📄️ Through Interface
You can load a small number of samples directly on the browser through the interface. In the cohort-level view, there is a side panel that enables you to add a new sample by providing metadata (e.g., cancer type) and file URLs (e.g., bedpe, txt, vcf).
📄️ Through Data Config
You can load a large number of samples through data configurations. You need to (1) make a data config file (.json) that contains the information for individual samples, (2) store the config file in a HTTPS file server (e.g., AWS S3 or GitHub Gist), and (3) use it with the external parameter of the Chromoscope URL:
📄️ Data Formats
This page describes file formats used in Chromoscope. To find a list of required and optional files, please refer to the Data Configuration section.
📄️ Thumbnails
To enable browsing a large number of samples at scale in the cohort-level view, we generated thumbnail images for individual samples and use these image files. We pre-generated thumbnails for all datasets that we provide.
📄️ URL Parameters
There are multiple parameters available that you can used along with the base URL (https
📄️ Loading Local Data
You may also want to see the Chromoscope Python package, which allows you to load local files on computational notebooks.
📄️ Loading Private Data
This article details how to set up security credentials for an Amazon Web Services (AWS) account and temporarily visualize data stored in private S3 buckets (all public access blocked) via presigned URLs. Presigned URLs are used in provided scripts described in the following section, to create configuration files for large cohorts of sample data saved on private S3 buckets.
📄️ Cohort Config Creation
Overview
📄️ Using Python Package
You can use a Chromoscope Python package to visualize your local SV files directly on computational notebooks, such as Jupyter Notebook, Jupyter Lab, and Google Colab.