Accelerating the Pace of Childhood Cancer Research with Big Data

Alex's Lemonade Stand Foundation Logo

The Childhood Cancer Data Lab was established by Alex’s Lemonade Stand Foundation (ALSF) in 2017. ALSF recognized that pediatric cancer researchers face hurdles that impede the pace of research. 

ALSF introduced the Data Lab to empower researchers and scientists across the globe by removing roadblocks, supporting opportunities for collaboration and sharing, and developing resources to accelerate new treatment and cure discovery.

The Data Lab's mission is to empower pediatric cancer experts poised for the next big discovery with the knowledge, data, and tools to reach it. We construct tools that make vast amounts of data widely available, easily mineable, and broadly reusable. We train researchers and scientists to better understand their own data and to advance their work more quickly.

To date, the Data Lab has trained over 200 childhood cancer researchers and has harmonized over 1.3 million data samples and made them easily available. Learn more about the Data Lab’s impact here. 

Two people looking at goals


The Data Lab develops tools designed to make data and analysis widely available and broadly reusable.

Data Science Workshops

The Data Lab offers workshops to teach researchers the data science skills they need to examine their own data. Our courses focus on the most cutting edge tools and analysis techniques. We ensure that participants walk away with an understanding of:

  • The R programming language, R Notebooks, and some reproducible research practices.
  • Processing bulk and single-cell RNA-seq data from raw all the way to downstream analyses.
  • Downstream analyses methods like differential expression analyses, hierarchical clustering, and preparing publication-ready plots.

“I think anyone who is working on or near single-cell data should take this course. I am so much more confident in what I understand about single-cell analyses compared to where I was at the beginning. 10/10 recommend.”

Jessica Elswood, Postdoctoral Associate, Baylor College of Medicine
- Jessica Elswood, Postdoctoral Associate, Baylor College of Medicine


Make a donation to support the Data Lab’s mission of putting knowledge and resources in the hands of pediatric cancer experts poised for the next big discovery. 

With your help, we can

Fund innovative models to scale training workshops.

Offer our expertise and provide consultation on projects that will change the future for children fighting cancer.

Train at least 200 childhood cancer researchers over the next four years.



July 16, 2024

Data Lab Bulk RNA-Seq and Reproducible Research Practices Workshop, Minneapolis, August 19-22, 2024

We are excited to announce our next workshop, Introduction to Bulk RNA-Sequencing and Reproducible Research Practices, will take place in Minneapolis, MN from August 19-22, 2024! In this workshop, Data Lab staff will introduce researchers studying pediatric cancer to the R programming language, the Tidyverse R packages for data science, bulk RNA-seq data analysis, pathway analyses, and techniques to achieve reproducible results in computational cancer research.



July 8, 2024

OpenScPCA: Call for contributions, new grant offerings, and analyses in progress!

In April 2024, we announced the Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project. Since then, we’ve been working to build a supportive community while getting started on a few analysis ideas! We’re excited to see growing interest in the project, and we have some big news for prospective collaborators.



May 13, 2024

Choosing wisely: A behind-the-scenes look at how we selected cell type annotation platforms for the ScPCA Portal

So you recently did some single-cell RNA sequencing and are working on analyzing your data. You’ve already quantified the gene expression data, performed any filtering, and normalized your data, but now what? You know you want to perform differential expression analysis or that you need to annotate the cell types found in your data, but there are so many different tools and methods for performing these analyses. How do you know which one is the best method for your dataset? Don’t worry, we’ve all been there – even experts in the single-cell field have been there.