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Next Generation Sequencing (NGS) technologies are increasingly being used for gene expression profiling as a | Next Generation Sequencing (NGS) technologies are increasingly being used for gene expression profiling as a | ||
- | replacement for microarrays. The expression level given by these technologies is the number of reads in the library mapping to a given feature (gene, exon, transcript, etc.), 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, ...). Moreover, many of them need replicates to work and tend to have problems to evaluate differential expression in features with low counts. | + | replacement for microarrays. The expression level given by these technologies is the number of reads in the library mapping to a given feature (gene, exon, transcript, etc.), 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, ...). |
- | NOISeq is a non-parametric approach for the identification of differentially expressed genes from 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. | ||
- | The are two variants of the method: NOISeq-real uses replicates, when available, to compute | + | |
- | the noise distribution and, NOISeq-sim simulates them in absence of replication. It should be noted that | + | 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 replication. It should be noted that |
the NOISeq-sim simulation procedure assimilates to technical replication and does not reproduce biological | the NOISeq-sim simulation procedure assimilates to technical replication and does not reproduce biological | ||
variability, which is necessary for population inferential analysis. | variability, which is necessary for population inferential analysis. | ||
- | Please, find {{:posternoiseq_2010.pdf|here}} an outline of the NOISeq method. | ||
- | NOISeq has been implemented in R language. | + | Please, find {{:posternoiseq_2012.pdf|here}} an outline of the NOISeq method. |
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+ | //**NEW!!!**//\\ | ||
+ | A new version of the method is being developed to better handle biological variation. This new version is called **NOISeqBIO** and it is described in this {{:noiseqbio_techreport.pdf|Technical Report}} and also in this {{:posternoiseqbio.pdf|poster}}. | ||
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+ | Both NOISeq and NOISeqBIO have been implemented in R language. | ||