Queueing Javascript Promises
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.
Let’s start by breaking down some of the terminology we’ll be using.
What is a queue?
A queue is a data structure that allows you to collect individual units of work in a pile and then take from that pile at a later time. A unit of work can be anything! It might be the data you want to operate on or a function that wraps the work you want to do. In this post we’ll be pretending to fetch data from an API as an example.
The order in which you take work from your queue is defined by your application's needs. The most commonly used method is First In First Out (FIFO), which pretty much does what it sounds like. If you enqueue 2 items, the one you added first should be worked on (dequeued) before the second item.
What is a Promise?
“A Promise is an object representing the eventual completion ( or failure ) of an asynchronous operation” - MDN
We will use Promises to call the next item from our queue. A Promise is essentially a placeholder that says, "I don't have the result of this yet, but when it completes, I will put the result here." Promises are one of the mechanisms that allow for asynchronous code in JavaScript. You use them when you want to do some work but don’t want to be blocked by that work.
When you create a Promise you pass one function that takes two callbacks as arguments. The first callback is used when the Promise is successful or “resolved.” The second callback is used when the Promise encounters an error or is “rejected.” Promises are always in 1 of 3 possible states; pending, fulfilled, or rejected.
You can also chain Promises by using the “then” and “catch” functions. The “then” function is called when the promise is fulfilled and the “catch” function is called when the promise is rejected.
However, the “then” callback will always execute as long as the Promise is not handled in the rejected state. We will demonstrate this caveat in the example code below.
How do we put this together?
We start by creating a function that generates a queue to manage our API requests. It will take two arguments that we will chain to each of the Promises in our queue’s stack. The first argument will be a function that is called when the unit of work is successfully completed. The second will be called when it fails.
Next we will store the items for our queue in an array, allowing us to order the units of work. The [.inline-snippet]items[.inline-snippet] will be placed at the end / top and we will take them from the front / bottom.
Let’s skip down to the returned function aptly named [.inline-snippet]enqueue[.inline-snippet], which is the method we will use to stack items to the top / end of our queue. This function takes one argument, a function that when invoked returns a Promise, and if we only have one item in our stack we will dequeue and invoke that function right away.
Now we will look at the dequeue function. We will start by invoking the function at the bottom / front of our queue. This will return a Promise that we can chain our onResolve and onReject callbacks that we passed when we created the queue. After that we will remove the current function that we just executed from our stack since that work is now complete. Then we simply dequeue the next item since our queue has handled the previous request already.
How do we use it?
We will create a couple of mock API endpoints that will pretend to reach out to an API and get a response. Then we will create our queue and enqueue 3 mock API calls. After that we will wrap some more calls in a setTimeout to simulate a 10 second delay and then enqueue 3 more API calls.
Mocking API Requests
For the purposes of demonstration we will create a couple of mock API methods. Imagine using a normal AJAX / AJAJ call, or any other asynchronous function. The two functions above simulate going over the wire to an API. This is a particularly slow API that takes anywhere between 1 and 5 seconds to respond. This illustrates that it doesn’t matter how long one of the units of work takes to complete, as the next unit of work won’t start until the previous work has been completed.
Example Implementation
Now we have everything we need to test a use case for our queue. Here is an example script that simulates 3 API calls in quick succession. 10 seconds later another 3 API calls follow in quick succession.
Example Output
Despite each function taking anywhere between 1 and 5 seconds to execute and resolve or reject, the subsequent function is not called until the previous function's resolution. If we did not enqueue these functions the output would be in a random order.
Conclusion
This example demonstrates one way to create a queue system in JavaScript. There are other ways to approach this problem, but our example illustrates how you would create a data structure to store work that you want to complete in sequence.
We have only just scratched the surface of what can be done with Promises or how to orchestrate several asynchronous operations with interdependency at the same time. Using Promises by MDN is a helpful resource if you’re interested in discovering what else is possible!
On this blog, we share our expertise with the scientific community. You can expect to read technical content about our processes, information about our products and services, and much more. Subscribe here to receive updates!
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.
Let’s start by breaking down some of the terminology we’ll be using.
What is a queue?
