Matching metadata and counts data
To perform any analysis with DESeq2, we need to create a DESeq2 object by providing the raw counts, metadata, and design formula. To do this, we need to read in the raw counts data and associated metadata we created previously, make sure the sample names are in the same order in both datasets, then create a DESeq2 object to use for differential expression analysis. We will use the design formula ~ condition to test for differential expression between conditions (normal and fibrosis).
The DESeq2 and dplyr libraries have been loaded for you, and the smoc2_rawcounts and smoc2_metadata files have been read in.
This is a part of the course
“RNA-Seq with Bioconductor in R”
Exercise instructions
Use the
match()function to return the indices for how to reorder the columns of the counts data to match the order of the row names of the metadata. Assign the result toreorder_idx.Reorder the columns of the count data with
reorder_idxsuch that the column names match the order of the row names in the metadata.Create a DESeq2 object,
dds_smoc2using theDESeqDataSetFromMatrix()function using the metadata and reordered counts.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Use the match() function to reorder the columns of the raw counts
reorder_idx <- match(___(___), ___(___))
# Reorder the columns of the count data
reordered_smoc2_rawcounts <- smoc2_rawcounts[ , ___]
# Create a DESeq2 object
dds_smoc2 <- DESeqDataSetFromMatrix(countData = ___,
colData = ___,
design = ~ condition)