How to download edger differential file from r

Learn more. Asked 4 years, 10 months ago. Active 4 years, 10 months ago. Viewed times. Please read the info about how to ask a good question and how to give a reproducible example. This will make it much easier for others to help you. Our GFF file has a lot of redundant features that describe a gene multiple times, so we are going to trim it just to have "gene" features using grep.

What is this doing? Use head to see the before and after. HTseq is another tool to count reads. In contrast, HTseq is a specialized utility for counting reads, and it does not have many functions other than that. HTseq is very slow and you need to run multiple command lines in order to do the same job as what bedtools multicov did.

Why do we learn this? Well, you may want to care about reads mapped on intersection when you count reads. Please take a look at this page , and if this sophisticated counting method looks useful for you, use HTseq. Otherwise, use bedtools.

Check out the last 5 lines. They are basic statistics. The basic statistics last 5 lines is useful to know, but should be removed to use it as a input file for DEGseq. HTseq-count is strand-specific in default. Let's clean it up a bit before loading into R - which likes to work on simple tables.

GFF are tab-delimited files. We can do this cleanup many ways, but a quick one is to use the Unix string editor sed. Some commands are spread across multiple lines. So this:. It's ok to copy across the multiple lines and paste into R as long as you get all the way to the closing parenthesis.

They are, roughly speaking, the relative average coverage of each data set. Specifically, they are the size parameter of the negative binomial fit to the counts per gene per data file. In this model, the lower the counts are, the more dispersion relative to the mean is expected red line in graph.

Thus, higher fold changes are required in lowly expressed genes to call the same observed fold-change difference as significant. Note that the "FC" fold change calculated is initially the reverse of that for the DESeq example for the output here.

It is wt relative to mut. A good rule of thumb when analyzing RNA-seq data from a single cell type is to expect thousand expressed genes. In this case, we have many more because the samples include RNA collected from many different cell types, and thus it is not surprising that many more genes are robustly expressed. What information are we ignoring? Another method to view the relationships between samples is principal components analysis PCA.

Recall from lecture that the read counts for moderately to lowly expressed genes can be strongly influenced by small fluctuations in the expression level of highly expressed genes. In other words, small differences in expression of highly expressed genes between samples can give the appearance that many lower expressed genes are differentially expressed between conditions.

TMM adjusts for this by removing the extremely lowly and highly expressed genes and also those genes that are very different across samples. It then compares the total counts for this subset of genes between the two samples to get the scaling factor this is a simplification.

Similar to normalization methods for microarray data, this method assumes the majority of genes are not differentially expressed between any two samples. As expected from the description of the samples and the heatmap, there are many differentially expressed genes.

The blue lines represent a four-fold change in expression. Please read the posting guide. Post questions about Bioconductor to one of the following locations:. Home Bioconductor 3. DOI: