Collaborating with the Data Lab on OpenPBTA shaped how our team works reproducibly
This is a guest blog post by Jo Lynne Rokita, PhD, leader of the Bioinformatics Translational Pediatric Oncology Team at the Center for Data-Driven Discovery in Biomedicine (D3b) at Children’s Hospital of Philadelphia (CHOP).
At the Center for Data-Driven Discovery in Biomedicine (D3b), I lead the Bioinformatics Translational Pediatric Oncology Team, a team of bioinformatics scientists. Our mission is to advance pediatric oncology research and precision medicine through collaboration and development of open-source analytical tools, frameworks, and data resources. In 1998, I lost my four year old cousin John Matthew to a brain tumor we now know was likely a diffuse intrinsic pontine glioma. So, it was bittersweet for me to see the Open Pediatric Brain Tumor Atlas (OpenPBTA) manuscript published in Cell Genomics on the last day of brain tumor awareness month this past year1. But let’s rewind.
By the end of 2018, the Children’s Brain Tumor Network (CBTN), through D3b, had generated and released the Pediatric Brain Tumor Atlas (PBTA), a collection of genomic data for over 1,000 pediatric brain tumors. I remember being inspired by the open crowd-sourced Deep Review manuscript2 and a bit of Twitter brainstorming with Casey Greene, Director of Alex’s Lemonade Stand Foundation’s (ALSF) Childhood Cancer Data Lab at the time, to maximize the impact of the available PBTA data.
In 2019, just in time for Childhood Cancer Awareness Month that year, D3b, along with ALSF, CBTN, and many collaborators, announced the OpenPBTA, which we intended to be a ”speedy” project. The COVID-19 pandemic (among other things) had other plans. But, we powered through what became a fun, challenging, educational, and immensely rewarding project. What began as a “crazy idea” later became the first open and crowd-sourced analysis of pediatric brain tumor genomic data that allowed for contributions from experts across the globe. Aside from the manuscript and data distribution resource we have provided the pediatric oncology community, I frequently reflect on how grateful I am to Jaclyn Taroni, current Director of ALSF’s Childhood Cancer Data Lab and OpenPBTA collaborator, and the entire Data Lab team for their perseverance and commitment to training and maintaining standard processes throughout the duration of the project. The OpenPBTA has reshaped not only how my team works together, but also how we collaborate across teams at D3b.
Working on the OpenPBTA was fun, but as the first open analysis project of its kind, it was very challenging!
Turns out building things from scratch is hard. OpenPBTA was no different and although we built the GitHub and Docker infrastructure in the open, we also had to build new processes behind-the-scenes at D3b. Some of the challenges included:
Learning the technologies. Until the OpenPBTA, D3b was focused on brain tumor specimen and data collection, model generation, sequencing, and harmonization. It was time to propel analysis of the PBTA forward. D3b’s bioinformatics analytical team(s) were in their infancy and admittedly, we had no documentation in place for working collaboratively or reproducibly. Our bioinformatics scientists weren’t routinely and uniformly using GitHub or Docker, and we certainly were not doing code reviews. Combining the Data Lab’s technical expertise with D3b’s domain knowledge of brain tumors was an ideal pairing.
Building a framework for the histologies file. Ahhh, the histologies file. After my experience leading the genomic analysis of patient-derived xenograft (PDX) models from the multi-institutional Pediatric Preclinical Testing Program3, my first goal with OpenPBTA was to create a histologies file with essential and harmonized fields. Looking back at some of my initial code to create this file, I was combining data from >25 files - nothing was in one place. It was unsustainable having to manually re-pull and re-collate files and in the spirit of OpenPBTA, we wanted something reproducible. At somewhere around 15 data releases, we decided with our ADAPT (Advance Data and Platform Technologies) Unit that this data needed to be in a database. Since then, we have built and currently maintain a data warehouse of tables written in dbt (data build tool). The data warehouse pulls together genomic file attributes from Kids First, specimen attributes from Nautilus, and patient attributes from RedCap into a histologies table used to generate [.inline-snippet]pbta-histologies-base.tsv[.inline-snippet] for OpenPBTA data releases. As we harmonize data and generate additional data modalities (single nucleus RNA-Seq, proteogenomics, etc.), we continue building upon the histologies table in the D3b data warehouse.
