Gene Expression Repositories Explained
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. A big part of what refine.bio does is downloading gene expression data from various repositories. We wanted to download and provide ALL THE DATA, but certain repositories mirror some fraction of other repositories. To be able to do this successfully without producing duplicate data, we had to learn a lot about the various repositories for gene expression data and their data models. We also needed to determine if and how the various repositories overlapped. This post has two main sections:
- Repositories and Organizations: The different repositories we download from, the organizations that run them, and how they interconnect and overlap.
- The SRA Data Model: An explanation of the data model used by the Sequence Read Archive (SRA).
The Repositories and Organizations
The data refine.bio has downloaded, processed, and is now serving can be found across five repositories (Array Express, GEO, SRA, ENA, and DRA) which are run by three different organizations (NCBI, EMBL-EBI, and DDBJ). That’s a whole alphabet of acronyms so why don’t we start by explaining what/who they all are.
The National Center for Biotechnology Information (NCBI)
NCBI is a part of the U.S. National Library of Medicine which is part of The National Institutes of Health (NIH). NCBI has a lot of resources, tools, programs, and data repositories but the two that store gene expression data are The Gene Expression Omnibus (GEO) and the Sequence Read Archive (SRA). Microarray data are stored in GEO. RNA-seq data are primarily stored in SRA; however, certain RNA-seq datasets without raw data available appear to be provided via GEO.
The European Molecular Biology Laboratory’s European Bioinformatic’s Institute (EMBL-EBI or EBI)
EBI is another large organization which also offers a lot of resources, tools, programs, and data repositories, but the two that store gene expression data are Array Express and The European Nucleotide Archive (ENA). Array Express and the ENA have a similar relationship to GEO & SRA at NCBI, though they serve as the primary data deposit location for investigators in Europe.
The DNA Data Bank of Japan (DDBJ)
The DDBJ seems to be smaller than the NCBI or EMBL-EBI in terms of data uniquely found at the DDBJ, but it does include the DDBJ Sequence Read Archive (DRA) which is relevant for this post.
The Sequence Read Archive
Over 30 years ago subsections of NCBI, EMBL-EBI, and DDBJ came together to form the The International Nucleotide Sequence Database Collaboration (INSDC). (source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013722/) The INSDC joined NCBI’s Genbank, EMB-EBI’s ENA, and DDBJ’s DRA together into a unified group “to ensure that all public domain nucleotide sequence data deposited in the archives is preserved as part of the scientific record and is accessible in standardized formats across the three sites through daily data exchange.” (source: https://academic.oup.com/nar/article/46/D1/D48/4668651). The INSDC has two primary offerings: “Raw data archives under the collaboration are known as the Trace Archive for raw data from capillary electrophoresis platforms and the Sequence Read Archive for raw and read alignment data from next-generation platforms.” (source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013722/).
You may have noticed that both NCBI and INSDC both have an offering called SRA and you may be wondering if these are the same thing. As far as I can tell, the answer is both yes and no. The INSDC’s SRA is a special database that is co-managed by NCBI, EBI, and DDBJ. Each of those organizations host a full copy of the database. EBI’s copy of the database is called ENA, DDBJ’s copy of the database is called DRA, and NCBI’s copy of the database is confusingly called SRA. Each of these organizations also can accept new submissions to the database.
If an experiment is first submitted to ENA, it and associated data objects will be prefixed with `ER` such as ERP008771, ERX1762259, or ERR1692631. If an experiment is first submitted to DRA, it and associated data objects will instead be prefixed with `DR` such as DRP000425, DRX000772, or DRR001175. If an experiment is first submitted to NCBI’s SRA, it and associated data objects will be prefixed with `SR` such as SRP060416, SRX1082691, or SRR2088722. If you’re asking what are the differences between ERP/DRP/SRP, ERX/DRX/SRX, and ERR/DRR/SRR data objects, then stay tuned. I’ll cover SRA’s data model in the next section.
Fun Fact: According to the SRA wikipedia page it used to stand for Short Read Archive, so if you ever see that it’s not entirely wrong.
Bonus: While researching this post, I came across China’s Genome Sequence Archive (GSA) which adheres “with data standards and structures of the INSDC” (source: https://www.sciencedirect.com/science/article/pii/S1672022917300025). However they do not appear to be replicating and contributing to the shared SRA repository and instead are maintaining their own collection. Downloading, processing, and serving data from GSA has already been added to our future plans, especially given the growth in Chinese investment in basic science.
Microarray Repositories
refine.bio harmonizes two different types of gene expression data: RNA-seq and microarray. SRA is where refine.bio downloads all its RNA-seq data. refine.bio downloads microarray data from both NCBI’s GEO and EMBL-EBI’s Array Express. These two repositories also have a somewhat convoluted relationship. Array Express used to replicate data from GEO on a weekly basis, so it contains a lot of data from there. This was true when we first started the refine.bio project, so our original idea was to only download microarray data from Array Express. However since then they have stopped replicating data, so we now download data from the source (Array Express / GEO) that it was originally uploaded to, which we determine by its identifier.
