OpenPBTA: Someone is wrong on the internet and it’s probably us (updated 9-9-2020)
Also authored by Chante Bethell, Candace Savonen, and Josh Shapiro
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).
Since September of last year, we have been organizing the Open Pediatric Brain Tumor Atlas (OpenPBTA) project with the team at Center for Data-Driven Discovery in Biomedicine at Children’s Hospital of Philadelphia. The ideas behind the OpenPBTA are straightforward – analyze one of the largest collections of somatic pediatric brain tumor data in the open, on GitHub, and crowdsource expertise from all over the world to better understand one of the leading causes of death from disease for children and young adults. Beyond better molecular characterization of pediatric brain tumors, there are added benefits to how we’ve structured (and licensed!) the OpenPBTA project. The robust, reusable workflows that we build can continue to be applied as the PBTA data grows to include additional samples and reused to study other pediatric cancers.
The implementation of the project has been decidedly less straightforward. To our knowledge, this is the first time something quite like this has been tried. We focus a lot on the tools that underlie reproducible data science in our day-to-day. We’ve built infrastructure that lets us automatically test new contributions, including our own. This has revealed opportunities to improve the underlying data, compare concordance of software, and incorrect simplifying assumptions.
We need your help
So why this blog post now? Well, quite simply, we need the help of other scientists to keep moving the project forward. We at the CCDL are looking to wind down our role in actively analyzing the OpenPBTA data so we can focus on our virtual training workshops and the Alex’s Lemonade Stand Foundation Single Cell Pediatric Cancer Atlas project.
We’ve been preparing for this transition since we began our involvement in the process. We’ve focused on getting the manuscript’s methods section updated, certain figures included, and other portions outlined.
But before we go (and by go, we mean stay but play a different role that is more community-focused), we want to share some of the things that keep us up at night. You see, someone is wrong on the internet and we think it’s probably us.
We hope you find our figures aesthetically pleasing, but we think if you spend some time with them you’ll probably agree that we’re wrong. That’s okay, though–to be wrong is to be alive and in the business of science. We want to work together to be less wrong! Only then will we reach the full potential and promise of the OpenPBTA project.
Below, we’ll talk about 3 areas where we think the project could use some input from you, dear reader. We will highlight what we’ve done, what we're concerned about, and some thoughts about moving forward. We’ll also link to relevant GitHub issues, pull requests, and analysis modules so you can take a closer look.
Comparing tumor mutation burden across datasets: WGS vs. WXS
Previous studies comparing pediatric and adult tumor datasets report lower tumor mutational burden (TMB) in pediatric cancers (Vogelstein et al. 2013., Lawrence et al. 2013., and Grobner et al. 2018.). A challenge in comparing TMB results between publications, and in some cases datasets, is the lack of consensus about how to calculate TMB (TMB Harmonization Project and Merino et al. 2020.).
In this project, we’ve defined TMB as
(total # coding sequence SNVs called both Strelka2 and Mutect2 ) / size of the effectively surveyed coding region of the genome
We considered base pairs to be effectively surveyed if they were in the intersection of the genomic ranges considered by the SNV callers and coding sequences of the genome.
To minimize potential sources of artificial TMB variance, we started from the sequence level and used the same SNV caller algorithms, variant allele frequency cutoff minimums, consensus SNV decisions, and coding regions considered for both adult and pediatric datasets. See the code for these processing steps here: https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/snv-callers
TMB before, when we were wrong on the internet (5-2-2020)
Our TMB comparisons of pediatric and adult run counter to previous literature:
We suspect this result may be a limitation of sequencing strategy; all adult cancer data used here are whole exome sequenced (WXS), while nearly all the pediatric cancers were whole genome sequenced (WGS). TMB comparison between pediatric and adults is highly influenced by the SNV callers used and some SNV callers like Lancet perform differently on paired WXS and WGS data.
TMB after we fixed what was wrong on the internet in an extremely narrow sense (9-9-20)
A bug in our filtering step was found and addressed! This bug left in some mutations that were outside of the ranges where mutations are able to be called (akin to situation f on the first figure). These regions we filter by (but weren’t doing so correctly) are mainly coding regions. This disproportionally increased WGS samples’ number of mutations since there are, by design, more noncoding regions sequenced in WGS than WXS samples. This made our PBTA pediatric samples (which are almost all WGS samples) falsely seem like they had higher TMB than TCGA adult samples (which are mostly WXS samples).
We have also explored updating our analysis to include only nonsynonymous mutations, but this has little effect on the TMB results, especially not comparisons among samples.