Developing systematic data releases. D3b serves as the Data Resource Center for Kids First, so we had the majority of somatic pipelines already benchmarked and had processed data ready to go. (Copy number was something we benchmarked during OpenPBTA). We had to figure out how to merge and release the data systematically, such that if we added or removed data, we could easily update our merged matrices. We divided these tasks across people and did this mostly ad hoc in CAVATICA using the [.inline-snippet]pbta-histologies-base.tsv[.inline-snippet] file as the sample cohort, but ad hoc requests often disrupted scrum sprint planning or had to be integrated among the center priorities and were mistake-prone. I’m not sure if we ever figured out how to do this well during OpenPBTA (heck, we had 23 data releases!), but in the time since, we developed an Apache Airflow DAG (directed acyclic graph) to schedule weekly merges for each primary analysis file (fusion calls, expression values, consensus SNVs, CNV calls, SV calls, splice variants). Further, we created a pre-release QC module to ensure that data release files are not missing samples nor are we including additional samples not in the histologies file. This enables a more real-time integration of incoming samples into the merged matrices used for data releases and molecular tumor board analyses (more on that later).
Collaborating across D3b teams. D3b currently has nearly 100 full-time employees spread across seven multi-team units (panel A, below) and in panel B, I highlight the steps within the OpenPBTA projects in which each of five D3b units were integral to our data releases. Given the scale of D3b, we periodically strategize how best to align priorities across units and teams. The OpenPBTA taught us how to better communicate across teams. For example, we have created cross-team sprint planning meetings, regularly scheduled 1:1s between leads, synchronized unit and team priorities by quarter, and created resource management plans to ensure staffing across center projects.
Working on the OpenPBTA was a learning experience and improved collaboration within our team.
While the Data Lab adopted R [.inline-snippet]tidyverse[.inline-snippet] and its style guide, I was not nearly proficient. Turns out code review will get you up to speed quickly! Below are some ways that working on the OpenPBTA has influenced the standard practices in our Bioinformatics Translational Pediatric Oncology Team:
- GitHub and repository organization - we create research project-specific GitHub repositories following a similar structure to that of OpenPBTA, for example, the alternative lengthening of telomeres project
- Data releases - we maintain versioned project-specific data release files on AWS S3
- Docker - we require each project-specific repository to contain a Dockerfile with required packages used to run all analyses
- R tidyverse - we adopted R tidyverse and its style guide for writing code across repositories
- Pull requests (PRs) and code review - while each bioinformatics scientist leads 1-2 research projects and is responsible for maintenance of their repositories, all scripts and modules are added through a PR model and code is reviewed by 1-2 others in the group.
- GitHub actions - we use GitHub actions for OpenPedCan as our continuous integration tool of choice for running testing data through analysis modules upon PR (more on this project below!)
You can read more of the Data Lab’s thoughts on setting up data-intensive research for success and how carrying out the work of OpenPBTA influenced their own philosophy and processes.
Co-leading the OpenPBTA was a highlight of my career.
We have expanded OpenPBTA into an open pan-pediatric cancer analysis project, OpenPedCan, which now contains over 2,700 PBTA tumors harmonized with data from TARGET, GMKF Neuroblastoma, and CHOP’s Division of Genomic Diagnostics. Perhaps the most rewarding part about working on the OpenPBTA is that it permeated various domains within D3b in real-time. Specifically, the data we released was simultaneously used as the foundation for research and molecular tumor board analyses (below).
Molecular Tumor Boards. The Clinical and Translational Data Sciences Unit (CTDS) at D3b leverages the automated merged output from the Airflow DAG and the histologies table from the D3b data warehouse as its underlying data for molecular tumor board-specific analysis modules. To support a more real-time analysis, D3b’s Bioinformatics Unit (BIXU) developer team scaled many OpenPBTA and OpenPedCan modules to work in CAVATICA and are now routinely using a separate Airflow DAG. CTDS works with the Pacific Pediatric Neuro-Oncology Consortium (PNOC) and CHOP’s Frontier precision medicine initiative to support evidence-based therapy recommendations.
