How do you perform a DESeq2 analysis?
DESeq2 differential gene expression analysis workflow
- Step 1: Estimate size factors.
- Step 2: Estimate gene-wise dispersion.
- Step 3: Fit curve to gene-wise dispersion estimates.
- Step 4: Shrink gene-wise dispersion estimates toward the values predicted by the curve.
What is the difference between DESeq and DESeq2?
Using the same counts table, DESeq2 is giving vastly more differentially expressed genes than DESeq (at FDR 0.05). That just seems a bit odd to me. Same thing for me, getting A LOT of more significantly different genes (tried with 2 different projects). Though I assumed that was intended.
What is the difference between EdgeR and DESeq2?
DESeq and EdgeR are very similar and both assume that no genes are differentially expressed. DESeq uses a “geometric” normalisation strategy, whereas EdgeR is a weighted mean of log ratios-based method. Both normalise data initially via the calculation of size / normalisation factors.
What is DESeq used for?
The DESeq2 package is designed for normalization, visualization, and differential analysis of high- dimensional count data. It makes use of empirical Bayes techniques to estimate priors for log fold change and dispersion, and to calculate posterior estimates for these quantities.
How long does it take to run DESeq2?
Running DESeq() on 1000 genes for 300 samples across 10 conditions takes <2 minutes, and you can easily run this in parallel over multiple cores. Another thing to help the speed: you can make sure you’re not running DESeq() on genes with little power by pre-filtering on normalized counts.
How does DESeq2 calculate P value?
In DESeq2, the p-values attained by the Wald test are corrected for multiple testing using the Benjamini and Hochberg method by default. There are options to use other methods in the results() function. The p-adjusted values should be used to determine significant genes.
What is the difference between edgeR and Limma?
Both the edgeR and limma gene set test methods call the same underlying test functions, the only difference is in how the counts are transformed at the beginning. The edgeR methods use a transformation based on the fitted negative binomial model, which is obviously not relevant for a limma analysis.
What is DESeq2 in bioinformatics?
DESeq2 is a tool for differential gene expression analysis of RNA-seq data. DESeq2 is a new version of DESeq and can detect more differentially expressed genes (DEGs) than DESeq2. However, it also seems to allow more false positives.
How does Deseq normalize?
DESeq2 performs an internal normalization where geometric mean is calculated for each gene across all samples. The counts for a gene in each sample is then divided by this mean. The median of these ratios in a sample is the size factor for that sample.
What is the difference between DESeq2 and Limma?
DESeq2 found more genes than limma. But more than 90% of the genes detected by the two methods are overlapped, which means both methods are reliable. If precise results are needed, limma has a better ability to find the accurate DE genes.
How do you analyze fold change data?
Fold change is computed simply as the ratio of the changes between final value and the original value over the initial value. Thus, if the original value is X and final value is Y, the fold change is (Y – X)/X or equivalently Y/X – 1.
What test does DESeq2 use?
the Wald test
With DESeq2, the Wald test is commonly used for hypothesis testing when comparing two groups. A Wald test statistic is computed along with a probability that a test statistic at least as extreme as the observed value were selected at random. This probability is called the p-value of the test.