A clustering analysis workflow for use with your ScPCA dataset!

January 5, 2023

Recently, we told you about the Single-cell Pediatric Cancer Atlas (ScPCA) downstream analysis workflow. The ready-to-go workflow is intended to be used with single-cell and single-nuclei gene expression data available on the ScPCA Portal. We developed the workflow to filter, normalize, and perform dimensionality reduction, as well as incorporate initial clustering results to each processed sample/library object. Now we’re excited to introduce one of our latest offerings for use with ScPCA data, a clustering analysis workflow, which can be applied to datasets after running the filtering, normalization, and dimensionality reduction workflow! 

What is the clustering analysis workflow?

The clustering analysis workflow can help identify the optimal clustering method and parameters for each library in an ScPCA dataset. Users will be able to test a variety of clustering options and parameters in parallel. After running the workflow, a report is provided for each dataset, which summarizes the results of every clustering method tested.

There are two main steps of the clustering analysis workflow: clustering and plotting clustering results. 

  • Graph-based clustering is applied to all libraries using a set of parameters provided by the user.
  • Clustering is evaluated quantitatively through calculation of a set of metrics including cluster purity, silhouette width, and cluster stability. (Learn more about these metrics and how they are used to identify the optimal clustering results!) After clustering results are  calculated, they are displayed in a UMAP plot. Plots are provided as an html report for ease of reference.

Getting Started

⚠️ Successfully running the downstream analysis workflow is required before you will be able to implement the clustering analysis workflow. Learn more about running the downstream analysis workflow!

If you are interested in trying the clustering analysis workflow on your data or are just curious to learn more about how it works, you can read the full documentation here. Learn what you’ll need to provide to get started, what you can expect to get back, and how to run the workflow. Note that the same software requirements needed for the downstream analysis workflow are also required for this clustering workflow. 

The Data Lab continues to make enhancements to the portal and we appreciate your feedback. Currently, we are conducting usability testing for the downstream analysis workflow and are looking for more childhood cancer researchers to participate. Fill out this form if you’re interested in learning more. Keep an eye out for other exciting ScPCA developments soon! 

If you have questions about the ScPCA portal, you can reach out to us at scpca@ccdatalab.org.

On this blog, we share our expertise with the scientific community. You can expect to read technical content about our processes, information about our products and services, and much more. Subscribe here to receive updates!

Recently, we told you about the Single-cell Pediatric Cancer Atlas (ScPCA) downstream analysis workflow. The ready-to-go workflow is intended to be used with single-cell and single-nuclei gene expression data available on the ScPCA Portal. We developed the workflow to filter, normalize, and perform dimensionality reduction, as well as incorporate initial clustering results to each processed sample/library object. Now we’re excited to introduce one of our latest offerings for use with ScPCA data, a clustering analysis workflow, which can be applied to datasets after running the filtering, normalization, and dimensionality reduction workflow! 

What is the clustering analysis workflow?

The clustering analysis workflow can help identify the optimal clustering method and parameters for each library in an ScPCA dataset. Users will be able to test a variety of clustering options and parameters in parallel. After running the workflow, a report is provided for each dataset, which summarizes the results of every clustering method tested.

There are two main steps of the clustering analysis workflow: clustering and plotting clustering results. 

  • Graph-based clustering is applied to all libraries using a set of parameters provided by the user.
  • Clustering is evaluated quantitatively through calculation of a set of metrics including cluster purity, silhouette width, and cluster stability. (Learn more about these metrics and how they are used to identify the optimal clustering results!) After clustering results are  calculated, they are displayed in a UMAP plot. Plots are provided as an html report for ease of reference.

Getting Started

⚠️ Successfully running the downstream analysis workflow is required before you will be able to implement the clustering analysis workflow. Learn more about running the downstream analysis workflow!

If you are interested in trying the clustering analysis workflow on your data or are just curious to learn more about how it works, you can read the full documentation here. Learn what you’ll need to provide to get started, what you can expect to get back, and how to run the workflow. Note that the same software requirements needed for the downstream analysis workflow are also required for this clustering workflow. 

The Data Lab continues to make enhancements to the portal and we appreciate your feedback. Currently, we are conducting usability testing for the downstream analysis workflow and are looking for more childhood cancer researchers to participate. Fill out this form if you’re interested in learning more. Keep an eye out for other exciting ScPCA developments soon! 

If you have questions about the ScPCA portal, you can reach out to us at scpca@ccdatalab.org.

On this blog, we share our expertise with the scientific community. You can expect to read technical content about our processes, information about our products and services, and much more. Subscribe here to receive updates!

Back To Blog