Pinning transitive R dependencies for fun and reproducible builds
Transitive dependencies (n)
- Packages that your dependencies depend on, and packages those packages depend on and so on and so on and so on and so on and so on
- The packages which are most likely to break your builds and are the most difficult to manage
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. Many package managers (e.g. Cargo, NPM) and helper tools (e.g. pip-compile, Bundler) now generate a “lockfile” to guarantee reproducibility in installing transitive dependencies. The first time you install all of your dependencies, the tool runs its dependency resolution algorithm to determine which packages to install at what versions, taking into account the version requirements all packages have of other packages. The output of the dependency resolution is then stored in a lockfile (Gemfile.lock, cargo.lock, package-lock.json, etc.), which is stored alongside the file specifying dependencies. With subsequent installations, the same exact version of all dependencies are installed.
Our problem
While most of our business logic is written in Python, with its dependencies managed by “pip-compile”, we rely on some external R libraries for processing biological data. In the name of stability, we pinned our R dependencies using devtools to specify a version during installation. R’s default package installation scheme, however, doesn’t deal well with pinned versions of packages. Because there is no equivalent of a lockfile, devtools will install the specified pinned versions of packages, but the most recent versions of all other packages. This behaviour subverts the stability and reproducibility of pinning package versions, since transitive dependencies can update and break our builds. This is a recent example of an R build failing for exactly that reason:
After tons of digging, I tracked down the source of our build failure. It turns out, the latest vctrs update required rlang version 0.4.0 or greater, which caused R to skip installing vctrs since we pin rlang at 0.3.1. vctrs is 3 layers down the dependency graph from a package we explicitly install, but its update still broke our tests and prevented us from deploying. Eventually, I decided that we had to do something to prevent these errors moving forward.
Our solution
In thinking about the problem, we recognized that there were two distinct steps that a dependency-locking tool needs to accomplish: resolving version requirements and calculating an installation order.
Resolving version requirements
As I alluded to earlier, the approach that most package managers and tools take to resolve version requirements is to apply a dependency resolution algorithm. Dependency resolution algorithms are able to parse version requirements between packages and resolve which version of each package to install. Given how complicated the description is, dependency resolution algorithms can be complex. For that reason, we decided on a simpler approach: using package versions from an already-built docker container. Essentially, we are manually resolving dependencies, and then storing the output of that resolution for future use.
To get all of the installed R package versions on a system, you can use this R one-liner:
print(as.data.frame(installed.packages()[, c(1, 3:4)])[1:2])
It looks fairly complicated, but all that it does is to convert specific parts of “installed.packages” (a structure holding information about all installed R packages) to a data frame, then it does more filtering on the data frame and prints it out. What is nice about this is that the output format is easily machine readable, so we can convert it into a TSV file for use by other scripts. This is a simplified version of the script we use to run the R script on a specified docker container and convert the output to TSV:
docker run -it "$docker_image" Rscript -e "$(cat ./list_dependencies.R)" \
| tr -d '\r'
| tail -n+2
| awk '{ print $1 "\t" $3 }'
We run the above R script inside $docker_image, then use “tr” to remove the windows line endings and “tail” to remove the column headers, then use “awk” to print out two of the columns with a tab in between. Although this requires a bit more manual input than a dependency resolution algorithm, it was much simpler to implement and was perfectly sufficient for our problem.
Resolving installation order
The other main problem that we had to address is installation order. Since our solution uses “devtools::install_version” to install packages at specific versions, we had to pay attention to the order in which we installed packages. Even if we install every single transient dependency manually, we can still run into issues if we install a package before its dependencies. While installing a package, R checks to see if its dependencies are installed, and if they aren’t already installed it automatically installs the latest version of that dependency. Which is a problem, because R automatically picking the latest version of dependencies is the exact problem we are trying to avoid in the first place.
To better understand the problem and our solution, it is helpful to think of this problem in terms of a dependency graph:
A dependency graph is a way of representing a package’s transitive dependencies based on which packages depend on each other. In the very simple example above, “callr” depends on “processx” and “R6”, “R6” doesn’t depend on anything, “processx” depends on “R6” and “ps”, and “ps” doesn’t depend on anything.
Now that we understand what a dependency graph is, it is easier to see what we mean by installing packages in the proper order. To install packages in the correct order, we look at the dependency graph and install packages “from the bottom up”. That is, we only want to install a package once all packages pointed to by that package have already been installed. This type of ordering is called a “topological ordering” in graph theory.
