Introduction

Welcome to the Enable Platform! This document outlines the step-by-step process for setting up a new study from scratch. For more details, please see our Enable User Manual.

Video walkthrough:

NOTE: we highly recommend going through the tutorial in order to familiarize yourself with the platform! However, we have also recorded a video walkthrough of the tutorial for you to watch.

https://www.loom.com/share/21a9916887bf46edbbf781405b456aae?sid=9df8058f-5648-45be-95d6-8e56553fe28f

• We are working on updating part 1 of our video tutorial! Please follow the rest of this user manual entry in the meantime, and email [email protected] for any questions.
• part 2

Data Overview

The dataset you will be working with is a subset from a study investigating Cutaneous T Cell Lymphomas or CTCL.

<aside> 💡 Citation: Phillips, D., Matusiak, M., Gutierrez, B.R. et al. Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma. Nat Commun 12, 6726 (2021)).

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You can see the entire dataset from this study as a spectator by navigating to the study “Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma” on the Portal.

The study you will create through the course of this tutorial has one experiment, which is comprised of 10 samples, each with 1 region. See this page for more details around the key terminology used on the Enable Platform.

Configuration Files Overview

The dataset can be found here.

As part of this dataset, we have also included the configuration details that will be needed to specify the clinical data, biomarker panel, filename-to-sample mapping, and gating hierarchy.

• images.zip: a folder containing all of the stitched PhenoCycler images to be imported onto the Enable platform
  • 💡 What kind of images can I upload to the platform?
• sample_labels.txt: list of all samples in the experiment
  • 💡 What is a sample label?
• biomarker_panel.csv: list of all biomarkers used in the panel to acquire the images
  • 💡 Why do I need a biomarker panel?
• clinical_data.csv: clinical data associated with each sample
  • 💡 Why do I need clinical data?
• filename_sample_association.csv: mapping between the files and their sample labels
• gating_hierarchy_config.json: a dictionary describing the gating hierarchy used to classify cells based on biomarker expression