Creating an open source workflow to uniformly process data for the Single-cell Pediatric Cancer Atlas portal
Last year, the Data Lab launched the Single-cell Pediatric Cancer Atlas (ScPCA) Portal, which today holds uniformly processed single-cell gene expression data obtained from 8 separate labs, over 500 samples, and representing 52 cancer types. The portal is still growing as we continue to receive and process raw data from ScPCA investigators! All uniformly processed data is made available for download on the ScPCA Portal, giving researchers easy access to a growing database of summarized gene expression data and metadata to utilize for their own research.
But how exactly did we make sure that all of the data was uniformly processed? And how are we able to ensure uniform processing for incoming samples as the portal continues to grow? We invested time up-front to build a reusable and reproducible pipeline for processing single-cell RNA-seq data using the Nextflow workflow manager called scpca-nf. In this post, we’ll go behind the scenes and tell you a little about scpca-nf – how it works, why we like it, and how you can use it too!
What is scpca-nf?
scpca-nf is a reproducible Nextflow-based workflow that takes FASTQ files from single-cell or single-nuclei RNA-seq samples and returns a gene by cell count matrix and associated metadata stored within a SingleCellExperiment object. For each input sample, the workflow performs the following steps:
- Gene expression quantification – Salmon is used to map reads to a reference transcriptome and alevin-fry is used to generate gene by cell counts matrices. For more insight into why we use alevin-fry for gene expression quantification, see the FAQ section of the ScPCA documentation.
- Removal of empty droplets – The gene by cell count matrix is imported into R to create a SingleCellExperiment object which is subject to filtering of any droplets that do not contain cells. Two different objects are saved, one containing the unfiltered counts matrix and one with the empty droplets removed.
- Filtering low quality cells – Further filtering is performed to remove any potential cells that may have a low number of genes detected or high mitochondrial count, suggesting that the cell is likely to be dead or dying. Here, we remove cells with greater than a 75% chance of being poor quality as calculated by miQC and cells with fewer than 100 genes detected per cell.
- Post-processing – The counts for cells that remain after removal of empty droplets and low quality cells then undergo normalization and log-transformation. Finally, dimensionality reductions are calculated using both principal component analysis (PCA) and UMAP and are stored in the final processed object.
For more detailed information on the individual steps performed in the workflow, see the processing information page in the ScPCA documentation.
For each sample in the portal, the scpca-nf workflow produces three versions of the count matrix that can be downloaded from the portal as SingleCellExperiment objects: the unfiltered count matrix, filtered count matrix, and final processed count matrix. After download, any of the three objects can be directly read into R to begin analysis (see Getting started with an ScPCA dataset). The processed objects can be particularly useful as they contain normalized data and dimensionality reduction, allowing researchers to jump straight into asking important biological questions and performing downstream analyses.
Why we like scpca-nf
Having a workflow like scpca-nf has allowed us to maintain a reproducible and uniform way to process the data available on the ScPCA portal. But what are some of our favorite features of this workflow, and what benefits have we seen over the lifetime of the project?
- Reproducible – Due to the number of participating labs and our hope to expand the portal in the future, we needed a workflow that would allow us to process data intermittently over a long period of time in the same exact way. Using Nextflow helps us accomplish this goal by performing all processing within container environments (docker or singularity), ensuring consistent versions of tools and dependencies.
- Fast and efficient – Using Nextflow means we can take advantage of parallel processing, and process more samples faster. For gene expression quantification we use alevin-fry, which requires less computing power and memory than tools like Cell Ranger without compromising accuracy. The combined speed of parallel processing and efficiency of alevin-fry means samples are processed quickly and efficiently.
- Modular setup – We can easily add new features. When we want to make changes or additions to the analysis steps, we don’t need to create a brand new workflow – we can simply add new steps to our existing workflow. This was particularly helpful when we added support for the other data types found on the portal (bulk RNA-seq, CITE-seq, spatial transcriptomics, and multiplexed data). Scpca-nf can process any of these data types, all within the same workflow. We can also re-process samples to incorporate changes or new features, and with a few tricks we make sure that Nextflow skips repeating any time-consuming steps, saving us even more time!
Read more about workflow managers, like Nextflow, and their benefits in our blog post on Automating analyses with workflow managers.