So that we can perform a topological sort on the graph to get a topological ordering, we have to ensure that the graph has two general properties. The first property is that the graph is “directed”, meaning that connections between nodes encode a direction, and are usually depicted as arrows. For dependency graphs, the arrows’ directions encode which package on each edge depends on the other. The second is that it is acyclic, meaning that there are no cycles in the dependency graph, since packages would fail to install if cycles existed because each package in the cycle would be waiting on another package in the cycle before it could install. Since both of these conditions hold in dependency graphs, we can perform a topological sort on the graphs to easily determine an installation order.
Once we can transform a dependency graph into an installation order, the rest of the code is easy. The general outline of what our code for installation order resolution does is the following:
- Scrape CRAN’s website for each package to find out what it “depends” on and what it “imports” (since there are two ways of specifying what we think of as a “dependency” in R).
- Use the information from CRAN to build up a dependency tree. This is different from a dependency graph, because each node only has one arrow pointing into it and packages are duplicated if multiple packages depend on them.
- Convert the dependency tree into a dependency graph.
- Apply a topological sort to the graph to get the installation order, using depth first search.
From there, we combine the installation order information with the version information to generate an R script like the following:
# Generated from cranlock
options(warn=2)
options(Ncpus=parallel::detectCores())
options(repos=structure(c(CRAN="https://cran.revolutionanalytics.com")))
devtools::install_version('codetools', version='0.2-15')
devtools::install_version('iterators', version='1.0.10')
devtools::install_version('foreach', version='1.4.4')
devtools::install_version('doParallel', version='1.0.11')
devtools::install_version('data.table', version='1.11.0')
devtools::install_version('getopt', version='1.20.3')
devtools::install_version('optparse', version='1.4.4')
devtools::install_version('lazyeval', version='0.2.1')
Conclusion
As long as you do it properly, pinning dependencies can save you a ton of time by drastically reducing surprise build failures.
Although we couldn’t find any, there could still be a more fully fleshed-out method of pinning R transitive dependencies out there. If there is, we would love to hear about it.
If you are interested in reading the full code, it is hosted on Github here: https://github.com/AlexsLemonade/cranlock
Transitive dependencies (n)
- Packages that your dependencies depend on, and packages those packages depend on and so on and so on and so on and so on and so on
- The packages which are most likely to break your builds and are the most difficult to manage
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. Many package managers (e.g. Cargo, NPM) and helper tools (e.g. pip-compile, Bundler) now generate a “lockfile” to guarantee reproducibility in installing transitive dependencies. The first time you install all of your dependencies, the tool runs its dependency resolution algorithm to determine which packages to install at what versions, taking into account the version requirements all packages have of other packages. The output of the dependency resolution is then stored in a lockfile (Gemfile.lock, cargo.lock, package-lock.json, etc.), which is stored alongside the file specifying dependencies. With subsequent installations, the same exact version of all dependencies are installed.
Our problem
While most of our business logic is written in Python, with its dependencies managed by “pip-compile”, we rely on some external R libraries for processing biological data. In the name of stability, we pinned our R dependencies using devtools to specify a version during installation. R’s default package installation scheme, however, doesn’t deal well with pinned versions of packages. Because there is no equivalent of a lockfile, devtools will install the specified pinned versions of packages, but the most recent versions of all other packages. This behaviour subverts the stability and reproducibility of pinning package versions, since transitive dependencies can update and break our builds. This is a recent example of an R build failing for exactly that reason:
After tons of digging, I tracked down the source of our build failure. It turns out, the latest vctrs update required rlang version 0.4.0 or greater, which caused R to skip installing vctrs since we pin rlang at 0.3.1. vctrs is 3 layers down the dependency graph from a package we explicitly install, but its update still broke our tests and prevented us from deploying. Eventually, I decided that we had to do something to prevent these errors moving forward.
Our solution
In thinking about the problem, we recognized that there were two distinct steps that a dependency-locking tool needs to accomplish: resolving version requirements and calculating an installation order.
Resolving version requirements
As I alluded to earlier, the approach that most package managers and tools take to resolve version requirements is to apply a dependency resolution algorithm. Dependency resolution algorithms are able to parse version requirements between packages and resolve which version of each package to install. Given how complicated the description is, dependency resolution algorithms can be complex. For that reason, we decided on a simpler approach: using package versions from an already-built docker container. Essentially, we are manually resolving dependencies, and then storing the output of that resolution for future use.