Structural variants, ploidy, and the challenges of consistency
Copy number alterations and other structural variants are often highly consequential mutations in cancers, but they are notoriously difficult to identify correctly from short read data, and even more so in heterogeneous cell populations like tumors. We started with somatic copy number variant (CNV) and somatic structural variant (SV) calls from Control-FREEC, CNVkit, and Manta. Each of these methods has false positives and false negatives, so we combined their powers by identifying consensus copy number variant segments that were present in at least two callers’ results. Because we expect the precise copy number to have the greatest variation among callers, we only require the direction of a call segment (loss or gain) to match. We also included a number of other filters in the creation of this consensus set, including requiring copy number variant sets to be greater than a certain length, and excluding regions that are known to be problematic for these kinds of analyses, including centromeres, telomeres, and segmentally duplicated regions.
This set of consensus CNV segments was used in downstream analyses, but it seems quite likely that this is an area ripe for improvement. For example, our initial consensus calls largely ignored the inferred ploidy of each tumor due to variation in how ploidy is handled by the underlying callers. We would love to update these calls to more accurately reflect copy number variation in non-diploid tumors, and to better reflect quantitative variation in copy number to capture the difference between single duplication events and more extensive amplification.
As a first pass at this, we compared the copy number of each segment as defined by CNVkit to the ploidy inferred by Control-FREEC to assign losses, gains, and amplifications (where copy number was more than twice the inferred ploidy).
We also wanted to define “focal” copy number alterations to avoid calling strings of adjacent genes as separate lesions where it would be more appropriate to call a broader region. We tried using GISTIC 2.0 (Mermel et al. 2011.) to differentiate between broad and focal copy number alterations but it proved challenging in this dataset (more on that below). Instead, we took a more manual approach, starting with calling alterations that affected the vast majority of a chromosome arm, then alterations not captured by arm calls that affected the vast majority of a cytoband. Finally, alterations of genes that were not captured in arm or cytoband calls were included individually. We think this approach is reasonable as a start, but is there a better way to classify status across segments and select focal lesions? Are our cutoffs well-chosen?
What does it mean for alterations or lesions to be “interesting” in a dataset with multiple histologies, some of which have small sample sizes?
The OpenPBTA dataset is an excellent resource with over 30 different disease types represented. A mix of disease types can pose an analytical challenge, especially considering some groups are composed of very few samples; others have hundreds of samples.
Many approaches to identifying “significant” lesions rely in part on the notion of recurrence–if a particular alteration is observed in many samples, it is more likely to be of interest and perhaps to confer some kind of selective advantage for tumor cells (e.g., be a driver). We’ve struggled with selecting appropriate methodologies and, more generally, what it means for an alteration to be interesting in this many-disease context.
For example, we started by running GISTIC 2.0 on the entire cohort to identify somatic copy number alterations of interest (Mermel et al. 2011.). When we compare the GISTIC results from an individual histology to the results from the entire cohort, we see considerable differences. This is not unexpected–the largest individual histology (low-grade glioma) only makes up about a quarter of the samples and it stands to reason that not all copy number alterations will be shared (Extended Data Figure 8 from Grobner et al. 2018.). For histologies with small sample sizes, we ran into errors running GISTIC that we haven’t resolved yet. So now, we count how many times a somatic copy number alteration (gene-level, cytoband-level, or chromosome arm-level) occurs and apply a cutoff that ignores the majority of genes, instead.
We’d love to include methods that identify drivers and account for things like the background rate of somatic alterations. How do you approach these problems in a multi-disease context when sometimes n is small?
OF COURSE, THESE ARE NOT THE ONLY WAYS WE MIGHT BE WRONG, BUT THEY ARE A FEW THAT COME TO MIND!
Contribute to the OpenPBTA project
Did you spot a way that we were wrong on the internet? Are you ready to contribute to the OpenPBTA project? We’d love to have you!
You can find all the analyses on GitHub at https://github.com/AlexsLemonade/OpenPBTA-analysis and a draft of the manuscript at https://github.com/AlexsLemonade/OpenPBTA-manuscript. GitHub is the main way to discuss and contribute analyses to the project; other technologies like Docker and CircleCI for continuous integration round out the project infrastructure (see an excerpt from the OpenPBTA presentation at Rocky 2019 below).
In the analysis repository README, we ask folks who are new to the project to read documentation about the data, look at the analyses that have been performed so far, and check out our instructions for contributing.
We realize this can be a lot of information for first-time contributors and for folks who do not regularly use all these features of GitHub or technologies. We are here to help! Feel free to start small: File a new issue suggesting ways to improve an analysis, or comment on existing issues where you’d like to contribute! (Here’s a good example.) If you have general questions, we encourage you to sign up for the Cancer Data Science Slack, join the #open-pbta channel, and introduce yourself! Organizers will direct you to different parts of the project or tag other contributors as needed. That’s what we’re here for!
In the future, we plan to blog about the CCDL’s internal processes for analytical code review and some of the lessons we’ve learned from the OpenPBTA project so far.