D3b and Collaborative Research. We worked with internal and external collaborators to develop new tools (annoFuse and ShinyFuse4, medulloClassifier5) and contribute to research projects6–8. OpenPBTA and OpenPedCan data releases were/are the source data for research projects and we are currently exploring GitHub submodules as a way to pull OpenPedCan data into project-specific repositories to avoid duplicate storage on AWS S3.
Pediatric Molecular Targets Platform. We collaborated with Frederick National Laboratory to create the NCI’s Pediatric Molecular Targets Platform using OpenPedCan as the underlying data source.
A warm thank you to ALSF for supporting the OpenPBTA, the Data Lab for partnering with D3b and investing in each contributor, and to each family whose specimen donations to the CBTN or PNOC made this possible. We are excited to learn how OpenPBTA and OpenPedCan are being used by others and truly hope these data resources will break down barriers to data access and empower researchers to make new discoveries and translate therapies across the pediatric oncology domain.
Jo Lynne Rokita can be contacted by email at rokita@chop.edu.
Bibliography
1. Shapiro, J. A. et al. OpenPBTA: The Open Pediatric Brain Tumor Atlas. Cell Genomics 3, (2023).
2. Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, (2018).
3. Rokita, J. L. et al. Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design. Cell Rep. 29, 1675–1689.e9 (2019).
4. Gaonkar, K. S. et al. annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions. BMC Bioinformatics 21, 577 (2020).
5. Rathi, K. S. et al. A transcriptome-based classifier to determine molecular subtypes in medulloblastoma. PLoS Comput. Biol. 16, e1008263 (2020).
6. Kline, C. et al. Upfront biology-guided therapy in diffuse intrinsic pontine glioma: therapeutic, molecular, and biomarker outcomes from PNOC003. Clin. Cancer Res. (2022) doi:10.1158/1078-0432.CCR-22-0803.
7. Dang, M. T. et al. Macrophages in SHH subgroup medulloblastoma display dynamic heterogeneity that varies with treatment modality. Cell Rep. 34, 108917 (2021).
8. Stundon, J. L. et al. Alternative lengthening of telomeres (ALT) in pediatric high-grade gliomas can occur without ATRX mutation and is enriched in patients with pathogenic germline mismatch repair (MMR) variants. Neuro. Oncol. 25, 1331–1342 (2023).
This is a guest blog post by Jo Lynne Rokita, PhD, leader of the Bioinformatics Translational Pediatric Oncology Team at the Center for Data-Driven Discovery in Biomedicine (D3b) at Children’s Hospital of Philadelphia (CHOP).
At the Center for Data-Driven Discovery in Biomedicine (D3b), I lead the Bioinformatics Translational Pediatric Oncology Team, a team of bioinformatics scientists. Our mission is to advance pediatric oncology research and precision medicine through collaboration and development of open-source analytical tools, frameworks, and data resources. In 1998, I lost my four year old cousin John Matthew to a brain tumor we now know was likely a diffuse intrinsic pontine glioma. So, it was bittersweet for me to see the Open Pediatric Brain Tumor Atlas (OpenPBTA) manuscript published in Cell Genomics on the last day of brain tumor awareness month this past year1. But let’s rewind.
By the end of 2018, the Children’s Brain Tumor Network (CBTN), through D3b, had generated and released the Pediatric Brain Tumor Atlas (PBTA), a collection of genomic data for over 1,000 pediatric brain tumors. I remember being inspired by the open crowd-sourced Deep Review manuscript2 and a bit of Twitter brainstorming with Casey Greene, Director of Alex’s Lemonade Stand Foundation’s (ALSF) Childhood Cancer Data Lab at the time, to maximize the impact of the available PBTA data.
In 2019, just in time for Childhood Cancer Awareness Month that year, D3b, along with ALSF, CBTN, and many collaborators, announced the OpenPBTA, which we intended to be a ”speedy” project. The COVID-19 pandemic (among other things) had other plans. But, we powered through what became a fun, challenging, educational, and immensely rewarding project. What began as a “crazy idea” later became the first open and crowd-sourced analysis of pediatric brain tumor genomic data that allowed for contributions from experts across the globe. Aside from the manuscript and data distribution resource we have provided the pediatric oncology community, I frequently reflect on how grateful I am to Jaclyn Taroni, current Director of ALSF’s Childhood Cancer Data Lab and OpenPBTA collaborator, and the entire Data Lab team for their perseverance and commitment to training and maintaining standard processes throughout the duration of the project. The OpenPBTA has reshaped not only how my team works together, but also how we collaborate across teams at D3b.
