Automating analyses with workflow managers
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?
For many of the repetitive analysis and day-to-day tasks we perform, we implement workflows and automate these workflows with workflow managers. Workflows help us create single streamlined processes that can be run repeatedly for multiple samples and produce the same results. Not only do we save analysis time, but we also save time testing and adding features to our analysis. Changes only have to be made in one place, and that change is applied across all samples!
This approach allows us to spend less time repeating the same task over and over again and more time working on developing new analyses, exploring new datasets, and helping teach researchers like you!
So what are workflows and workflow managers?
A workflow refers to any number of steps or tasks joined together in a particular order to be executed in a repetitive manner and produce a desired outcome. Workflows can be as simple as a two-step process requiring one input to complicated multi-step processes taking multiple inputs and returning multiple outputs at various stages.
Workflow managers take these multi-part workflows and introduce automation of the individual tasks to increase productivity, eliminate manual repetition, and reduce errors. The ultimate goal of using a workflow manager is to translate a set of mundane tasks into a structured, automated set of tasks that can be repeated constantly with efficiency and accuracy to produce the desired output when provided a specific input.
When should you use workflow managers?
Building reproducible workflows run by workflow managers helps us by automating the repetitive, boring tasks, allowing us to clear our minds to focus on the more important tasks. However, knowing when it’s time to utilize automated workflows is a skill in itself. Setting up a workflow is a time intensive process, but when implemented in the right situations, it can save a lot of time in the long run.
What makes a task an ideal candidate for automation?
- Repetitive - If a task is something that is repeated more than three times, there’s a good chance that it would benefit from automation. When you start to copy and paste your code in multiple places, that’s a sign that automation could benefit you! Or if you need to analyze multiple samples through the same multi-step process, automation will save you time and manual effort.
- Error-prone - Tasks that are prone to human error (e.g. typos) are good candidates for workflow managers. Examples include having to manually enter in file paths throughout code or data entry.
Why do we need workflow managers and how do they help us?
Two examples of workflow managers that we use at the Data Lab are Nextflow and Snakemake. Both of these tools allow for multi-step workflow management, and creating workflows that are reproducible and scalable. They both do this by utilizing modular workflows. A modular workflow is one that separates each individual task into its own unit of work with each process having its own input and output inside of the overarching workflow. Using a modular setup, like those implemented with Nextflow and Snakemake, allows you to easily add new features and test and debug your workflow.
Below are some more key ways that these tools improve our workflows:
- Running multiple steps in sequence, or in parallel when possible
- Skipping steps that have already been completed, allowing users to easily modify later steps and resume the workflow from the last successfully completed step
- Managing resources needed for each step of the workflow
- Keeping a log of successes and failures
- Using containers, allowing all dependencies to be packaged together and increasing reproducibility
- Allowing for the same workflow to be run on multiple platforms - locally, in the cloud, or on a server
Each of these tools have their own unique benefits, and we encourage you to read more on how they manage workflows.
Example: Automating an RNA-Seq Analysis Workflow
We previously described the processes behind setting up a workflow for RNA-sequencing analysis, so we’ll build on that example here. In this scenario, you are starting with a set of FASTQ files for a large amount of RNA-sequencing samples that your PI has asked you to analyze. They have asked you to identify the expression of a set of marker genes that you are studying. You’ve already identified that you have to perform three separate tasks to analyze these files: trim the reads, align and quantify, and plot the expression of your marker genes.
One option is to analyze each of these samples individually, setting up the code each time to take in a different FASTQ file. However, that can be time-consuming and error-prone. Imagine having to make a change and implementing it for every single sample, that would take forever and be likely to cause errors. With automation, we only have to make any future changes one time! Additionally, with a workflow manager, we can process multiple samples in parallel, saving us overall analysis time. This is the perfect situation in which automation can help us, so let’s set up a workflow and use a workflow manager!
- Outline the workflow - The first thing we will want to do is outline our workflow. This entails determining what we want to have as our input to the workflow and what the workflow will return as output. Our workflow will take FASTQ files as input and then output a summarized file with the gene expression of all samples and a plot showing the marker gene expression. Then we decide on the individual steps that we will take to get from the input (FASTQ files) to the output (plots showing marker gene expression). For our specific example, the steps that we will perform are trimming the FASTQ files, aligning the FASTQ files, and then plotting our results. Each of these steps will also have an input and then an output and the outline will help us reveal how we plan to connect the outputs of step 1 (trimming) to the inputs of step 2 (aligning).
- Define the parameters - Now that we know what inputs our workflow will require, we can design a set of parameters. These parameters will set the default settings and input needed to run the workflow. For our workflow, the parameters we will set are the location for the FASTQ files, the metadata information about each sample, and a list of marker genes that we are interested in. To ensure that our workflow is reproducible, we don’t want to hard code this information into the workflow. Instead we want to set each of these inputs as a parameter so we can easily re-use this workflow for a different set of samples in the future.
- Develop individual steps - Next, we are ready to start adding the individual processes to the workflow. Each process will correspond to one step of the workflow, and steps will be executed in the order that you specify, parallelizing when possible. See the schematic below to see how the individual steps of the workflow will work together to produce the desired output.
