. > > The experiments were performed with bacterial RNA and genomic DNA > samples. What I noticed was that the data were pretty consistent across > all chips on both channels. But there exists a huge difference between > the two channels in terms of the distribution of the probe intensities, > although the average intensities were the same for the both channels. T > statistics with non-normalized data showed that there were two thirds > probes with p values <= 0.05 by comparing the hybridization intensities > between red and green channels. > > Regarding to the huge difference described above the normalization > methods people usually use may not be appropriate for the RNA/DNA data > sets. What normalization algorithms would be useful if there is any? > Does anyone have experience with this? > > Any comments or suggestions will be appreciated! > > Jianping Jin > > > ################################## > Jianping Jin Ph.D. > Bioinformatics scientist > Center for Bioinformatics > Room 3133 Bioinformatics building > CB# 7104 > University of Chapel Hill > Chapel Hill, NC 27599 > Phone: (919)843-6105 > FAX: (919)843-3103 > E-Mail: jjin at email.unc.edu > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD COMMENT •
0
Entering edit mode
Dear Wolfgang, It is an interesting model for DNA reference normalization. I will take a look at it in details. Thanks a lot! Jianping --On Friday, June 06, 2008 10:10 AM +0100 Wolfgang Huber <huber at="" ebi.ac.uk=""> wrote: > > Dear Jianping, > > I think the following paper (method is implemented in the function > normalizeByReference of the tilingArray package) describes a setup very > similar to yours (using Affymetrix 25mer tiling arrays and with yeast): > > Section 2.3 of Huber W., Toedling J., Steinmetz L. M. (2006) Transcript > mapping with high-density oligonucleotide tiling arrays. Bioinformatics > 22(16): 1963-1970. > http://bioinformatics.oxfordjournals.org/cgi/reprint/22/16/1963.pdf > > Best wishes > Wolfgang > > ------------------------------------------------------------------ > Wolfgang Huber EBI/EMBL Cambridge UK http://www.ebi.ac.uk/huber > > > 05/06/2008 17:31 Jianping Jin scripsit >> Dear list, >> >> I would like to ask comments and suggestions on how to normalize >> microarray data with genomic DNA as reference. >> >> The experiments were performed with bacterial RNA and genomic DNA >> samples. What I noticed was that the data were pretty consistent across >> all chips on both channels. But there exists a huge difference between >> the two channels in terms of the distribution of the probe intensities, >> although the average intensities were the same for the both channels. T >> statistics with non-normalized data showed that there were two thirds >> probes with p values <= 0.05 by comparing the hybridization intensities >> between red and green channels. >> >> Regarding to the huge difference described above the normalization >> methods people usually use may not be appropriate for the RNA/DNA data >> sets. What normalization algorithms would be useful if there is any? >> Does anyone have experience with this? >> >> Any comments or suggestions will be appreciated! >> >> Jianping Jin >> >> >> ################################## >> Jianping Jin Ph.D. >> Bioinformatics scientist >> Center for Bioinformatics >> Room 3133 Bioinformatics building >> CB# 7104 >> University of Chapel Hill >> Chapel Hill, NC 27599 >> Phone: (919)843-6105 >> FAX: (919)843-3103 >> E-Mail: jjin at email.unc.edu >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor ################################## Jianping Jin Ph.D. Bioinformatics scientist Center for Bioinformatics Room 3133 Bioinformatics building CB# 7104, Campus Phone: (919)843-6105 FAX: (919)843-3103 E-Mail: jjin at unc.edu
ADD REPLY •

Login before adding your answer.

Similar Posts
Loading Similar Posts
Traffic: 925 users visited in the last hour
Content Search
Users
Tags
Badges
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6