RNA-Seq DE workflow summary

NOTE: It may take a bit longer to load this exercise.

Let's run through the DESeq2 workflow using the full dataset with both wildtype and smoc2 overexpression samples included. We have loaded the DESeq2 and dplyr libraries and read in the metadata file, all_metadata and the raw counts file, all_rawcounts for you.

full metadata

This exercise is part of the course

RNA-Seq with Bioconductor in R

View Course

Exercise instructions

Check that the samples are in the same order in both all_rawcounts and all_metadata using the rownames(), colnames(), all(), and %in% operator.
Create the DESeq2 object using the appropriate design, testing for the effect of condition while controlling for genotype.
Create the DESeq2 object using the appropriate design, controlling for genotype and condition individually, but test for genotype:condition.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Check that all of the samples are in the same order in the metadata and count data
all(___(___) %in% ___(___))

# DESeq object to test for the effect of fibrosis regardless of genotype
dds_all <- DESeqDataSetFromMatrix(countData = ___,
                        colData = ___,
                        design = ___)

# DESeq object to test for the effect of genotype on the effect of fibrosis                        
dds_complex <- DESeqDataSetFromMatrix(countData = ___,
                                ___,
                                ___)

Edit and Run Code