This Troubleshooting guide
aims to provide assistance and solutions to common issues that users may
encounter while working with the QurvE package or shiny app. As we
gather more user feedback, we will continue to update this page with
additional information and solutions for a smooth QurvE
experience.
Outliers can significantly affect the accuracy and reliability of growth curve analysis. Identifying and addressing outliers is an essential step when using QurvE for high-throughput growth curve experiments. In this section, we discuss some strategies to identify and handle outliers in your data.
Visual Inspection: Use the “Validation” window in the Shiny app to visually inspect each growth or fluorescence curve and its corresponding fit. Look for any irregularities or deviations from the expected growth pattern.
Examine Growth Parameters: Plot the computed growth or fluorescence parameters (e.g., growth rate, lag time, and maximum OD, fluorescence increase rate) for each sample to identify any unusual values that may indicate an outlier sample.
Bootstrapping: QurvE offers bootstrapping for spline fitting, which can help estimate uncertainty, validate the model, and identify potential outliers. Use the bootstrapping results to assess the reliability of your curve fits.
Modify Fitting Parameters: Adjust the fitting parameters in the
[Computation] section of the Shiny app to systematically exclude
problematic sections of the measurements (e.g., outside the detection
limit of the instrument). In R, this can be achieved by defining values
for t0, tmax, min.growth, and
max.growth in the growth.workflow() and
fl.workflow() functions
Re-run outlier samples: After identifying outliers, re-run their analysis with modified parameters in the [Validation] panel of the shiny app. An example for how this can be achieved within R is provided in the growth curve analysis documentation
Data Preprocessing: Before importing your data into QurvE, preprocess it to remove or reduce the impact of outliers. This can be done using various data cleaning techniques, such as filtering, interpolation, or imputation. Please note that such preprocessing methods are outside the scope of QurvE.
A: The appropriate measurement interval depends on
the organism and growth conditions being studied. For instance,
fast-growing organisms like Escherichia coli require shorter
sampling intervals of at least one hour while slow-growing organisms
like plants can be measured up to every five days. It’s also important
to note that the appropriate sampling interval may vary depending on the
specific research question being addressed. For example, if the goal is
to study the effects of a particular treatment on growth rates or
diauxic shifts, more frequent sampling may be necessary to capture the
short-term changes in growth. Alternatively, if the goal is to assess
overall growth patterns over a longer time frame, less frequent sampling
may be sufficient.
For linear fits, we recommend having at least four measurements within
the mid-log phase of the growth curves to reliably estimate growth
rates. When using smoothing splines with a low measurement frequency,
such as in bioreactor fermentations or shaken flask experiments, you can
increase the smoothing factor to make the fit less susceptible to
outliers.