Batch ID

What is a batch?

A batch is a set of assay values measured under comparable experimental conditions. Each batch typically contains a set of controls prepared in a way consistent with the sample.

When scientists repeat or start a new experiment and add a newly purified control (such as their wild type), that defines a new batch of data. That new batch contains your new controls as well as all the data points in your plate(s).

Examples

Data belongs to the same batch when

• Sequences belonging to the same plate were assayed simultaneously under consistent lab conditions, with variability low enough to require only one set of controls for the plate.

• Two plates were assayed on different days under the same conditions, with control measurements on both plates showing minimal to no variation.

• The activity of sequences on the same plate was measured at three different temperatures such as 55°C, 60°C, and 65°C.

Note: While these measurements belong to the same batch, each temperature should be a different Assay, e.g., activity_55, activity_60, and activity_65.

Data belongs to different batches when:

• Two plates were assayed on different days under the same conditions, but either no control sequences were included to assess variability, or the control sequences show significant variation between the two assays.

• Measurements come from different engineering rounds unless they are repeated together with the same controls.

• The data was generated from the same sequences, which were purified multiple times at different time points.

Why add Batch ID?

Cradle's machine learning models focus on relative relationships between measurements. To ensure accuracy, the models should only compare data that was measured under comparable conditions. By adding a Batch ID, you inform the model which data comes from measurements under comparable conditions.

For more details on data requirements, see Preparing your data.