What is dispersion RNA-seq?

What is dispersion RNA-seq?

In all RNA-seq analysis applications they talk about the dispersion of a gene. The BCV is the relative variability of expression between biological replicates. If you estimate dispersion = 0.19, then sqrt(dispersion) = BCV = 0.44. This means that the expression values vary up and down by 44% between replicates.

What is dispersion in gene expression?

The dispersion is a parameter describing how much the variance deviates from the mean. The Poission distribution is sometimes said to be a special case of the NB distribution, when dispersion=1 and thereby mean = variance. So to answer your question: no, the dispersion is not the variance of your gene.

What is TMM normalization?

TMM normalization is a simple and effective method for estimating relative RNA production levels from RNA-seq data. The TMM method estimates scale factors between samples that can be incorporated into currently used statistical methods for DE analysis.

What is FPKM?

FPKM stands for fragments per kilobase of exon per million mapped fragments. It is analogous to RPKM and is used specifically in paired-end RNA-seq experiments [17].

What is LFC shrinkage?

Shrunken log2 foldchanges (LFC) As with the shrinkage of dispersion estimates, LFC shrinkage uses information from all genes to generate more accurate estimates. So even though two genes can have similar normalized count values, they can have differing degrees of LFC shrinkage.

What does the DESeq function do?

By default, DESeq will replace outliers if the Cook’s distance is large for a sample which has 7 or more replicates (including itself). This replacement is performed by the replaceOutliers function. This default behavior helps to prevent filtering genes based on Cook’s distance when there are many degrees of freedom.

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. DESeq2 detects automatically count outliers using Cooks’s distance and removes these genes from analysis.

What is TPM Rnaseq?

Transcripts Per Million (TPM) is a normalization method for RNA-seq, should be read as “for every 1,000,000 RNA molecules in the RNA-seq sample, x came from this gene/transcript.”

What is DESeq analysis?

DESeq is an R package to analyse count data from high-throughput sequencing assays such as RNA-Seq and test for differential expression. The package is available via Bioconductor and can be conveniently installed as follows: Start an R session and type source(“http://www.bioconductor.org/biocLite.R”) biocLite(“DESeq”)

How do you cite DESeq?

Citation (from within R, enter citation(“DESeq2”) ): Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi: 10.1186/s13059-014-0550-8.

What does log2fc mean?

It’s also useful to know that a log2 fold change (B/A) of 1 means B is twice as large as A, while log2fc of 2 means B is 4x as large as A. Conversely, -1 means A is twice as large as B, and -2 means A is 4x as large as B.

What is another word for replicate?

Replicate: to make an exact likeness of. Synonyms: clone, copy, copycat… Antonyms: originate… Find the right word.

How does minreplicatesforreplace work in DESeq?

The argument minReplicatesForReplace is used to decide which samples are eligible for automatic replacement in the case of extreme Cook’s distance. By default, DESeq will replace outliers if the Cook’s distance is large for a sample which has 7 or more replicates (including itself). This replacement is performed by…

Can I use alternative size factors in deseq2?

This is the link for the Deseq2 script I am using. This is another Deseq script that shows: how you can use alternative size factors if you know the size factors might be affected by the data in some way Design terms information: Imagine you have 3 biological replicates (repA, repB, repC) of RNA-seq between two people (person1 and person2).

How does deseq2 test for differential expression?

The package DESeq2 provides methods to test for differential expression by use of negative binomial generalized linear models; the estimates of dispersion and logarithmic fold changes incorporate data-driven prior distributions. This vignette explains the use of the package and demonstrates typical workflows.