Using Sleuth to analyze time course experiments

Multiple-condition, multiple-level experiments, such as timecourse experiments, are more difficult to analyze than simple comparisons because they involve a greater amount of data and complexity. In a timecourse experiment, for example, the goal is to understand how a biological system changes over time in response to a particular treatment or condition. This requires analyzing data from multiple timepoints and conditions, which can make the data more complex and harder to interpret.

One aspect that makes timecourse experiments more difficult is the filter function in the sleuth_prep() filter argument. This function is used to filter out low-quality or non-informative data from the analysis. The filter function works by excluding targets that are not present in a minimum percentage of samples. In a simple comparison, the filter function is relatively straightforward to apply as it is only necessary to compare two conditions and identify...