Working on the OpenPBTA was fun, but as the first open analysis project of its kind, it was very challenging!
Turns out building things from scratch is hard. OpenPBTA was no different and although we built the GitHub and Docker infrastructure in the open, we also had to build new processes behind-the-scenes at D3b. Some of the challenges included:
Learning the technologies. Until the OpenPBTA, D3b was focused on brain tumor specimen and data collection, model generation, sequencing, and harmonization. It was time to propel analysis of the PBTA forward. D3b’s bioinformatics analytical team(s) were in their infancy and admittedly, we had no documentation in place for working collaboratively or reproducibly. Our bioinformatics scientists weren’t routinely and uniformly using GitHub or Docker, and we certainly were not doing code reviews. Combining the Data Lab’s technical expertise with D3b’s domain knowledge of brain tumors was an ideal pairing.
Building a framework for the histologies file. Ahhh, the histologies file. After my experience leading the genomic analysis of patient-derived xenograft (PDX) models from the multi-institutional Pediatric Preclinical Testing Program3, my first goal with OpenPBTA was to create a histologies file with essential and harmonized fields. Looking back at some of my initial code to create this file, I was combining data from >25 files - nothing was in one place. It was unsustainable having to manually re-pull and re-collate files and in the spirit of OpenPBTA, we wanted something reproducible. At somewhere around 15 data releases, we decided with our ADAPT (Advance Data and Platform Technologies) Unit that this data needed to be in a database. Since then, we have built and currently maintain a data warehouse of tables written in dbt (data build tool). The data warehouse pulls together genomic file attributes from Kids First, specimen attributes from Nautilus, and patient attributes from RedCap into a histologies table used to generate [.inline-snippet]pbta-histologies-base.tsv[.inline-snippet] for OpenPBTA data releases. As we harmonize data and generate additional data modalities (single nucleus RNA-Seq, proteogenomics, etc.), we continue building upon the histologies table in the D3b data warehouse.
Developing systematic data releases. D3b serves as the Data Resource Center for Kids First, so we had the majority of somatic pipelines already benchmarked and had processed data ready to go. (Copy number was something we benchmarked during OpenPBTA). We had to figure out how to merge and release the data systematically, such that if we added or removed data, we could easily update our merged matrices. We divided these tasks across people and did this mostly ad hoc in CAVATICA using the [.inline-snippet]pbta-histologies-base.tsv[.inline-snippet] file as the sample cohort, but ad hoc requests often disrupted scrum sprint planning or had to be integrated among the center priorities and were mistake-prone. I’m not sure if we ever figured out how to do this well during OpenPBTA (heck, we had 23 data releases!), but in the time since, we developed an Apache Airflow DAG (directed acyclic graph) to schedule weekly merges for each primary analysis file (fusion calls, expression values, consensus SNVs, CNV calls, SV calls, splice variants). Further, we created a pre-release QC module to ensure that data release files are not missing samples nor are we including additional samples not in the histologies file. This enables a more real-time integration of incoming samples into the merged matrices used for data releases and molecular tumor board analyses (more on that later).
Collaborating across D3b teams. D3b currently has nearly 100 full-time employees spread across seven multi-team units (panel A, below) and in panel B, I highlight the steps within the OpenPBTA projects in which each of five D3b units were integral to our data releases. Given the scale of D3b, we periodically strategize how best to align priorities across units and teams. The OpenPBTA taught us how to better communicate across teams. For example, we have created cross-team sprint planning meetings, regularly scheduled 1:1s between leads, synchronized unit and team priorities by quarter, and created resource management plans to ensure staffing across center projects.
Working on the OpenPBTA was a learning experience and improved collaboration within our team.
