Sphinx allows you to group data by any column in your dataset in a number of ways. This page provides example use cases to help decide which way you want to present grouped data.

Grouping Columns

Grouping columns are useful when you want to replace the view of data in all column with the average, sum, or other summary metric. This method will affect dependent plots; only showing the new summary metric in the plot.

Example use cases include:

• Replacing all columns in the data with a summary, grouped by biological replicates. This can provide a more concise view of the data, and can be useful when you want to see the average or sum of the data across technical replicates.
• Showing a summary of the data based on multiple grouping factors. This can be useful when you have multiple experimental factor and want to see a summary metric for each group.

You can read more about grouping columns in the Datatable Toolbar.

Custom Column for Grouping

A custom column for grouping is useful when you want to add a column to the data table. Adding a grouped column will not affect dependent plots; the new column will be added to the data table, but the data will still be plotted as it was before.

Example use cases include:

• Adding a column that reports a summary metric for biological replicates. This can be useful when you want to see the complete data table, but also have a summary metric for each biological replicate visible in a unique column.
• Adding a column with a summary metrics you want to use in a later calculation. This can be useful when you have a specific calculation you want to perform on the data, and want to use an intermediate summary metric for the data.

You can read more about adding a grouped column in the Analysis Toolbar.

Summarizing Data

Summarizing data is useful when you want to create a new table that has many summary metrics for all the data. Adding a summary can be done via the Analyze menu via selection of the “Statitical Tests” and then “Descriptive Statistics” option.

A summary data table can be worked with independently of the original data table. This can be useful when you want to see a summary for all the data, but also have the complete data table available for further analysis or plotting.