Bye for now 👋 and we hope to see you on GitHub soon!
Also authored by Chante Bethell, Candace Savonen, and Josh Shapiro
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).
Since September of last year, we have been organizing the Open Pediatric Brain Tumor Atlas (OpenPBTA) project with the team at Center for Data-Driven Discovery in Biomedicine at Children’s Hospital of Philadelphia. The ideas behind the OpenPBTA are straightforward – analyze one of the largest collections of somatic pediatric brain tumor data in the open, on GitHub, and crowdsource expertise from all over the world to better understand one of the leading causes of death from disease for children and young adults. Beyond better molecular characterization of pediatric brain tumors, there are added benefits to how we’ve structured (and licensed!) the OpenPBTA project. The robust, reusable workflows that we build can continue to be applied as the PBTA data grows to include additional samples and reused to study other pediatric cancers.
The implementation of the project has been decidedly less straightforward. To our knowledge, this is the first time something quite like this has been tried. We focus a lot on the tools that underlie reproducible data science in our day-to-day. We’ve built infrastructure that lets us automatically test new contributions, including our own. This has revealed opportunities to improve the underlying data, compare concordance of software, and incorrect simplifying assumptions.
We need your help
So why this blog post now? Well, quite simply, we need the help of other scientists to keep moving the project forward. We at the CCDL are looking to wind down our role in actively analyzing the OpenPBTA data so we can focus on our virtual training workshops and the Alex’s Lemonade Stand Foundation Single Cell Pediatric Cancer Atlas project.
We’ve been preparing for this transition since we began our involvement in the process. We’ve focused on getting the manuscript’s methods section updated, certain figures included, and other portions outlined.
But before we go (and by go, we mean stay but play a different role that is more community-focused), we want to share some of the things that keep us up at night. You see, someone is wrong on the internet and we think it’s probably us.
We hope you find our figures aesthetically pleasing, but we think if you spend some time with them you’ll probably agree that we’re wrong. That’s okay, though–to be wrong is to be alive and in the business of science. We want to work together to be less wrong! Only then will we reach the full potential and promise of the OpenPBTA project.
Below, we’ll talk about 3 areas where we think the project could use some input from you, dear reader. We will highlight what we’ve done, what we're concerned about, and some thoughts about moving forward. We’ll also link to relevant GitHub issues, pull requests, and analysis modules so you can take a closer look.
Comparing tumor mutation burden across datasets: WGS vs. WXS
Previous studies comparing pediatric and adult tumor datasets report lower tumor mutational burden (TMB) in pediatric cancers (Vogelstein et al. 2013., Lawrence et al. 2013., and Grobner et al. 2018.). A challenge in comparing TMB results between publications, and in some cases datasets, is the lack of consensus about how to calculate TMB (TMB Harmonization Project and Merino et al. 2020.).
In this project, we’ve defined TMB as
(total # coding sequence SNVs called both Strelka2 and Mutect2 ) / size of the effectively surveyed coding region of the genome
We considered base pairs to be effectively surveyed if they were in the intersection of the genomic ranges considered by the SNV callers and coding sequences of the genome.
To minimize potential sources of artificial TMB variance, we started from the sequence level and used the same SNV caller algorithms, variant allele frequency cutoff minimums, consensus SNV decisions, and coding regions considered for both adult and pediatric datasets. See the code for these processing steps here: https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/snv-callers
TMB before, when we were wrong on the internet (5-2-2020)
Our TMB comparisons of pediatric and adult run counter to previous literature:
We suspect this result may be a limitation of sequencing strategy; all adult cancer data used here are whole exome sequenced (WXS), while nearly all the pediatric cancers were whole genome sequenced (WGS). TMB comparison between pediatric and adults is highly influenced by the SNV callers used and some SNV callers like Lancet perform differently on paired WXS and WGS data.
TMB after we fixed what was wrong on the internet in an extremely narrow sense (9-9-20)
A bug in our filtering step was found and addressed! This bug left in some mutations that were outside of the ranges where mutations are able to be called (akin to situation f on the first figure). These regions we filter by (but weren’t doing so correctly) are mainly coding regions. This disproportionally increased WGS samples’ number of mutations since there are, by design, more noncoding regions sequenced in WGS than WXS samples. This made our PBTA pediatric samples (which are almost all WGS samples) falsely seem like they had higher TMB than TCGA adult samples (which are mostly WXS samples).
We have also explored updating our analysis to include only nonsynonymous mutations, but this has little effect on the TMB results, especially not comparisons among samples.