A queue is a data structure that allows you to collect individual units of work in a pile and then take from that pile at a later time. A unit of work can be anything! It might be the data you want to operate on or a function that wraps the work you want to do. In this post we’ll be pretending to fetch data from an API as an example.
The order in which you take work from your queue is defined by your application's needs. The most commonly used method is First In First Out (FIFO), which pretty much does what it sounds like. If you enqueue 2 items, the one you added first should be worked on (dequeued) before the second item.
What is a Promise?
“A Promise is an object representing the eventual completion ( or failure ) of an asynchronous operation” - MDN
We will use Promises to call the next item from our queue. A Promise is essentially a placeholder that says, "I don't have the result of this yet, but when it completes, I will put the result here." Promises are one of the mechanisms that allow for asynchronous code in JavaScript. You use them when you want to do some work but don’t want to be blocked by that work.
When you create a Promise you pass one function that takes two callbacks as arguments. The first callback is used when the Promise is successful or “resolved.” The second callback is used when the Promise encounters an error or is “rejected.” Promises are always in 1 of 3 possible states; pending, fulfilled, or rejected.
You can also chain Promises by using the “then” and “catch” functions. The “then” function is called when the promise is fulfilled and the “catch” function is called when the promise is rejected.
However, the “then” callback will always execute as long as the Promise is not handled in the rejected state. We will demonstrate this caveat in the example code below.
How do we put this together?
We start by creating a function that generates a queue to manage our API requests. It will take two arguments that we will chain to each of the Promises in our queue’s stack. The first argument will be a function that is called when the unit of work is successfully completed. The second will be called when it fails.
Next we will store the items for our queue in an array, allowing us to order the units of work. The [.inline-snippet]items[.inline-snippet] will be placed at the end / top and we will take them from the front / bottom.
Let’s skip down to the returned function aptly named [.inline-snippet]enqueue[.inline-snippet], which is the method we will use to stack items to the top / end of our queue. This function takes one argument, a function that when invoked returns a Promise, and if we only have one item in our stack we will dequeue and invoke that function right away.
Now we will look at the dequeue function. We will start by invoking the function at the bottom / front of our queue. This will return a Promise that we can chain our onResolve and onReject callbacks that we passed when we created the queue. After that we will remove the current function that we just executed from our stack since that work is now complete. Then we simply dequeue the next item since our queue has handled the previous request already.
How do we use it?
We will create a couple of mock API endpoints that will pretend to reach out to an API and get a response. Then we will create our queue and enqueue 3 mock API calls. After that we will wrap some more calls in a setTimeout to simulate a 10 second delay and then enqueue 3 more API calls.
Mocking API Requests
For the purposes of demonstration we will create a couple of mock API methods. Imagine using a normal AJAX / AJAJ call, or any other asynchronous function. The two functions above simulate going over the wire to an API. This is a particularly slow API that takes anywhere between 1 and 5 seconds to respond. This illustrates that it doesn’t matter how long one of the units of work takes to complete, as the next unit of work won’t start until the previous work has been completed.
Example Implementation
Now we have everything we need to test a use case for our queue. Here is an example script that simulates 3 API calls in quick succession. 10 seconds later another 3 API calls follow in quick succession.
Example Output
Despite each function taking anywhere between 1 and 5 seconds to execute and resolve or reject, the subsequent function is not called until the previous function's resolution. If we did not enqueue these functions the output would be in a random order.
Conclusion
This example demonstrates one way to create a queue system in JavaScript. There are other ways to approach this problem, but our example illustrates how you would create a data structure to store work that you want to complete in sequence.
We have only just scratched the surface of what can be done with Promises or how to orchestrate several asynchronous operations with interdependency at the same time. Using Promises by MDN is a helpful resource if you’re interested in discovering what else is possible!
On this blog, we share our expertise with the scientific community. You can expect to read technical content about our processes, information about our products and services, and much more. Subscribe here to receive updates!
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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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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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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!
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.
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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.
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Writing effective documentation is challenging. Users might not always read every word in the documentation. They might even just scroll past large chunks of text, but we can accommodate those behaviors by structuring and formatting content appropriately.
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!
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!
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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.
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 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!
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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.
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.
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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.
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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