Sliding window t-test?
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Tim Smith ★ 1.1k
@tim-smith-1532
Last seen 7.9 years ago
Hi, I was trying to analyze some methylation (illumina 27k) data. I have data for 18 cancer and 18 normal samples. I want to find out if certain regions show consistently higher methylation in cancer (as compared to normal). A t-test (and then corrected for fdr) for individual probes does not reveal a statistically significant difference between samples. Is there a way I can use a sliding window approach to see if there are consistently differentially methylated regions? Is there a R and/or bioconductor package that does it? Or if not, which statistical methods would be most appropriate? Tim [[alternative HTML version deleted]]
Cancer Cancer • 1.5k views
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Julian Gehring ★ 1.3k
@julian-gehring-5818
Last seen 3.3 years ago
Hi Tim, the 'les' (Loci of Enhanced Significance) package could be suited for your analysis. In a first step you assess the effect between the groups for each probe with a statistical test (as you have already done with the t-test, but you could also use other tests). Then, the les package estimates the degree of significant probes within a sliding window along the genome. This is based on a non-parametric estimation of the distribution of the p-values, and hence you do not have to specify a threshold or correct for multiple testing. Finally, you can search for regions with a high degree of significant probes which would correspond to methylated regions. We have successfully used this to analyze methylation arrays while in principle it is suited for all kinds of tiling arrays. Another package applying a sliding window approach is 'rMAT', but it seems to be specific for Affymetrix tiling arrays. Best Julian
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@michal-okoniewski-2676
Last seen 7.9 years ago
Hi Tim, In the package rnaSeqMap we have implemented (also in C) the Aumann-Lindell algorithm, which is sort of slide-and-join algorithm on two windows across the chromosome and finds the regions even when there are some small gaps. The regions found by A-L are "irreducible" -see the paper on theorems of this property - and thus overcome some limitations of a single sliding window. Applying it to your data probably would require some workaround, but you may check rnaSeqMap vignette and paper and the original paper of Aumann and Lindell from 2003, perhaps it will be of some use for you. Cheers, Michal On 6/6/11 8:35 PM, "Tim Smith" <tim_smith_666 at="" yahoo.com=""> wrote: >Hi, > >I was trying to analyze some methylation (illumina 27k) data. I have data >for 18 >cancer and 18 normal samples. I want to find out if certain regions show >consistently higher methylation in cancer (as compared to normal). A >t-test (and >then corrected for fdr) for individual probes does not reveal a >statistically >significant difference between samples. > > >Is there a way I can use a sliding window approach to see if there are >consistently differentially methylated regions? Is there a R and/or >bioconductor >package that does it? Or if not, which statistical methods would be most >appropriate? > >Tim > > [[alternative HTML version deleted]] > >_______________________________________________ >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
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