Structural variants, ploidy, and the challenges of consistency
Copy number alterations and other structural variants are often highly consequential mutations in cancers, but they are notoriously difficult to identify correctly from short read data, and even more so in heterogeneous cell populations like tumors. We started with somatic copy number variant (CNV) and somatic structural variant (SV) calls from Control-FREEC, CNVkit, and Manta. Each of these methods has false positives and false negatives, so we combined their powers by identifying consensus copy number variant segments that were present in at least two callers’ results. Because we expect the precise copy number to have the greatest variation among callers, we only require the direction of a call segment (loss or gain) to match. We also included a number of other filters in the creation of this consensus set, including requiring copy number variant sets to be greater than a certain length, and excluding regions that are known to be problematic for these kinds of analyses, including centromeres, telomeres, and segmentally duplicated regions.
This set of consensus CNV segments was used in downstream analyses, but it seems quite likely that this is an area ripe for improvement. For example, our initial consensus calls largely ignored the inferred ploidy of each tumor due to variation in how ploidy is handled by the underlying callers. We would love to update these calls to more accurately reflect copy number variation in non-diploid tumors, and to better reflect quantitative variation in copy number to capture the difference between single duplication events and more extensive amplification.
As a first pass at this, we compared the copy number of each segment as defined by CNVkit to the ploidy inferred by Control-FREEC to assign losses, gains, and amplifications (where copy number was more than twice the inferred ploidy).
We also wanted to define “focal” copy number alterations to avoid calling strings of adjacent genes as separate lesions where it would be more appropriate to call a broader region. We tried using GISTIC 2.0 (Mermel et al. 2011.) to differentiate between broad and focal copy number alterations but it proved challenging in this dataset (more on that below). Instead, we took a more manual approach, starting with calling alterations that affected the vast majority of a chromosome arm, then alterations not captured by arm calls that affected the vast majority of a cytoband. Finally, alterations of genes that were not captured in arm or cytoband calls were included individually. We think this approach is reasonable as a start, but is there a better way to classify status across segments and select focal lesions? Are our cutoffs well-chosen?
What does it mean for alterations or lesions to be “interesting” in a dataset with multiple histologies, some of which have small sample sizes?
The OpenPBTA dataset is an excellent resource with over 30 different disease types represented. A mix of disease types can pose an analytical challenge, especially considering some groups are composed of very few samples; others have hundreds of samples.
Many approaches to identifying “significant” lesions rely in part on the notion of recurrence–if a particular alteration is observed in many samples, it is more likely to be of interest and perhaps to confer some kind of selective advantage for tumor cells (e.g., be a driver). We’ve struggled with selecting appropriate methodologies and, more generally, what it means for an alteration to be interesting in this many-disease context.
For example, we started by running GISTIC 2.0 on the entire cohort to identify somatic copy number alterations of interest (Mermel et al. 2011.). When we compare the GISTIC results from an individual histology to the results from the entire cohort, we see considerable differences. This is not unexpected–the largest individual histology (low-grade glioma) only makes up about a quarter of the samples and it stands to reason that not all copy number alterations will be shared (Extended Data Figure 8 from Grobner et al. 2018.). For histologies with small sample sizes, we ran into errors running GISTIC that we haven’t resolved yet. So now, we count how many times a somatic copy number alteration (gene-level, cytoband-level, or chromosome arm-level) occurs and apply a cutoff that ignores the majority of genes, instead.
We’d love to include methods that identify drivers and account for things like the background rate of somatic alterations. How do you approach these problems in a multi-disease context when sometimes n is small?
OF COURSE, THESE ARE NOT THE ONLY WAYS WE MIGHT BE WRONG, BUT THEY ARE A FEW THAT COME TO MIND!
Contribute to the OpenPBTA project
Did you spot a way that we were wrong on the internet? Are you ready to contribute to the OpenPBTA project? We’d love to have you!
You can find all the analyses on GitHub at https://github.com/AlexsLemonade/OpenPBTA-analysis and a draft of the manuscript at https://github.com/AlexsLemonade/OpenPBTA-manuscript. GitHub is the main way to discuss and contribute analyses to the project; other technologies like Docker and CircleCI for continuous integration round out the project infrastructure (see an excerpt from the OpenPBTA presentation at Rocky 2019 below).
In the analysis repository README, we ask folks who are new to the project to read documentation about the data, look at the analyses that have been performed so far, and check out our instructions for contributing.
We realize this can be a lot of information for first-time contributors and for folks who do not regularly use all these features of GitHub or technologies. We are here to help! Feel free to start small: File a new issue suggesting ways to improve an analysis, or comment on existing issues where you’d like to contribute! (Here’s a good example.) If you have general questions, we encourage you to sign up for the Cancer Data Science Slack, join the #open-pbta channel, and introduce yourself! Organizers will direct you to different parts of the project or tag other contributors as needed. That’s what we’re here for!
In the future, we plan to blog about the CCDL’s internal processes for analytical code review and some of the lessons we’ve learned from the OpenPBTA project so far.
Bye for now 👋 and we hope to see you on GitHub soon!
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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!
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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