Data Table Toolbar

The data table toolbar contains multiple options for working with a data table in an Analysis. Options in the toolbar can edit data, change the view of data, and create new columns.

Columns

The Columns option allows you to define which columns you want displayed in the data table. Selecting columns only impacts the view in the current data table.

To edit the display of columns, simply toggle on/off the columns you want in the data table.

Filters

The Filters option allows you to specify one or more filters to apply to the data table. Applying filters allows you to only display data that meet a certain criteria. This can be useful for selecting only relevant data to be shown in the current cell and used in other calculations in you Analysis.

To add a filter, select the filter option and define your filter criteria. You can add one or many filters, each with a different criteria.

Filters applied to the data table affect calculations in the current data table and downstream Analysis cells.

Group

The Group option allows you to specify one or more columns to group by. This will update the data table by grouping all data by the selected columns and summarizing the data in the group.

To add a group, select the group option then select the columns you want to group by. You may optionally define how you want numeric data to be summarized by selecting the “Metric” option.

The Group metric is applied to all columns in the data table and affects downstream Analysis cells.

Custom Columns

Custom columns allow you to add new columns to your data table. You can add any number using columns that are in the source data table. There are multiple kinds of customs columns you can add, listed below.

Biotools

Biotools are useful when working with protein variants where you want to determine the position, reference, and type of mutation.

To add a Biotools column, add a custom column and select “Biotools”, then define your variant column and output column name. Data in the variant column should be in the single-letter amino acid notation or point mutation notation as shown in the below example table.

Variant	Description
A123B	Alanine at position 123 replaced by B (e.g., Alanine to Arginine)
C456D	Cysteine at position 456 replaced by D (e.g., Cysteine to Aspartic Acid)
E789F	Glutamic Acid at position 789 replaced by F (e.g., Glutamic Acid to Phenylalanine)

Formula

A formula column lets you add a new column that is calculated using existing columns in your data table. Formula columns operate on an entire column allowing you to define one formula for all rows. You can add a formula column by selecting the “Custom Column” option, and then adding a “Formula” column.

Expressions can be written using the following reference.

Function	Details	Example
Reference Column	You can directly reference columns in the dataset. Names are case sensitive. If the column name contains spaces you can use quotation marks to reference the column	`"predicted_affinity"`
Arithmetic Operations	You can use standard operators (+-/*) or exponentiation (^), use parenthesis to group operations	`(2.0 * "predicted_affinity")^2 / 88.0`
Mathematical Functions	You can use the following mathematical functions: sin, cos, tan, exp, ln, log2, log10, sqrt, int	`exp(2.0) + sqrt("predicted_affinity")`
Text Functions	You can use the following text functions: substring	`substring("sample_id", 2, 4)`
Text Joining	You can combine text or column values using the & operator. For example, if you want to combine the text from column “a” and column “b” with a dash in between, you would write: a & ”-” & b. This will result in text like “valueA-valueB”.	`"sample_id" & "_suffix"` `"part1" & "-" & "part2"`
Comparison Operators	You can use the following operators to compare expressions: =, !=, <, <=, >, >=. These operators have lower precedence than arithmetic operations.	`"predicted_affinity" * 2 != "observed_affinity"`

Formula columns will be present in any downstream data tables in an Analysis. These columns can only be edited in the data table in which they were created.

Grouping

A grouping column lets you add a new column that is calculated using existing columns in your data table. Grouping columns calculate a metric for all data in the column’s groups, then output those results in a new column. This is useful for adding summary metrics directly into a table when you want the original data to be unedited. Compare this to the Group Option where the summary metrics replace the data in the columns.