- Test your workflow - Before implementing our workflow, we want to make sure that we test it on 1-2 of our samples, rather than running it on all of them. During testing we don’t want to run on all of them in case we do run into errors. Running our pipeline on all of our samples before it’s ready could use up valuable resources, like computing time and money. If we encounter any errors during testing, we can make adjustments to the processes that failed and re-run. The benefit of using a workflow manager is the ability to intelligently restart the pipeline after any step that has completed successfully. This means that we can make adjustments to any downstream steps that had errors and re-run our pipeline without having to re-run the entire workflow, making the testing and debugging process smoother.
- Add features - If there are additional steps we want to incorporate, such as additional downstream analyses, we can easily add those in by creating another process in the main workflow.
Now you’re ready to give it a try and automate some of your own workflows! If you’re interested in learning more about the tools we use and the processes we implement at the Data Lab, fill out this form to be notified about future materials and offerings like this.
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?
For many of the repetitive analysis and day-to-day tasks we perform, we implement workflows and automate these workflows with workflow managers. Workflows help us create single streamlined processes that can be run repeatedly for multiple samples and produce the same results. Not only do we save analysis time, but we also save time testing and adding features to our analysis. Changes only have to be made in one place, and that change is applied across all samples!
This approach allows us to spend less time repeating the same task over and over again and more time working on developing new analyses, exploring new datasets, and helping teach researchers like you!
So what are workflows and workflow managers?
A workflow refers to any number of steps or tasks joined together in a particular order to be executed in a repetitive manner and produce a desired outcome. Workflows can be as simple as a two-step process requiring one input to complicated multi-step processes taking multiple inputs and returning multiple outputs at various stages.
Workflow managers take these multi-part workflows and introduce automation of the individual tasks to increase productivity, eliminate manual repetition, and reduce errors. The ultimate goal of using a workflow manager is to translate a set of mundane tasks into a structured, automated set of tasks that can be repeated constantly with efficiency and accuracy to produce the desired output when provided a specific input.
When should you use workflow managers?
Building reproducible workflows run by workflow managers helps us by automating the repetitive, boring tasks, allowing us to clear our minds to focus on the more important tasks. However, knowing when it’s time to utilize automated workflows is a skill in itself. Setting up a workflow is a time intensive process, but when implemented in the right situations, it can save a lot of time in the long run.
What makes a task an ideal candidate for automation?
- Repetitive - If a task is something that is repeated more than three times, there’s a good chance that it would benefit from automation. When you start to copy and paste your code in multiple places, that’s a sign that automation could benefit you! Or if you need to analyze multiple samples through the same multi-step process, automation will save you time and manual effort.
- Error-prone - Tasks that are prone to human error (e.g. typos) are good candidates for workflow managers. Examples include having to manually enter in file paths throughout code or data entry.
Why do we need workflow managers and how do they help us?
Two examples of workflow managers that we use at the Data Lab are Nextflow and Snakemake. Both of these tools allow for multi-step workflow management, and creating workflows that are reproducible and scalable. They both do this by utilizing modular workflows. A modular workflow is one that separates each individual task into its own unit of work with each process having its own input and output inside of the overarching workflow. Using a modular setup, like those implemented with Nextflow and Snakemake, allows you to easily add new features and test and debug your workflow.
Below are some more key ways that these tools improve our workflows:
- Running multiple steps in sequence, or in parallel when possible
- Skipping steps that have already been completed, allowing users to easily modify later steps and resume the workflow from the last successfully completed step
- Managing resources needed for each step of the workflow
- Keeping a log of successes and failures
- Using containers, allowing all dependencies to be packaged together and increasing reproducibility
- Allowing for the same workflow to be run on multiple platforms - locally, in the cloud, or on a server
Each of these tools have their own unique benefits, and we encourage you to read more on how they manage workflows.
Example: Automating an RNA-Seq Analysis Workflow
We previously described the processes behind setting up a workflow for RNA-sequencing analysis, so we’ll build on that example here. In this scenario, you are starting with a set of FASTQ files for a large amount of RNA-sequencing samples that your PI has asked you to analyze. They have asked you to identify the expression of a set of marker genes that you are studying. You’ve already identified that you have to perform three separate tasks to analyze these files: trim the reads, align and quantify, and plot the expression of your marker genes.
One option is to analyze each of these samples individually, setting up the code each time to take in a different FASTQ file. However, that can be time-consuming and error-prone. Imagine having to make a change and implementing it for every single sample, that would take forever and be likely to cause errors. With automation, we only have to make any future changes one time! Additionally, with a workflow manager, we can process multiple samples in parallel, saving us overall analysis time. This is the perfect situation in which automation can help us, so let’s set up a workflow and use a workflow manager!