How can you get started using scpca-nf to analyze your own samples?
Does scpca-nf sound like it could be useful to you and your research? You can try it out for yourself! Our pipeline is open source and available for all to use to analyze their own set of single-cell and single-nuclei RNA-seq samples. By using scpca-nf, you will be able to take advantage of the tools we have set up to automate the initial pre-processing steps typically performed when analyzing single-cell data, decreasing the overall time you spend analyzing each sample. Additionally, you can take advantage of the low computational cost of alevin-fry and Nextflow’s ability to parallelize jobs, and with one command analyze multiple samples at once in a reproducible way!
So what do you need to get started analyzing your single-cell data faster and more reproducibly?
- Organize your single-cell data and create a metadata table where each row contains the required information for each sample to be processed (e.g., sample identifier, path to FASTQ files)
- Install Nextflow and Docker.
- Test it out on your samples!
For step-by-step instructions on how to get started using scpca-nf to analyze your own data see the instructions for processing your samples in the scpca-nf github repository.
Have any questions or run into trouble trying to use scpca-nf? Tell us about it and file an issue in the scpca-nf github repository. If you have questions about the ScPCA Portal or if you have single-cell data related to childhood cancer that you are interested in including in the ScPCA Portal you can reach out to us at scpca@ccdatalab.org. Happy processing!
Last year, the Data Lab launched the Single-cell Pediatric Cancer Atlas (ScPCA) Portal, which today holds uniformly processed single-cell gene expression data obtained from 8 separate labs, over 500 samples, and representing 52 cancer types. The portal is still growing as we continue to receive and process raw data from ScPCA investigators! All uniformly processed data is made available for download on the ScPCA Portal, giving researchers easy access to a growing database of summarized gene expression data and metadata to utilize for their own research.
But how exactly did we make sure that all of the data was uniformly processed? And how are we able to ensure uniform processing for incoming samples as the portal continues to grow? We invested time up-front to build a reusable and reproducible pipeline for processing single-cell RNA-seq data using the Nextflow workflow manager called scpca-nf. In this post, we’ll go behind the scenes and tell you a little about scpca-nf – how it works, why we like it, and how you can use it too!
What is scpca-nf?
scpca-nf is a reproducible Nextflow-based workflow that takes FASTQ files from single-cell or single-nuclei RNA-seq samples and returns a gene by cell count matrix and associated metadata stored within a SingleCellExperiment object. For each input sample, the workflow performs the following steps:
- Gene expression quantification – Salmon is used to map reads to a reference transcriptome and alevin-fry is used to generate gene by cell counts matrices. For more insight into why we use alevin-fry for gene expression quantification, see the FAQ section of the ScPCA documentation.
- Removal of empty droplets – The gene by cell count matrix is imported into R to create a SingleCellExperiment object which is subject to filtering of any droplets that do not contain cells. Two different objects are saved, one containing the unfiltered counts matrix and one with the empty droplets removed.
- Filtering low quality cells – Further filtering is performed to remove any potential cells that may have a low number of genes detected or high mitochondrial count, suggesting that the cell is likely to be dead or dying. Here, we remove cells with greater than a 75% chance of being poor quality as calculated by miQC and cells with fewer than 100 genes detected per cell.
- Post-processing – The counts for cells that remain after removal of empty droplets and low quality cells then undergo normalization and log-transformation. Finally, dimensionality reductions are calculated using both principal component analysis (PCA) and UMAP and are stored in the final processed object.
For more detailed information on the individual steps performed in the workflow, see the processing information page in the ScPCA documentation.
For each sample in the portal, the scpca-nf workflow produces three versions of the count matrix that can be downloaded from the portal as SingleCellExperiment objects: the unfiltered count matrix, filtered count matrix, and final processed count matrix. After download, any of the three objects can be directly read into R to begin analysis (see Getting started with an ScPCA dataset). The processed objects can be particularly useful as they contain normalized data and dimensionality reduction, allowing researchers to jump straight into asking important biological questions and performing downstream analyses.
Why we like scpca-nf
Having a workflow like scpca-nf has allowed us to maintain a reproducible and uniform way to process the data available on the ScPCA portal. But what are some of our favorite features of this workflow, and what benefits have we seen over the lifetime of the project?