To get all of the installed R package versions on a system, you can use this R one-liner:
print(as.data.frame(installed.packages()[, c(1, 3:4)])[1:2])
It looks fairly complicated, but all that it does is to convert specific parts of “installed.packages” (a structure holding information about all installed R packages) to a data frame, then it does more filtering on the data frame and prints it out. What is nice about this is that the output format is easily machine readable, so we can convert it into a TSV file for use by other scripts. This is a simplified version of the script we use to run the R script on a specified docker container and convert the output to TSV:
docker run -it "$docker_image" Rscript -e "$(cat ./list_dependencies.R)" \
| tr -d '\r'
| tail -n+2
| awk '{ print $1 "\t" $3 }'
We run the above R script inside $docker_image, then use “tr” to remove the windows line endings and “tail” to remove the column headers, then use “awk” to print out two of the columns with a tab in between. Although this requires a bit more manual input than a dependency resolution algorithm, it was much simpler to implement and was perfectly sufficient for our problem.
Resolving installation order
The other main problem that we had to address is installation order. Since our solution uses “devtools::install_version” to install packages at specific versions, we had to pay attention to the order in which we installed packages. Even if we install every single transient dependency manually, we can still run into issues if we install a package before its dependencies. While installing a package, R checks to see if its dependencies are installed, and if they aren’t already installed it automatically installs the latest version of that dependency. Which is a problem, because R automatically picking the latest version of dependencies is the exact problem we are trying to avoid in the first place.
To better understand the problem and our solution, it is helpful to think of this problem in terms of a dependency graph:
A dependency graph is a way of representing a package’s transitive dependencies based on which packages depend on each other. In the very simple example above, “callr” depends on “processx” and “R6”, “R6” doesn’t depend on anything, “processx” depends on “R6” and “ps”, and “ps” doesn’t depend on anything.
Now that we understand what a dependency graph is, it is easier to see what we mean by installing packages in the proper order. To install packages in the correct order, we look at the dependency graph and install packages “from the bottom up”. That is, we only want to install a package once all packages pointed to by that package have already been installed. This type of ordering is called a “topological ordering” in graph theory.
So that we can perform a topological sort on the graph to get a topological ordering, we have to ensure that the graph has two general properties. The first property is that the graph is “directed”, meaning that connections between nodes encode a direction, and are usually depicted as arrows. For dependency graphs, the arrows’ directions encode which package on each edge depends on the other. The second is that it is acyclic, meaning that there are no cycles in the dependency graph, since packages would fail to install if cycles existed because each package in the cycle would be waiting on another package in the cycle before it could install. Since both of these conditions hold in dependency graphs, we can perform a topological sort on the graphs to easily determine an installation order.
Once we can transform a dependency graph into an installation order, the rest of the code is easy. The general outline of what our code for installation order resolution does is the following:
- Scrape CRAN’s website for each package to find out what it “depends” on and what it “imports” (since there are two ways of specifying what we think of as a “dependency” in R).
- Use the information from CRAN to build up a dependency tree. This is different from a dependency graph, because each node only has one arrow pointing into it and packages are duplicated if multiple packages depend on them.
- Convert the dependency tree into a dependency graph.
- Apply a topological sort to the graph to get the installation order, using depth first search.
From there, we combine the installation order information with the version information to generate an R script like the following:
# Generated from cranlock
options(warn=2)
options(Ncpus=parallel::detectCores())
options(repos=structure(c(CRAN="https://cran.revolutionanalytics.com")))
devtools::install_version('codetools', version='0.2-15')
devtools::install_version('iterators', version='1.0.10')
devtools::install_version('foreach', version='1.4.4')
devtools::install_version('doParallel', version='1.0.11')
devtools::install_version('data.table', version='1.11.0')
devtools::install_version('getopt', version='1.20.3')
devtools::install_version('optparse', version='1.4.4')
devtools::install_version('lazyeval', version='0.2.1')
Conclusion
As long as you do it properly, pinning dependencies can save you a ton of time by drastically reducing surprise build failures.
Although we couldn’t find any, there could still be a more fully fleshed-out method of pinning R transitive dependencies out there. If there is, we would love to hear about it.
