### DESeq: Digital gene expresion analysis based on the negative binomial distribution

#### Description

The BioC package DESeq provides a powerful tool to estimate the variance in count data and test for differential expression. It can estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. Variance estimation The core assumption of this method is that the mean is a good predictor of the variance, i.e., that genes with a similar expression level also have similar variance across replicates. Hence, we need to estimate for each condition a function that allows to predict the variance from the mean. This estimation is done by calculating, for each gene, the sample mean and variance within replicates and then tting a curve to this data.

#### Parameters

1. Dataset: 2 sample groups.
2. P-value/FdrSignificance Cutt-off method and value.
3. Output: DESeq creates a standard MeV viewer nodeo nthe left tree which conists of heatmaps and tables of both significant, non-significant and a combined list of genes.

#### How to Run DESeq

1. Load DESeq type data
2. Launch DESeq from ToolBar -> Statistics -> DESeq
3. Assign samples to 2 groups or leave them out
4. Choose significance coutt-off method (p-value or fdr) and specify a value
5. Hit OK