how to combine microarray data and phenotype data into a least squares analysis?
1
0
Entering edit mode
Martin Bonke ▴ 40
@martin-bonke-2901
Last seen 8.2 years ago
Hello everyone, I am looking for some help in setting up an analysis protocol in R for my microarray dataset. My knowledge of R is still somewhat rudimentary, but, having worked with it for about half a year now I do understand the basics and can get most of the packages that I've needed to work. However, the past week I've been stumped on a certain analysis that I would like to perform on my results. My dataset consists of microarrays of RNAi experiments that affect the cell cycle. Part of the results is a phenotypic analysis, where I have the percentages of cells in the different stages of the cell cycle. Now I would like to link this phenotypic data with the microarray data and find out whether the expression of genes is linked with a certain stage of the cell cycle. So, currently I have the matrix of all my microarray data, where the columns are the experiments, and the rows are the genes, and the values are their log-fold differences compared to wild type. I also have vectors that contain for each experiment the percentage of cells in a specific stage of the cell cycle (a vector for G1, one for G2, etc). Now I am quite at a loss on how to link these two together, I was suggested to use a least squares analysis and I've been trying make lsfit() work for my data, but so far without luck. The documentation with these functions generally is rather hard to understand for me and finding descriptive guides on how to do something like this has been very unsuccessful so far, probably because I am not really sure how this would be named. I hope that someone out here understands what I am trying to do and perhaps can give me a hint or two on what I should be looking into. Many thanks in advance. Martin [[alternative HTML version deleted]]
Microarray cycle Microarray cycle • 894 views
ADD COMMENT
0
Entering edit mode
@james-w-macdonald-5106
Last seen 23 hours ago
United States
Hi Martin, Martin Bonke wrote: > Hello everyone, > > > > I am looking for some help in setting up an analysis protocol in R for my > microarray dataset. My knowledge of R is still somewhat rudimentary, but, > having worked with it for about half a year now I do understand the basics > and can get most of the packages that I've needed to work. However, the past > week I've been stumped on a certain analysis that I would like to perform on > my results. > > > > My dataset consists of microarrays of RNAi experiments that affect the cell > cycle. Part of the results is a phenotypic analysis, where I have the > percentages of cells in the different stages of the cell cycle. Now I would > like to link this phenotypic data with the microarray data and find out > whether the expression of genes is linked with a certain stage of the cell > cycle. So, currently I have the matrix of all my microarray data, where the > columns are the experiments, and the rows are the genes, and the values are > their log-fold differences compared to wild type. I also have vectors that > contain for each experiment the percentage of cells in a specific stage of > the cell cycle (a vector for G1, one for G2, etc). > > > > Now I am quite at a loss on how to link these two together, I was suggested > to use a least squares analysis and I've been trying make lsfit() work for > my data, but so far without luck. The documentation with these functions > generally is rather hard to understand for me and finding descriptive guides > on how to do something like this has been very unsuccessful so far, probably > because I am not really sure how this would be named. I doubt you want to use lsfit() for this analysis. More likely you simply want to fit a linear model to your data. You could use lm() for this (note that the help page for lsfit() even told you that you probably want lm()), but it would be much easier to use limma. I'm not sure if there is an example of a 'standard' linear model in the limma User's Guide, but the only real difference between most of the examples there and what you will want to do is to use your cell cycle data directly rather than converting to factors first. Read the limma User's Guide and let us know if you still have questions. Best, Jim > > I hope that someone out here understands what I am trying to do and perhaps > can give me a hint or two on what I should be looking into. > > > > Many thanks in advance. > > Martin > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 -- James W. MacDonald, M.S. Biostatistician Hildebrandt Lab 8220D MSRB III 1150 W. Medical Center Drive Ann Arbor MI 48109-0646 734-936-8662
ADD COMMENT
0
Entering edit mode
Hi Martin, Hi Jim, as far as I remember, there was the time course example in the limma docu for using lm(). Benjamin -----Urspr?ngliche Nachricht----- Von: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] Im Auftrag von James W. MacDonald Gesendet: Tuesday, September 30, 2008 2:26 PM An: Martin Bonke Cc: bioconductor at stat.math.ethz.ch Betreff: Re: [BioC] how to combine microarray data and phenotype data into a least squares analysis? Hi Martin, Martin Bonke wrote: > Hello everyone, > > > > I am looking for some help in setting up an analysis protocol in R for my > microarray dataset. My knowledge of R is still somewhat rudimentary, but, > having worked with it for about half a year now I do understand the basics > and can get most of the packages that I've needed to work. However, the past > week I've been stumped on a certain analysis that I would like to perform on > my results. > > > > My dataset consists of microarrays of RNAi experiments that affect the cell > cycle. Part of the results is a phenotypic analysis, where I have the > percentages of cells in the different stages of the cell cycle. Now I would > like to link this phenotypic data with the microarray data and find out > whether the expression of genes is linked with a certain stage of the cell > cycle. So, currently I have the matrix of all my microarray data, where the > columns are the experiments, and the rows are the genes, and the values are > their log-fold differences compared to wild type. I also have vectors that > contain for each experiment the percentage of cells in a specific stage of > the cell cycle (a vector for G1, one for G2, etc). > > > > Now I am quite at a loss on how to link these two together, I was suggested > to use a least squares analysis and I've been trying make lsfit() work for > my data, but so far without luck. The documentation with these functions > generally is rather hard to understand for me and finding descriptive guides > on how to do something like this has been very unsuccessful so far, probably > because I am not really sure how this would be named. I doubt you want to use lsfit() for this analysis. More likely you simply want to fit a linear model to your data. You could use lm() for this (note that the help page for lsfit() even told you that you probably want lm()), but it would be much easier to use limma. I'm not sure if there is an example of a 'standard' linear model in the limma User's Guide, but the only real difference between most of the examples there and what you will want to do is to use your cell cycle data directly rather than converting to factors first. Read the limma User's Guide and let us know if you still have questions. Best, Jim > > I hope that someone out here understands what I am trying to do and perhaps > can give me a hint or two on what I should be looking into. > > > > Many thanks in advance. > > Martin > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 -- James W. MacDonald, M.S. Biostatistician Hildebrandt Lab 8220D MSRB III 1150 W. Medical Center Drive Ann Arbor MI 48109-0646 734-936-8662 _______________________________________________ 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 -- Pflichtangaben gem?? Gesetz ?ber elektronische Handelsregister und Genossenschaftsregister sowie das Unternehmensregister (EHUG): Universit?tsklinikum Hamburg-Eppendorf K?rperschaft des ?ffentlichen Rechts Gerichtsstand: Hamburg Vorstandsmitglieder: Prof. Dr. J?rg F. Debatin (Vorsitzender) Dr. Alexander Kirstein Ricarda Klein Prof. Dr. Dr. Uwe Koch-Gromus
ADD REPLY

Login before adding your answer.

Traffic: 610 users visited in the last hour
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