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Exercise

DESeq2 model - exploring dispersions

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After fitting the model in the previous exercise, let's explore the fit of our smoc2 data to the negative binomial model by plotting the dispersion estimates using the plotDispEsts() function. Remember that the dispersion estimates are used to model the raw counts; if the dispersions don't follow the assumptions made by DESeq2, then the variation in the data could be poorly estimated and the DE results could be less accurate.

The assumptions DESeq2 makes are that the dispersions should generally decrease with increasing mean and that they should more or less follow the fitted line.

Instructions
100 XP
  • Plot the dispersion estimates for the smoc2 data using the plotDispEsts() function. Assume all prior steps have been executed, including the creation of the DESeq2 object, dds_smoc2 and running the DESeq() function.
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