Why is the negative binomial a better model than the Poisson for RNA Seq data?
Essentially, the Negative Binomial is a good approximation for data where the mean < variance, as is the case with RNA-Seq count data. NOTE: If we use the Poisson this will underestimate variability leading to an increase in false positive DE genes.
Why is negative binomial used?
The negative binomial distribution is a probability distribution that is used with discrete random variables. This type of distribution concerns the number of trials that must occur in order to have a predetermined number of successes. In addition, this distribution generalizes the geometric distribution.
Why do we use the negative binomial distribution For RNAseq?
This is especially a problem when the number of biological replicates are low because it is hard to accurately model variance of count based data if you are looking at only that gene and making the assumptions of normally distributed continuous data (ie a t -test).
What kind of Technology is used for RNA Seq?
RNA-Seq uses next-generation sequencing technologies, such as SOLiD, 454, Illumina, or ION Torrent [36–39]. Figure 1 depicts the main steps in an RNA-Seq experiment, ending with the first step of analysis, which is typically annotating or mapping the data to a reference.
How many reads are needed for differential RNA Seq?
The number of reads obtained will depend on the coverage done during the sequencing of the samples. Usually for a differential RNA-Seq experiment of a bacteria there should be around 20 million reads per sample. The length of the reads can vary from 50 bp to over 1000 bp depending on the system used.
Which is better RNA Seq or cDNA cloning?
RNA-Seq is a high-throughput alternative to the traditional RNA/cDNA cloning and sequencing strategies. Furthermore, RNA-Seq also provides information on the expression levels of the transcripts and the alternate splice variants.