- Reproducible – Due to the number of participating labs and our hope to expand the portal in the future, we needed a workflow that would allow us to process data intermittently over a long period of time in the same exact way. Using Nextflow helps us accomplish this goal by performing all processing within container environments (docker or singularity), ensuring consistent versions of tools and dependencies.
- Fast and efficient – Using Nextflow means we can take advantage of parallel processing, and process more samples faster. For gene expression quantification we use alevin-fry, which requires less computing power and memory than tools like Cell Ranger without compromising accuracy. The combined speed of parallel processing and efficiency of alevin-fry means samples are processed quickly and efficiently.
- Modular setup – We can easily add new features. When we want to make changes or additions to the analysis steps, we don’t need to create a brand new workflow – we can simply add new steps to our existing workflow. This was particularly helpful when we added support for the other data types found on the portal (bulk RNA-seq, CITE-seq, spatial transcriptomics, and multiplexed data). Scpca-nf can process any of these data types, all within the same workflow. We can also re-process samples to incorporate changes or new features, and with a few tricks we make sure that Nextflow skips repeating any time-consuming steps, saving us even more time!
Read more about workflow managers, like Nextflow, and their benefits in our blog post on Automating analyses with workflow managers.
How can you get started using scpca-nf to analyze your own samples?
Does scpca-nf sound like it could be useful to you and your research? You can try it out for yourself! Our pipeline is open source and available for all to use to analyze their own set of single-cell and single-nuclei RNA-seq samples. By using scpca-nf, you will be able to take advantage of the tools we have set up to automate the initial pre-processing steps typically performed when analyzing single-cell data, decreasing the overall time you spend analyzing each sample. Additionally, you can take advantage of the low computational cost of alevin-fry and Nextflow’s ability to parallelize jobs, and with one command analyze multiple samples at once in a reproducible way!
So what do you need to get started analyzing your single-cell data faster and more reproducibly?
- Organize your single-cell data and create a metadata table where each row contains the required information for each sample to be processed (e.g., sample identifier, path to FASTQ files)
- Install Nextflow and Docker.
- Test it out on your samples!
For step-by-step instructions on how to get started using scpca-nf to analyze your own data see the instructions for processing your samples in the scpca-nf github repository.
Have any questions or run into trouble trying to use scpca-nf? Tell us about it and file an issue in the scpca-nf github repository. If you have questions about the ScPCA Portal or if you have single-cell data related to childhood cancer that you are interested in including in the ScPCA Portal you can reach out to us at scpca@ccdatalab.org. Happy processing!
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We are excited to announce that our next virtual workshop, Introduction to Single-cell RNA-Seq, will run from June 10-14, 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, single-cell RNA-seq data analysis, and annotating cell types.
Applications are open for the Data Lab's next workshop! We are holding a two-day course on Reproducible Research Practices and the Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project from May 14-15, 2024. Please note that the OpenScPCA module is an optional part of the workshop. The course begins with an introduction to principles and techniques to achieve reproducible results in computational cancer research. On day two, you can choose to continue the workshop and learn how to put your skills to use for OpenScPCA, our new pediatric cancer research project.
Are you attending the American Association for Cancer Research (AACR) annual meeting in San Diego, CA? Visit the Alex’s Lemonade Stand Foundation (ALSF) Grants and Data Lab teams at booth 3755 in the exhibit hall from April 7-10 and during poster sessions on April 8. We will announce a new collaborative project and share exciting news about the Single-cell Pediatric Cancer Atlas Portal and training opportunities!
Did you know that 70% of the Alex’s Lemonade Stand Foundation (ALSF) Childhood Cancer Data Lab team are currently women? Advancing our mission to empower childhood cancer researchers with knowledge, data, and tools would not be possible without their expertise. On the International Day of Women and Girls in Science, we are excited to introduce you to these women who integrate science, engineering, and design to tackle some of the greatest challenges faced by the pediatric cancer research community!
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There is an old joke in computer science about how there are only two hard things: cache invalidation, naming things, and off-by-one errors. I’ll leave aside the first one as beyond my own expertise, but the second comes up all the time in my work as a biological data scientist. Naming variables and functions in my code is a constant struggle, but one I have to deal with on my own or with my team. Much bigger problems come up when trying to deal with all the various ways that people across the world use names when talking about the diseases they work on, the types of cells they are looking at, the experimental methods they are using, and just about every other aspect of their studies.
