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- | NOISeq is a non-parametric approach for the identification of differentially expressed genes from count data or previously normalized count data. NOISeq empirically models the noise distribution of count changes by contrasting fold-change differences (M) and absolute expression differences (D) for all the features in samples within the same condition. | + | **NOISeq** is a non-parametric approach for the identification of differentially expressed genes from count data or previously normalized count data. NOISeq empirically models the noise distribution of count changes by contrasting fold-change differences (M) and absolute expression differences (D) for all the features in samples within the same condition. |
This reference distribution is then used to assess whether the M-D values computed between two conditions for a given gene is likely to be part of the noise or represent a true differential expression. | This reference distribution is then used to assess whether the M-D values computed between two conditions for a given gene is likely to be part of the noise or represent a true differential expression. | ||
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Please, find {{:posternoiseq_2012.pdf|here}} an outline of the NOISeq method. | Please, find {{:posternoiseq_2012.pdf|here}} an outline of the NOISeq method. | ||
+ | **NOISeqBIO** is the adaptation of NOISeq to handle biological variability. You can find a description of the NOISeqBIO method in this {{:noiseqbio_techreport.pdf|Technical Report}} and also a summary in this {{:posternoiseqbio.pdf|poster}}. | ||
- | A new version of the method is being developed to better handle biological variation. This new version is called NOISeqBIO. | ||
- | + | Both NOISeq and NOISeqBIO are included in [[http://www.bioconductor.org/packages/release/bioc/html/NOISeq.html|R/Bioconductor NOISeq package]] together with a set of graphical tools to assess the quality of sequencing count data. | |
- | Both NOISeq and NOISeqBIO have been implemented in R language. | + | |