While the Data Lab adopted R [.inline-snippet]tidyverse[.inline-snippet] and its style guide, I was not nearly proficient. Turns out code review will get you up to speed quickly! Below are some ways that working on the OpenPBTA has influenced the standard practices in our Bioinformatics Translational Pediatric Oncology Team:
- GitHub and repository organization - we create research project-specific GitHub repositories following a similar structure to that of OpenPBTA, for example, the alternative lengthening of telomeres project
- Data releases - we maintain versioned project-specific data release files on AWS S3
- Docker - we require each project-specific repository to contain a Dockerfile with required packages used to run all analyses
- R tidyverse - we adopted R tidyverse and its style guide for writing code across repositories
- Pull requests (PRs) and code review - while each bioinformatics scientist leads 1-2 research projects and is responsible for maintenance of their repositories, all scripts and modules are added through a PR model and code is reviewed by 1-2 others in the group.
- GitHub actions - we use GitHub actions for OpenPedCan as our continuous integration tool of choice for running testing data through analysis modules upon PR (more on this project below!)
You can read more of the Data Lab’s thoughts on setting up data-intensive research for success and how carrying out the work of OpenPBTA influenced their own philosophy and processes.
Co-leading the OpenPBTA was a highlight of my career.
We have expanded OpenPBTA into an open pan-pediatric cancer analysis project, OpenPedCan, which now contains over 2,700 PBTA tumors harmonized with data from TARGET, GMKF Neuroblastoma, and CHOP’s Division of Genomic Diagnostics. Perhaps the most rewarding part about working on the OpenPBTA is that it permeated various domains within D3b in real-time. Specifically, the data we released was simultaneously used as the foundation for research and molecular tumor board analyses (below).
Molecular Tumor Boards. The Clinical and Translational Data Sciences Unit (CTDS) at D3b leverages the automated merged output from the Airflow DAG and the histologies table from the D3b data warehouse as its underlying data for molecular tumor board-specific analysis modules. To support a more real-time analysis, D3b’s Bioinformatics Unit (BIXU) developer team scaled many OpenPBTA and OpenPedCan modules to work in CAVATICA and are now routinely using a separate Airflow DAG. CTDS works with the Pacific Pediatric Neuro-Oncology Consortium (PNOC) and CHOP’s Frontier precision medicine initiative to support evidence-based therapy recommendations.
D3b and Collaborative Research. We worked with internal and external collaborators to develop new tools (annoFuse and ShinyFuse4, medulloClassifier5) and contribute to research projects6–8. OpenPBTA and OpenPedCan data releases were/are the source data for research projects and we are currently exploring GitHub submodules as a way to pull OpenPedCan data into project-specific repositories to avoid duplicate storage on AWS S3.
Pediatric Molecular Targets Platform. We collaborated with Frederick National Laboratory to create the NCI’s Pediatric Molecular Targets Platform using OpenPedCan as the underlying data source.
A warm thank you to ALSF for supporting the OpenPBTA, the Data Lab for partnering with D3b and investing in each contributor, and to each family whose specimen donations to the CBTN or PNOC made this possible. We are excited to learn how OpenPBTA and OpenPedCan are being used by others and truly hope these data resources will break down barriers to data access and empower researchers to make new discoveries and translate therapies across the pediatric oncology domain.
Jo Lynne Rokita can be contacted by email at rokita@chop.edu.
Bibliography
1. Shapiro, J. A. et al. OpenPBTA: The Open Pediatric Brain Tumor Atlas. Cell Genomics 3, (2023).
2. Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, (2018).
3. Rokita, J. L. et al. Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design. Cell Rep. 29, 1675–1689.e9 (2019).
4. Gaonkar, K. S. et al. annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions. BMC Bioinformatics 21, 577 (2020).
5. Rathi, K. S. et al. A transcriptome-based classifier to determine molecular subtypes in medulloblastoma. PLoS Comput. Biol. 16, e1008263 (2020).
6. Kline, C. et al. Upfront biology-guided therapy in diffuse intrinsic pontine glioma: therapeutic, molecular, and biomarker outcomes from PNOC003. Clin. Cancer Res. (2022) doi:10.1158/1078-0432.CCR-22-0803.
7. Dang, M. T. et al. Macrophages in SHH subgroup medulloblastoma display dynamic heterogeneity that varies with treatment modality. Cell Rep. 34, 108917 (2021).
