The Analysis page is your go-to destination for visualizing and analyzing your bioprocess data. Here, you can choose between line charts and scatter charts, providing you with the options you need to tell the story behind your data. Use line charts for timeseries data, customize the view and add process annotations using the event feature. For a more quantitative analysis use scatter charts to take advantage of aggregations and non-time based analysis.
Navigating to the Analysis Page
To access the Analysis page, start by navigating to the Runs page and selecting a set of runs you wish to analyze. Press the 'Analyze' button in the top right corner of this view to transition to the Analysis page. The line chart view is the default option for visualizing timeseries data. To switch to scatter chart view, choose 'Scatter Chart' under Chart Type in the 'View' sidebar menu.
Workflow
Graph customization
Select one or multiple metrics from the dropdown list. Open the 'View' sidebar and make changes to Chart Type selection or viewing options (e.g. Split By > Metric)
Run selection
Update Run selection as needed by checking/unchecking run checkboxes in the table underneath the graph. Optionally, click 'Run selection' for altering the list of runs included in the analysis.
Full Screen view
Switch to Full Screen view for a close-up view of the graph.
Export
Export graph as an image (.PNG) or export displayed data as an Excel file.
Save Analysis
Save analysis as a report by creating a new report or appending the analysis to an existing report.
Line Charts
Visualization of Timeseries Data:
The line chart is a powerful tool for visualizing timeseries data, providing flexibility in layout to support various views. This includes a close-up view of individual runs or metrics, as well as side-by-side comparisons of multiple runs or metrics.
Viewing Options:
Customize your line chart with viewing options such as 'Split By' and 'Chart Layout'
Split By:
Separate: One graph per run and metric.
Split runs: One graph per selected run. Close-up view of an individual runs..
Split metrics: One graph per metric. Compare a single metric across multiple runs.
All-in-one-graph: Summary view with all runs and all metrics in a single graph.
Chart Layout:
1 Graph: One graph per line
2 Graphs: Two graphs per line
3 Graphs: Three graphs per line
X-Axis Zoom:
Zoom into specific areas of the graph to gain a closer look at the data, allowing for detailed analysis and insights. Click into the graph, highlight the area and release to zoom in. The updated bounds are available in the top right corner under "ERT" (Elapsed Run Time).
Y-Axis Custom Range:
Overwrite the default Y-axis range using manually entered, custom values for a more tailored viewing experience. Open the 'View' sidebar and navigate to the 'Y-Axis settings' section. Select the Y-metric in the dropdown menu and enter in values for 'Start range', 'End range' and optionally 'Interval'. Press the 'Apply' button to proceed. Undo the zoom filter in the top right corner to revert to the default settings.
Removing Zoom Configurations:
Cancel the zoom setting by clicking the "x" on the zoom annotation in the top right corner of the plot to revert to the default settings. Please note the zoom configurations are stored on a per axis basis. Enabling or disabling "Combined Y-Axis" will change the available set of displayed axes, but the zoom setting will still be stored.
Events:
Utilize the event annotation feature to create time-stamped and interactive event notes, enhancing your analysis. Upload images to provide additional context to your data. Use the event filter dropdown to limit the number of events shown on the graph as needed.
Formulas:
Create derived, custom metrics using the built-in formula editor. Type the custom metric name into the metric dropdown field and click 'Add'. Enter formulas name, dependencies and formulas into the formula editor. Use the formula preview feature for troubleshooting as needed. For more information on formulas, see Library article. In the example below, air flow rate (L/h) and reactor volume (L) are used to calculate VVM, a scale-independent air flow metric to enable comparison of the aeration regime across different bioreactor sizes (0.25L to 100.000L).
Grouping:
Use the "Group by" functionality to aggregate and compare related runs based on specific attributes, such as "Experimental Condition", "Strain" or "Alias". When runs are grouped, it enables the analysis of variability (shaded regions representing 16th and 84th percentiles) and central tendencies (median) within those groups. Run IDs in run tables and chart legends are replaced by the attribute name enabling users to assign custom run names.
Metric/Formula Notes:
Provide additional context to your analysis by adding notes to your metrics or formulas (Library>Editing page). On the analysis page. hover over the relevant quantity name for quick accessing the note via tool tip hover.
Run Data Table Customization:
Customize the run data table to provide additional context to the timeseries graph. This includes displaying relevant metadata such as strain ID, run ID, bioreactor size, and more, enhancing the interpretability of the visualized data.
Scatter Chart
Aggregation:
Use scatter charts when you want to explore relationships between two variables in your data. They are particularly useful when the input variable for X is non-time-based (e.g., Strain ID, Run ID). You have the option to choose between a variety of aggregations for the Y input variable, such as mean, standard deviation, sum, count, minimum, maximum, last value, etc. For example, compare 'Product (Last)' versus 'Run ID' or 'OD (Maximum)' versus 'Strain ID'. Use this tool to Identify trends, clusters, outliers, or other patterns in your data, facilitating data-driven decision-making and analysis.
Statistics Calculation:
Scatter chart automatically calculates statistics when using X variables with multiple entries providing insights into the distribution of the dependent variable across different categories of the independent variable. The available statistics are mean, standard deviation, standard error, count, and lower/upper 95%. For example, plot 'Biomass concentration (Last)' versus 'Strain ID' where Strain ID has three unique values (Strain ID#1, Strain ID#2, StrainID#3), to get a deeper understanding of how the Biomass concentration varies across each of the unique strain IDs.
