NOISeq

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-cheap lv handbags 
  
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 +replacement for microarrays. The expression level given by these technologies ​is the number of reads in the library mapping to a given feature (geneexontranscriptetc.), i.e.the read counts. Most of the statistical ​methods ​for assessment ​of differential expression using count data rely on parametric assumptions about the distribution of the counts (Poisson, Negative Binomial, ​...).  
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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.  
 +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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 +NOISeq was tested on data sets with technical replicates. The are two variants of this method: NOISeq-real uses replicates when available ​to compute ​the noise distribution and NOISeq-sim simulates them in absence of replicationIt should be noted that 
 +the NOISeq-sim simulation procedure assimilates to technical replication and does not reproduce biological 
 +variabilitywhich is necessary for population inferential analysis.  
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 +Please, find {{:​posternoiseq_2012.pdf|here}} an outline ​of the NOISeq method. 
 + 
 +**NOISeqBIO** is the adaptation ​of NOISeq to handle biological variabilityYou can find a description of the NOISeqBIO method in this {{:​noiseqbio_techreport.pdf|Technical Report}} and also a summary in this {{:​posternoiseqbio.pdf|poster}}. 
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 +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. 
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