What is Limma used for?
limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes.
What is the Limma package?
limma is a very popular package for analyzing microarray and RNA-seq data. LIMMA stands for “linear models for microarray data”. Perhaps unsurprisingly, limma contains functionality for fitting a broad class of statistical models called “linear models”.
Does edgeR use 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 Limma voom?
voom is a function in the limma package that modifies RNA-Seq data for use with limma. Together they allow fast, flexible, and powerful analyses of RNA-Seq data.
What is intercept in Limma?
In this case, the intercept term indicates what > sort of model you want to fit, either a cell means or factor effects model. > > Without an intercept you are fitting a cell means model in which you are > estimating the mean expression for each factor level (e.g., the model is > y_ij = u_i + e_ij).
What is the difference between edgeR and Limma?
Which is better DESeq2 or edgeR?
I used both DESeq2 and edgeR to analyze my RNAseq data. However, I found a higher number of significant genes in my DESeq2 analysis compared to edgeR. The difference in DE genes is about 100, not 1000 as you say in your question. To me the results from the two packages seem remarkably consistent.
What is B value in Limma?
The B-statistic (lods or B) is the log-odds that that gene is differentially expressed. Suppose for example that B=1.5. The odds of differential expression is exp(1.5)=4.48, i.e, about four and a half to one.