8. Stundon, J. L. et al. Alternative lengthening of telomeres (ALT) in pediatric high-grade gliomas can occur without ATRX mutation and is enriched in patients with pathogenic germline mismatch repair (MMR) variants. Neuro. Oncol. 25, 1331–1342 (2023).
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In this blog post, I’d like to give an overview of the refine.bio refactoring process and web accessibility considerations. Through this process, our goal is to enhance the site usability and performance by improving the code quality and making the application more accessible. But before going into more details about them, let me provide you a quick history of refine.bio.
Welcome to the Data Lab’s November Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
Here at the Data Lab, we're all about, well, data! We believe that data sharing and accessibility is key to accelerating the research process, and ultimately to improving outcomes for childhood cancer patients. So, we were excited to learn that one of the goals of the NCI/NIH initiative, the Childhood Cancer Data Initiative (CCDI), is to build up a Data Ecosystem that will facilitate pediatric cancer researchers' ability to explore and collect data from disparate resources. Although this Ecosystem is still in the early stages, several components are already being developed and are available for researchers to use! One component that is particularly interesting to us is the CCDI's Childhood Cancer Data Catalog (CCDC).
Welcome to the October Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
Welcome to the September Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
At the Data Lab, we are constantly looking for ways to enhance the tools we build for pediatric cancer researchers. Earlier this year, we launched the Single-cell Pediatric Cancer Atlas portal, a database of uniformly-processed single-cell data from pediatric cancer clinical samples. One way we felt the portal could be even more beneficial to pediatric cancer researchers is with a ready-to-go workflow that takes in single-cell data and prepares it for downstream analyses such as unsupervised clustering.
The Data Lab is excited to announce our next virtual workshop running from September 19-23, 2022! In this workshop, Data Lab staff will introduce researchers studying pediatric cancer to the R programming language, the Tidyverse R packages for data science, single-cell RNA-seq data analysis, and pathway analysis.
The Data Lab teaches data science courses targeted toward pediatric cancer researchers that introduce topics such as analysis of gene expression in bulk and single-cell data and principles of reproducible research. I wrote previously about how we use RStudio Server for our remote courses to simplify setup, and I wanted to write a bit more about some of the instructional practices we use so that our participants get the best experience we can provide. In particular, I wanted to talk about our use of live coding to facilitate active learning, and one of the tools we developed to make our course development just a bit easier.
Welcome to the August Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations.
Often when building a server-client web application, we will encounter a situation where we want to send requests to our API in the chronological order that they occur on the client. Due to the asynchronous nature of these requests, it might not be possible to send them in the same callback for the event that triggered them. This is because we want to use the response from the previous request to craft our current one. A solution to this problem would be to implement a queue. Instead of calling the API immediately after events occur, implementing a queue ensures the latest data is sent with any request.
Welcome to the July Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
Welcome to the Childhood Cancer Data Lab’s new blog feature, the monthly Scientific Community Bulletin! At the start of each month, we will share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Our goal is to promote learning opportunities and highlight some of the excellent resources that our community provides.
At the Data Lab, our science team has a practice where an individual team member shares something that they recently figured out (or didn’t totally figure out yet) on a biweekly basis. We call this short 5-10 minute presentation How I Solved This, and it’s a great way to formally share (often hard-won) knowledge with each other. In this post, we thought we’d share how we solved something with the `renv` package with you.
The Childhood Cancer Data Lab builds resources guided by the most pressing needs of our primary users: pediatric cancer researchers. As the Data Lab's UX Designer, I conduct research activities with scientists like usability evaluations, semi-structured interviews, and card sorts to gain insight into their activities, processes, pain-points, and behaviors. I work with scientists and engineers at the Data Lab to use this information to improve existing products and services or to create new ones.
The Data Lab is excited to announce that our next training workshop is taking place in-person on Friday, June 10, 2022! During this full day workshop, instructors will introduce principles and techniques to achieve reproducible results in computational cancer research. We’ll show you the fundamentals of commonly-used approaches in reproducibility that you can apply to increase the impact of your research by making your findings more robust and reliable!
The Childhood Cancer Data Lab is growing as a resource for pediatric cancer researchers and we have more to offer to our community now, than ever before. Transitioning to our new and improved website is an exciting milestone, and here, we look forward to sharing progress, introducing new initiatives, and cultivating more opportunities to support childhood cancer research. Welcome to our new virtual home!