If you are interested in reading the full code, it is hosted on Github here: https://github.com/AlexsLemonade/cranlock
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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Applications are open for the Data Lab's next workshop! We will hold a Reproducible Research Practices Course on October 23-24, 2024 in Milwaukee, WI. 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, a very limited number of seats will be available.
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!
We are excited to announce that our next virtual workshop, Introduction to Single-cell RNA-Seq, will run from June 10-14, 2024! In this workshop, Data Lab staff will introduce researchers studying pediatric cancer to the R programming language, the Tidyverse R packages for data science, single-cell RNA-seq data analysis, and annotating cell types.
Applications are open for the Data Lab's next workshop! We are holding a two-day course on Reproducible Research Practices and the Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project from May 14-15, 2024. Please note that the OpenScPCA module is an optional part of the workshop. The course begins with an introduction to principles and techniques to achieve reproducible results in computational cancer research. On day two, you can choose to continue the workshop and learn how to put your skills to use for OpenScPCA, our new pediatric cancer research project.
Are you attending the American Association for Cancer Research (AACR) annual meeting in San Diego, CA? Visit the Alex’s Lemonade Stand Foundation (ALSF) Grants and Data Lab teams at booth 3755 in the exhibit hall from April 7-10 and during poster sessions on April 8. We will announce a new collaborative project and share exciting news about the Single-cell Pediatric Cancer Atlas Portal and training opportunities!
Did you know that 70% of the Alex’s Lemonade Stand Foundation (ALSF) Childhood Cancer Data Lab team are currently women? Advancing our mission to empower childhood cancer researchers with knowledge, data, and tools would not be possible without their expertise. On the International Day of Women and Girls in Science, we are excited to introduce you to these women who integrate science, engineering, and design to tackle some of the greatest challenges faced by the pediatric cancer research community!
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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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.
The Data Lab is excited to announce that our next training workshop will be held virtually from March 13-17, 2023! During this workshop, we will cover advanced topics in the analysis of single-cell RNA-seq data for researchers studying pediatric cancer. The workshop will take place each day from 12-5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with our staff available for consultation. You’ll need a laptop with internet access and to install Zoom and Slack. You will log into an RStudio Server hosted by the Data Lab from your web browser. Pediatric cancer researchers are encouraged to apply now!
In September 2022, the Open Pediatric Brain Tumor Atlas (OpenPBTA) project culminated (for now) in a preprint on bioRxiv. This project, started in late 2019 and co-organized with the Center for Data Driven Discovery in Biomedicine (D3b) at Children’s Hospital of Philadelphia (CHOP), is a collaborative effort to comprehensively describe the Pediatric Brain Tumor Atlas (PBTA), a collection of multiple data types from tens of tumor types (read more about why crowdsourcing expertise for the study of pediatric brain tumors is important here). The project is designed to allow for contributions from experts across multiple institutions. We’ve conducted analysis and drafting of the manuscript openly on the version-control platform GitHub from the project’s inception to facilitate those contributions.
Recently, we told you about the Single-cell Pediatric Cancer Atlas (ScPCA) downstream analysis workflow. This ready-to-go workflow is intended to be used with single-cell and single-nuclei gene expression data available on the ScPCA Portal. We developed this workflow to filter, normalize, and perform dimensionality reduction, as well as incorporate initial clustering results to each processed sample/library object. Now we’re excited to introduce one of our latest offerings for use with ScPCA data, a clustering analysis workflow, which can be applied to datasets after running the filtering, normalization, and dimensionality reduction workflow!
The Data Lab is excited to announce that our next training workshop will be held in-person from January 31-February 2, 2023! During this workshop, we will cover advanced topics in the analysis of single-cell RNA-seq data for researchers studying pediatric cancer. The 3-day course will take place from 9am-5pm Eastern time in Bala Cynwyd, PA, just outside of Philadelphia. Travel reimbursement is available for qualifying participants.
Welcome to the Data Lab’s December Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
In this blog post, I’d like to give an overview of the refine.bio refactoring process and web accessibility considerations. Through this process, our goal is to enhance the site usability and performance by improving the code quality and making the application more accessible. But before going into more details about them, let me provide you a quick history of refine.bio.