- Outline the workflow - The first thing we will want to do is outline our workflow. This entails determining what we want to have as our input to the workflow and what the workflow will return as output. Our workflow will take FASTQ files as input and then output a summarized file with the gene expression of all samples and a plot showing the marker gene expression. Then we decide on the individual steps that we will take to get from the input (FASTQ files) to the output (plots showing marker gene expression). For our specific example, the steps that we will perform are trimming the FASTQ files, aligning the FASTQ files, and then plotting our results. Each of these steps will also have an input and then an output and the outline will help us reveal how we plan to connect the outputs of step 1 (trimming) to the inputs of step 2 (aligning).
- Define the parameters - Now that we know what inputs our workflow will require, we can design a set of parameters. These parameters will set the default settings and input needed to run the workflow. For our workflow, the parameters we will set are the location for the FASTQ files, the metadata information about each sample, and a list of marker genes that we are interested in. To ensure that our workflow is reproducible, we don’t want to hard code this information into the workflow. Instead we want to set each of these inputs as a parameter so we can easily re-use this workflow for a different set of samples in the future.
- Develop individual steps - Next, we are ready to start adding the individual processes to the workflow. Each process will correspond to one step of the workflow, and steps will be executed in the order that you specify, parallelizing when possible. See the schematic below to see how the individual steps of the workflow will work together to produce the desired output.
- Test your workflow - Before implementing our workflow, we want to make sure that we test it on 1-2 of our samples, rather than running it on all of them. During testing we don’t want to run on all of them in case we do run into errors. Running our pipeline on all of our samples before it’s ready could use up valuable resources, like computing time and money. If we encounter any errors during testing, we can make adjustments to the processes that failed and re-run. The benefit of using a workflow manager is the ability to intelligently restart the pipeline after any step that has completed successfully. This means that we can make adjustments to any downstream steps that had errors and re-run our pipeline without having to re-run the entire workflow, making the testing and debugging process smoother.
- Add features - If there are additional steps we want to incorporate, such as additional downstream analyses, we can easily add those in by creating another process in the main workflow.
Now you’re ready to give it a try and automate some of your own workflows! If you’re interested in learning more about the tools we use and the processes we implement at the Data Lab, fill out this form to be notified about future materials and offerings like this.
Related Post
In our last blog post, we shared some of the tools and methods we are using in the Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project to ensure that the analysis code remains usable and runnable throughout the project. That post mainly focused on some of the most dynamic phases of the project, when contributors are adding new analysis modules and updating existing ones with more refined results. Here, we will discuss the test data that enables the methods and our approach to running the full set of analyses on real data.
Applications are open for the Data Lab's next training 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 December 10-12, 2024 from 9am-5pm Eastern time in Bala Cynwyd, PA, just outside of Philadelphia.
Earlier this year, we launched the Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project, a collaborative project to openly analyze the data in the Single-cell Pediatric Cancer Atlas Portal on GitHub. We hope this project will bring transparently and expertly assigned cell type labels to the data in the Portal, help the community understand the strengths and limitations of applying existing single-cell methods to pediatric cancer data, and, frankly, allow us to meet more scientists in our community working with single-cell data (maybe you? 😄).
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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.
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.
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.
The Open Single-cell Pediatric Cancer Atlas (OpenScPCA) is an open, collaborative project to analyze data from the Single-cell Pediatric Cancer Atlas (ScPCA) Portal, which currently holds over 500 samples from over 50 pediatric cancer types. OpenScPCA uses an open contribution model designed to allow experts worldwide to contribute and rapidly share the results of analyses in real time. The project was officially launched in April 2024.
In March 2022, we launched the Single-cell Pediatric Cancer Atlas (ScPCA) Portal to make uniformly processed single-cell and single-nuclei RNA-Seq data widely available to the childhood cancer research community. Initially, all data available on the Portal was generated through grants funded by Alex’s Lemonade Stand Foundation (ALSF) as part of the ScPCA project. But enabling access to ALSF-funded data was just the beginning of our vision.Sharing is key to ensuring the Portal’s continued growth. Our sights were set on allowing more pediatric cancer researchers to contribute data to the ScPCA Portal.
The Data Lab has just launched the brand new Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project! This open, collaborative project aims to analyze data from the ScPCA Portal, which currently holds 500 samples from over 50 pediatric cancer types. We are seeking contributors with diverse skills and expertise to join the project!
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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!
I have a confession to make: I am lazy. Ok, maybe that's too strong. Let's go for a euphemism instead: I am efficient. I love learning handy tricks that make my life easier and make my job smoother with fewer hiccups along the way. This is one part of why, here in the Data Lab, we love automation - why waste our time on rote, repetitive, housekeeping tasks when we can get the bots to do it for us? In this blog post, we'll highlight a few tips about how you can use RStudio to code more efficiently.
Writing source code is a significant part of data-intensive biomedical research. Everything from cleaning and pre-processing data to generating publication figures can be accomplished programmatically. Increasingly, funding agencies and journals require researchers to share their code. To pick a few examples, the Data Lab’s parent organization, Alex’s Lemonade Stand Foundation (ALSF), has such a requirement for awardees, and PLoS Computational Biology requires authors to make code underlying results and conclusions available.
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.
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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.
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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.
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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.
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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!
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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.
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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