Applications are open for the Data Lab's next workshop! We will be holding a Reproducible Research Practices Course in-person on October 24-25, 2023. 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! To ensure that workshop attendees have a great hands-on experience, there will be a very limited number of seats available.
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 year. But let’s rewind.
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In 2019, Alex’s Lemonade Stand Foundation (ALSF) established the Single-cell Pediatric Cancer Atlas (ScPCA) through awards for data generation and to create an atlas of single-cell gene expression profiles of pediatric cancers of different types and from different organ sites. The Data Lab launched the ScPCA Portal in 2022 to make uniformly processed, summarized single-cell and single-nuclei RNA-seq data and de-identified metadata available for download. The ScPCA Portal also supports other data modalities, such as bulk RNA-seq, CITE-seq, and spatial transcriptomics. The ScPCA Portal currently hosts data for over 500 pediatric tumor and patient-derived xenograft samples from more than 50 cancer types, and continues to grow. The Data Lab is seeking contributions to the ScPCA Portal from researchers with existing single-cell datasets.
We are excited to announce that our next workshop, Introduction to Single-cell RNA-Seq, will take place in-person from June 13-15, 2023! 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, annotating cell types, and more. The 3-day course will take place from 9am-5pm Eastern time in Bala Cynwyd, PA, just outside of Philadelphia. Travel reimbursement (up to a certain amount) is available for qualifying participants.
The Childhood Cancer Data Lab maintains a collection of uniformly processed single-cell data from pediatric cancer clinical samples and xenografts in the Single-cell Pediatric Cancer Atlas (ScPCA) Portal. Although access to preprocessed data saves researchers time, we know that the downloads from the ScPCA Portal are only the starting point. That’s why we’ve created downstream analysis workflows for commonly performed analyses. Instead of writing code wholesale, you can analyze data once you’ve configured these workflows.
We are excited to announce that our next virtual workshop, Introduction to Single-cell RNA-Seq, will run from May 15-19, 2023! 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 annotating cell types.
Are you attending the American Association for Cancer Research (AACR) annual meeting in Orlando, FL this year? Visit Alex's Lemonade Stand Foundation (ALSF) at booth 369 in the exhibit hall from April 16-19! You'll find information about ALSF's grants program, the Childhood Cancer Data Lab and more. The Data Lab will also be holding office hours during select time slots.
The Data Lab is excited to announce that our next training workshop will be held virtually from March 13-17, 2023! During this workshop, we will cover advanced topics in the analysis of single-cell RNA-seq data for researchers studying pediatric cancer. The workshop will take place each day from 12-5pm 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. You’ll need a laptop with internet access and to install Zoom and Slack. You will log into an RStudio Server hosted by the Data Lab from your web browser. Pediatric cancer researchers are encouraged to apply now!
In September 2022, the Open Pediatric Brain Tumor Atlas (OpenPBTA) project culminated (for now) in a preprint on bioRxiv. This project, started in late 2019 and co-organized with the Center for Data Driven Discovery in Biomedicine (D3b) at Children’s Hospital of Philadelphia (CHOP), is a collaborative effort to comprehensively describe the Pediatric Brain Tumor Atlas (PBTA), a collection of multiple data types from tens of tumor types (read more about why crowdsourcing expertise for the study of pediatric brain tumors is important here). The project is designed to allow for contributions from experts across multiple institutions. We’ve conducted analysis and drafting of the manuscript openly on the version-control platform GitHub from the project’s inception to facilitate those contributions.
Recently, we told you about the Single-cell Pediatric Cancer Atlas (ScPCA) downstream analysis workflow. This 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 this 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!
The Data Lab is excited to announce that our next training workshop will be held in-person from January 31-February 2, 2023! During this workshop, we will cover advanced topics in the analysis of single-cell RNA-seq data for researchers studying pediatric cancer. The 3-day course will take place from 9am-5pm Eastern time in Bala Cynwyd, PA, just outside of Philadelphia. Travel reimbursement is available for qualifying participants.
Welcome to the Data Lab’s December 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!
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.
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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.
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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.
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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!
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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.
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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.
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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.
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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.
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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.
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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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