Welcome to the Data Lab’s November Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
Here at the Data Lab, we're all about, well, data! We believe that data sharing and accessibility is key to accelerating the research process, and ultimately to improving outcomes for childhood cancer patients. So, we were excited to learn that one of the goals of the NCI/NIH initiative, the Childhood Cancer Data Initiative (CCDI), is to build up a Data Ecosystem that will facilitate pediatric cancer researchers' ability to explore and collect data from disparate resources. Although this Ecosystem is still in the early stages, several components are already being developed and are available for researchers to use! One component that is particularly interesting to us is the CCDI's Childhood Cancer Data Catalog (CCDC).
Welcome to the October Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
Welcome to the September Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
At the Data Lab, we are constantly looking for ways to enhance the tools we build for pediatric cancer researchers. Earlier this year, we launched the Single-cell Pediatric Cancer Atlas portal, a database of uniformly-processed single-cell data from pediatric cancer clinical samples. One way we felt the portal could be even more beneficial to pediatric cancer researchers is with a ready-to-go workflow that takes in single-cell data and prepares it for downstream analyses such as unsupervised clustering.
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.
Though technology can introduce great benefit into our lives, it is often accompanied by a substantial amount of time and some expected frustration before we can reap the rewards. The time spent learning a new technology is what we usually call a learning curve.
The workshop will last from 9AM to 5PM on October 14th, 15th, and 16th at the CCDL offices at 1429 Walnut St Philadelphia, PA, 19102.
MultiPLIER is a machine learning approach that brings big data to bear on rare diseases. It’s also an example of the scientific approach and ethos of the CCDL, and the publication is a great opportunity to share how the CCDL is developing new technologies to accelerate research into cures for childhood cancers!
The Childhood Cancer Data Lab powered by Alex's Lemonade Stand Foundation is hosting a workshop to introduce childhood cancer researchers to reproducible analysis of bulk and single-cell transcriptomic data.
The Childhood Cancer Data Lab (CCDL), an initiative of Alex's Lemonade Stand Foundation develops tools, trainings, and methods to empower childhood cancer researchers. The work at the CCDL is focused and impactful. There are multiple opportunities and challenges for you to apply and grow your skills as a scientist or as an engineer.
The Childhood Cancer Data Lab powered by Alex's Lemonade Stand Foundation is hosting a workshop to introduce childhood cancer researchers to reproducible analysis of bulk and single-cell transcriptomic data.
At this hands-on, 3-day session held in Houston, researchers learned data science skills that could accelerate their own work. Drawing on skills learned at the workshop, childhood cancer researchers can perform basic analyses of their work to make informed decisions on how to proceed with their own research. Don’t just take our word for it, though. Read more about the workshop’s incredibly valuable benefits through its attendees’ perspectives.
The goal of our refine.bio project is to download, process, and make available gene expression datasets that can be analyzed together, or in parts, depending on a researcher’s need. Childhood cancer researchers need to be able to use data generated through multiple profiling technologies including microarrays and RNA-sequencing.
There are countless log blog posts out there about the benefits of good logging, how to log well, and how much to log. Going through them all can be a real log blog slog. Wouldn't it be cool if you could log like this:logger.info("Something happened!", job=job.id, user=user.id) and get an easily searchable output.
Caffeine is a stimulant that can induce alertness in certain individuals when consumed at an appropriate quantity. Caffeine is often obtained by ingesting caffeine-containing solutions. However, no protocol for obtaining caffeine from dehydrated, roasted beans using materials typically available in a Philadelphia office has been described in the published literature.
Alex’s Lemonade Stand Foundation (ALSF) staunchly believes that stronger scientific sharing practices will accelerate the pace of discovery and finding cures for children with cancer. Robust sharing improves reproducibility, minimizes redundant studies and maximizes our return on research investment.
Earlier this year, Alex’s Lemonade Stand Foundation identified single-cell gene expression profiling as an opportunity to build an atlas of cell types within tumors that could be broadly reused by pediatric cancer researchers.
This year was a big one for the CCDL. In our mission to empower pediatric cancer experts poised for big discoveries with the knowledge, data and methods to reach them we launched a software product, developed and delivered training workshops on single-cell and bulk RNA-seq analysis, and hired our data science team among other milestones.
I’m a scientist at Sage Bionetworks, a nonprofit research organization in Seattle, WA. My work focuses on a family of rare pediatric diseases (NF): neurofibromatosis type 1, type 2, and schwannomatosis.