The Single-cell Pediatric Cancer Atlas (ScPCA) project began in 2019 when Alex’s Lemonade Stand Foundation (ALSF) funded 10 awards for single-cell profiling of pediatric cancer samples. The goal was to produce an atlas of gene expression profiles for a variety of childhood cancer types from different organ sites.
The CCDL team includes science, engineering, and design expertise. Combining these three disciplines in different ways across projects enables us to carry out our mission.
Here at the CCDL we value putting publicly available data to work. For example, we are currently processing and normalizing 1.5 million publicly available gene expression samples totaling ~$1.5 billion research dollars expended.
Like many teams that work with large amounts of external software, we run into issues with our transitive dependencies. In general, transitive dependencies are a hard problem to solve.
Though technology can introduce great benefit into our lives, it is often accompanied by a substantial amount of time and some expected frustration before we can reap the rewards. The time spent learning a new technology is what we usually call a learning curve.
The workshop will last from 9AM to 5PM on October 14th, 15th, and 16th at the CCDL offices at 1429 Walnut St Philadelphia, PA, 19102.
MultiPLIER is a machine learning approach that brings big data to bear on rare diseases. It’s also an example of the scientific approach and ethos of the CCDL, and the publication is a great opportunity to share how the CCDL is developing new technologies to accelerate research into cures for childhood cancers!
The Childhood Cancer Data Lab powered by Alex's Lemonade Stand Foundation is hosting a workshop to introduce childhood cancer researchers to reproducible analysis of bulk and single-cell transcriptomic data.
The Childhood Cancer Data Lab (CCDL), an initiative of Alex's Lemonade Stand Foundation develops tools, trainings, and methods to empower childhood cancer researchers. The work at the CCDL is focused and impactful. There are multiple opportunities and challenges for you to apply and grow your skills as a scientist or as an engineer.
The Childhood Cancer Data Lab powered by Alex's Lemonade Stand Foundation is hosting a workshop to introduce childhood cancer researchers to reproducible analysis of bulk and single-cell transcriptomic data.
At this hands-on, 3-day session held in Houston, researchers learned data science skills that could accelerate their own work. Drawing on skills learned at the workshop, childhood cancer researchers can perform basic analyses of their work to make informed decisions on how to proceed with their own research. Don’t just take our word for it, though. Read more about the workshop’s incredibly valuable benefits through its attendees’ perspectives.
The goal of our refine.bio project is to download, process, and make available gene expression datasets that can be analyzed together, or in parts, depending on a researcher’s need. Childhood cancer researchers need to be able to use data generated through multiple profiling technologies including microarrays and RNA-sequencing.
There are countless log blog posts out there about the benefits of good logging, how to log well, and how much to log. Going through them all can be a real log blog slog. Wouldn't it be cool if you could log like this:logger.info("Something happened!", job=job.id, user=user.id) and get an easily searchable output.
Caffeine is a stimulant that can induce alertness in certain individuals when consumed at an appropriate quantity. Caffeine is often obtained by ingesting caffeine-containing solutions. However, no protocol for obtaining caffeine from dehydrated, roasted beans using materials typically available in a Philadelphia office has been described in the published literature.
Alex’s Lemonade Stand Foundation (ALSF) staunchly believes that stronger scientific sharing practices will accelerate the pace of discovery and finding cures for children with cancer. Robust sharing improves reproducibility, minimizes redundant studies and maximizes our return on research investment.
Earlier this year, Alex’s Lemonade Stand Foundation identified single-cell gene expression profiling as an opportunity to build an atlas of cell types within tumors that could be broadly reused by pediatric cancer researchers.
This year was a big one for the CCDL. In our mission to empower pediatric cancer experts poised for big discoveries with the knowledge, data and methods to reach them we launched a software product, developed and delivered training workshops on single-cell and bulk RNA-seq analysis, and hired our data science team among other milestones.
I’m a scientist at Sage Bionetworks, a nonprofit research organization in Seattle, WA. My work focuses on a family of rare pediatric diseases (NF): neurofibromatosis type 1, type 2, and schwannomatosis.
Our particular process is designed to source opportunities from our team members and external stakeholders, convert those opportunities into a set of potential goals, and then select the goals that we expect will most advance our mission.
