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Satya SinghApr 28, 2026

Triggering & Automating Workflows

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Triggering & Automating Workflows

Satya Singh

Learn how to automate your end-to-end workflows in Scispot by using triggers to execute Python scripts within the embedded Jupyter server. This tutorial demonstrates how changing a test status can automatically update samples, results, and notebook entries.

Triggering & Automating Workflows

Satya Singh

Learn how to automate your end-to-end workflows in Scispot by using triggers to execute Python scripts within the embedded Jupyter server. This tutorial demonstrates how changing a test status can automatically update samples, results, and notebook entries.

Chapters

00:00

Introduction to Scispot Triggers

Overview of automating end-to-end workflows using Scispot triggers.

00:23

Embedded Jupyter Server

00:53

Setting Up Triggers

01:31

Executing the Workflow

02:49

Verifying Automated Results

04:05

Conclusion and Summary

Transcript

00:00

Hi.

00:00

Here's a quick video that talks about how to leverage sispot triggers to automate your end to end workflows.

00:05

If you are a computational person or familiar with sispot APIs, this video would be useful for you.

00:10

What I'm going to show you is how you can use triggers.

00:13

Triggers could be like a sample status change a field change in lapsheet, that triggers a script that is part of your embedded jupyter server.

00:21

Embedded jupyter server is part of sispot, where you can write your own Python scripts.

00:25

And a lot of our customers, what they do is they start with a trigger, meaning if the test status is to execute list of samples or process a batch, then run a script that links, all of those things together and then also create a notebook entry for you.

00:39

So I'm going to show one example of that.

00:41

But you can use triggers in conjunction with Python scripts in jupyter server for other workflows as well.

00:48

So let's get started.

00:49

So in this example I will show like a test library in our Sandbox account where I will change the status of a test to processing and that would trigger a script.

00:59

So what does that mean?

01:00

That means that I have a test library here, a lapsheet, and I have a test code which ends in 1.8.

01:06

And what I'm going to do is I'm going to start processing this test script.

01:10

So this is my trigger.

01:11

And I've set up this trigger in the automation.

01:14

So you'll see, I'm running a script called process qPCR when the batch status is equals to start processing.

01:22

So I'm going to start processing my test code ending in 1.8.

01:26

So let's do start processing.

01:28

Now what this would do is it would trigger automation, meaning it would execute this trigger.

01:33

the rule for the trigger is to process a script called process qpcr.

01:37

So that script is written in the embedded jupyter server.

01:40

And what it does is it basically does three things.

01:43

So it checks for the status is processing and then it updates the test status.

01:48

It fetches samples associated with that test library, and updates all of those samples, and then once the tests get updated, it does a lookup and then also updates my notebook entry.

02:00

So to go back to scispot, what this will do is once the script is executed, it would automatically update my samples, which is in a different lab sheet but associated with this test code.

02:10

And it would mark all of these samples from sample register to sample Processed, it would then also update my results lab sheet, where it would link all of the samples associated with that test code for those results.

02:23

And then eventually it would also update my notebook entry, meaning it would embed my PCR results and the samples that are linked to that test.

02:31

So all of that is done by this script, that is basically using the batch status as processing.

02:37

So this is one example of a trigger that is executing a Python script.

02:40

Okay, so let's see if that script actually got executed or not.

02:43

So I'm going to refresh this page and it says processing complete, which means the 1.8, the processing has been completed.

02:49

So if I go back here and if I refresh this lapflow sample manager, I will see that the sample that were registered for 1.8 a few seconds before are now all marked as sample processed.

03:02

And it has automatically updated the sample status because it knows that these samples are linked with the test for, for which I executed the script.

03:10

And then it would also update my results lab sheet where it would mark all the samples for my test, that ends in 1.8.

03:18

Right.

03:19

So it has marked those samples as well.

03:21

And then it would also update my space page.

03:24

So this is the script that I wrote.

03:25

Basically it uses one of the endpoints, fetch, lapsheet, data, and then also update the lab space page.

03:31

So you'll see like I've run this script multiple times, but the recent one you'll see it has the 11 entries and these entries are all associated with test code 18.

03:41

And all the samples are processed and then also the results associated with those samples are also embedded into my lab space page.

03:50

so let me show that as well.

03:51

So these are all the results associated with the test run and the sample id.

03:56

So if you already have your data structured in a good data model and lab sheet, you can leverage these triggers to execute certain scripts.

04:04

And as a result you can automate your end to end workflows in sizeport.

04:08

Thank you.

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