data transformation
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Rohit Ghai ▴ 10
@rohit-ghai-684
Last seen 10.2 years ago
hello all I am trying to perform VSN normalization with one-color Codelink chip data. after normalization in vsn i start getting negative values all over the expression matrix... this makes it difficult to get fold changes for the genes. how can i get around this problem ? I think its because the raw intensity values i get from codelink are a bit low. I also multiplied the raw data matrix by a factor of 2. With the trasnformed data matrix VSN did not give any negative values, but the boxplots looked a little upward shifted as compared to the non-trasnformed data. The final gene list that I got (genes with a standard deviation of more than 2 in any experiment) was the same both with the raw data and with the transformed data.. but I want to know if its alright to do this? any ideas would be appreciated Rohit
Normalization vsn codelink Normalization vsn codelink • 1.1k views
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@wolfgang-huber-3550
Last seen 12 weeks ago
EMBL European Molecular Biology Laborat…
Hi Rohit, the results of vsn are invariant under scaling, i.e. the result of vsn(x) is the same as that of vsn(1000*x) except for an overall additive offset of the transformed values by a value of log(1000). Since log-ratios (strictly speaking, "generalized log-ratios") are differences between transformed values, it makes no difference between the two calls. Hope this helps, Wolfgang Rohit Ghai wrote: > hello all > I am trying to perform VSN normalization with one-color Codelink chip data. > after normalization in vsn i start getting negative values all over the > expression > matrix... this makes it difficult to get fold changes for the genes. how > can i get > around this problem ? I think its because the raw intensity values i get > from codelink > are a bit low. I also multiplied the raw data matrix by a factor of 2. > With the trasnformed > data matrix VSN did not give any negative values, but the boxplots > looked a little upward shifted > as compared to the non-trasnformed data. The final gene list that I got > (genes with a standard > deviation of more than 2 in any experiment) was the same both with the > raw data and with the > transformed data.. but I want to know if its alright to do this? > any ideas would be appreciated > > Rohit > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor -- ------------------------------------- Wolfgang Huber Division of Molecular Genome Analysis German Cancer Research Center Heidelberg, Germany Phone: +49 6221 424709 Fax: +49 6221 42524709 Http: www.dkfz.de/abt0840/whuber
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