The ability to restore scroll position is often critical for website usability. It helps users keep the flow of navigation when going back and forth between different pages. Most modern browsers take care of restoring the scroll position automatically, but it doesn’t always work for Single Page Applications where the content is generated on the client’s side, often asynchronously.
Carnegie Mellon University Libraries is partnering with the Childhood Cancer Data Lab (CCDL), founded by Alex’s Lemonade Stand Foundation, to host a Data Analysis workshop using CCDL materials.
The CCDL will have a team of scientists at the American Association for Cancer Research 2020 Annual Meeting in sunny San Diego! Our team members are excited to talk to researchers studying pediatric cancer at Booth 1601.
To help keep pediatric cancer research moving forward, here are 3 ways the CCDL is helping the research community during this time: refine.bio, virtual workshops, and the Open Pediatric Brain Tumor Atlas project.
We know that pandemic-related university closures mean that the demand for opportunities for pediatric cancer researchers to increase their analytical skills has never been higher. As such, we are delighted to announce a pilot virtual workshop running from May 4-8, 2020!
Here at the Childhood Cancer Data Lab, we value transparency and the practice of open science. Much of the work we’ve done and the products that we build hinge on the generosity and openness of other scientists. In this post, as part of National Brain Tumor Awareness month, we want to talk about a project that our science team has been working on over the last few months (and to do so in a way that aligns with our values).
The workshop will take place on June 22 - 26, 2020 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
When the CCDL (along with everyone else) realized that we would have to conduct our bioinformatics training workshops remotely, we had to make some quick decisions about how we were going to do it. Most of the instructional materials for our in person workshops were already online, so we knew we had a good base to work from. We just needed to figure how to adapt the live instruction.
At Alex’s Lemonade Stand Foundation’s Childhood Cancer Data Lab, we’re excited to be helping out with an upcoming event hosted by the Children’s Tumor Foundation. If you participate, you may meet members of our team who are mentoring and judging.
The workshop will take place on March 22 - 26, 2021 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation
The workshop will take place on June 28- July 2, 2021 from noon to 5pm eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
Hack4Rare is a virtual event that calls for healthcare startups, developers, solutions architects, and hackathon enthusiasts to join researchers, clinicians and patients in developing solutions built around a number of rare diseases including neurofibromatosis, PTEN Hamartoma Tumor Syndrome, RASopathies and Desmoid Tumors.
The workshop will take place on September 20 - 24, 2021 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
Introducing refine.bio examples. Here, users can access a variety of example analyses implemented in R, such as clustering and heat maps, differential expression analysis, and pathway analysis, for use with refine.bio data.
The workshop will take place on November 1-5, 2021 from noon to 5pm eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with our staff available for consultation.
I work at the Childhood Cancer Data Lab, where we use very big data to find cures for childhood cancers. To move data around the internet at very high speeds, we are forced to use a proprietary software suite called Aspera. If somebody could make a Free Software alternative, the future of the internet would be way more awesome! Best of all, you can be the one to do it!
When my daughter Alex was diagnosed with cancer and throughout her battle, we saw how our community of people rallied around our family. No one knew quite how to help, but they were willing to do whatever was needed to ease the burden we faced.
'Work smarter not harder’ is useless advice if you don’t know how to ‘work smarter’. But the Childhood Cancer Data Lab's work and processes may be the smartest I’ve ever had the pleasure of learning and adopting.
The Data Lab will hold our first virtual workshop of the year from March 14-18, 2022!In this workshop, we will introduce researchers studying pediatric cancer to the R programming language, the Tidyverse R packages for data science, single-cell RNA-seq data analysis, and pathway analyses.
At the Data Lab, we are big proponents of automating the boring stuff so we can spend more time thinking about the fun stuff. But how exactly do we do that, and what does it mean to automate the boring stuff?
November marked the final Childhood Cancer Data Lab training workshop for 2021. We held four week-long virtual workshops this year, teaching 88 researchers the data science skills they need to examine their own data.
Before working as a Data Scientist at the Childhood Cancer Data Lab, I spent time in my PhD and post-doctoral fellowship in two very different research environments. Each had their own unique way of doing research. I found that some things worked really well and others were not as successful.
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