Our particular process is designed to source opportunities from our team members and external stakeholders, convert those opportunities into a set of potential goals, and then select the goals that we expect will most advance our mission.
The ability to restore scroll position is often critical for website usability. It helps users keep the flow of navigation when going back and forth between different pages. Most modern browsers take care of restoring the scroll position automatically, but it doesn’t always work for Single Page Applications where the content is generated on the client’s side, often asynchronously.
Carnegie Mellon University Libraries is partnering with the Childhood Cancer Data Lab (CCDL), founded by Alex’s Lemonade Stand Foundation, to host a Data Analysis workshop using CCDL materials.
The CCDL will have a team of scientists at the American Association for Cancer Research 2020 Annual Meeting in sunny San Diego! Our team members are excited to talk to researchers studying pediatric cancer at Booth 1601.
To help keep pediatric cancer research moving forward, here are 3 ways the CCDL is helping the research community during this time: refine.bio, virtual workshops, and the Open Pediatric Brain Tumor Atlas project.
We know that pandemic-related university closures mean that the demand for opportunities for pediatric cancer researchers to increase their analytical skills has never been higher. As such, we are delighted to announce a pilot virtual workshop running from May 4-8, 2020!
Here at the Childhood Cancer Data Lab, we value transparency and the practice of open science. Much of the work we’ve done and the products that we build hinge on the generosity and openness of other scientists. In this post, as part of National Brain Tumor Awareness month, we want to talk about a project that our science team has been working on over the last few months (and to do so in a way that aligns with our values).
The workshop will take place on June 22 - 26, 2020 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
When the CCDL (along with everyone else) realized that we would have to conduct our bioinformatics training workshops remotely, we had to make some quick decisions about how we were going to do it. Most of the instructional materials for our in person workshops were already online, so we knew we had a good base to work from. We just needed to figure how to adapt the live instruction.
At Alex’s Lemonade Stand Foundation’s Childhood Cancer Data Lab, we’re excited to be helping out with an upcoming event hosted by the Children’s Tumor Foundation. If you participate, you may meet members of our team who are mentoring and judging.
The workshop will take place on March 22 - 26, 2021 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation
The workshop will take place on June 28- July 2, 2021 from noon to 5pm eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
Hack4Rare is a virtual event that calls for healthcare startups, developers, solutions architects, and hackathon enthusiasts to join researchers, clinicians and patients in developing solutions built around a number of rare diseases including neurofibromatosis, PTEN Hamartoma Tumor Syndrome, RASopathies and Desmoid Tumors.
The workshop will take place on September 20 - 24, 2021 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
Introducing refine.bio examples. Here, users can access a variety of example analyses implemented in R, such as clustering and heat maps, differential expression analysis, and pathway analysis, for use with refine.bio data.
The workshop will take place on November 1-5, 2021 from noon to 5pm eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with our staff available for consultation.
I work at the Childhood Cancer Data Lab, where we use very big data to find cures for childhood cancers. To move data around the internet at very high speeds, we are forced to use a proprietary software suite called Aspera. If somebody could make a Free Software alternative, the future of the internet would be way more awesome! Best of all, you can be the one to do it!
When my daughter Alex was diagnosed with cancer and throughout her battle, we saw how our community of people rallied around our family. No one knew quite how to help, but they were willing to do whatever was needed to ease the burden we faced.
'Work smarter not harder’ is useless advice if you don’t know how to ‘work smarter’. But the Childhood Cancer Data Lab's work and processes may be the smartest I’ve ever had the pleasure of learning and adopting.
The Data Lab will hold our first virtual workshop of the year from March 14-18, 2022!In this workshop, we will introduce researchers studying pediatric cancer to the R programming language, the Tidyverse R packages for data science, single-cell RNA-seq data analysis, and pathway analyses.
At the Data Lab, we are big proponents of automating the boring stuff so we can spend more time thinking about the fun stuff. But how exactly do we do that, and what does it mean to automate the boring stuff?
November marked the final Childhood Cancer Data Lab training workshop for 2021. We held four week-long virtual workshops this year, teaching 88 researchers the data science skills they need to examine their own data.
Before working as a Data Scientist at the Childhood Cancer Data Lab, I spent time in my PhD and post-doctoral fellowship in two very different research environments. Each had their own unique way of doing research. I found that some things worked really well and others were not as successful.
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