Fun Fact: Any accession code on Array Express that starts with “E-GEOD-” was duplicated from GEO. If you remove the leading “E-GEOD-” and replace it with “GSE”, you get the accession code used by GEO! For example, E-GEOD-7307 is the same experiment as GSE7307.
GEO is an NCBI offering. It does not include raw RNA-seq data that can be retrieved from SRA. However, there are some experiments that mix RNA-seq and microarray data in the GEO database. These will sometimes have the raw microarray data associated with them, but only metadata or processed data for RNA-seq data. However, using the metadata it is possible to find the same sample in SRA to get the raw data. As far as we have seen so far, Array Express does not host any RNA-seq data.
The Full Picture
If you have gotten confused trying to follow along as I explained the relationships between INSDC, NCBI, EMBL-EBI, DDBJ, and their various sub-organizations and offerings, we’ve put together this diagram which may help:
The SRA Data Model
Each these organizations do a lot more than just host the Gene Expression data we need for refine.bio, but that is outside the scope of this post. However, I promised to explain the SRA data model. To be clear, for the rest of this post when I say “SRA” I am referring to INSDC’s SRA instead of NCBI’s SRA. Everything will apply to both, but every member of the INSDC uses the same data model for their SRA databases.
Let’s start with the six types of SRA metadata objects, then we can get into how they relate to each other:
Run - A metadata object that directly represents the file generated by sequencing. Accessions for Runs have the prefix SRR/ERR/DRR.
Experiment - Metadata about how the sequencing was performed. Accessions for Experiments have the prefix SRX/ERX/DRX.
Sample - A description of biologically or physically unique specimens. Directly corresponds to a BioSample. Accessions for Samples have the prefix SRS/ERS/DRS.
Study - A description of the research effort that required the sequencing. Directly corresponds to a BioProject. Accessions for Studies have the prefix SRP/ERP/DRP.
Submission - Metadata about the submission of the data to SRA. Accessions for Submissions have the prefix SRA/ERA/DRA.
Analysis - A representation of an analysis that was submitted to SRA about the data. refine.bio does not survey or store metadata objects of this type.
(source: https://www.ddbj.nig.ac.jp/dra/submission-e.html)
The simplest image we’ve seen of the SRA data model comes from the ENA’s page on programmatically submitting data, although unfortunately it leaves out Submissions:
The description on that page is also one of the better descriptions of how the different objects relate to each other. The key takeaways for understanding this data model are:
- A single Sample can be used by more than one Experiment, so there is a one-to-many relationship between Samples and Experiments.
- Multiple Experiments can be grouped together to form a Study, so there is also a one-to-many relationship between Studies and Experiments.
- One Experiment can be run through the sequencing apparatus multiple time to generate multiple Runs, so there is a one-to-many relationship between Experiments and Runs.
- A Run has a one-to-one relationship with actual files containing reads, unless the Run was Paired-End, in which case there will be two files (assuming the FASTQ format was used).
The one metadata object type that is omitted here is Submission. The documentation is a bit less clear about the links for Submissions, most likely because they are generally linked up automatically during the submission process, which is what that documentation is about. However, Submission objects aren’t that involved. They are actually representing the submission of the data to SRA and therefore they can be linked to any of the other objects. They don’t have too much information other than linking everything together explicitly.
Simplifying for the Future
This pretty much covers what we know about gene expression repositories that wasn’t very easy for us to find and learn while developing refine.bio. We hope that as refine.bio discovers, downloads, processes, and makes available more and more datasets that fewer and fewer people will need to understand these arcane secrets. However, we think it’s important that people are able to use these repositories directly and we hope this post makes that a bit easier.
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. A big part of what refine.bio does is downloading gene expression data from various repositories. We wanted to download and provide ALL THE DATA, but certain repositories mirror some fraction of other repositories. To be able to do this successfully without producing duplicate data, we had to learn a lot about the various repositories for gene expression data and their data models. We also needed to determine if and how the various repositories overlapped. This post has two main sections:
- Repositories and Organizations: The different repositories we download from, the organizations that run them, and how they interconnect and overlap.
- The SRA Data Model: An explanation of the data model used by the Sequence Read Archive (SRA).
The Repositories and Organizations
The data refine.bio has downloaded, processed, and is now serving can be found across five repositories (Array Express, GEO, SRA, ENA, and DRA) which are run by three different organizations (NCBI, EMBL-EBI, and DDBJ). That’s a whole alphabet of acronyms so why don’t we start by explaining what/who they all are.
The National Center for Biotechnology Information (NCBI)
NCBI is a part of the U.S. National Library of Medicine which is part of The National Institutes of Health (NIH). NCBI has a lot of resources, tools, programs, and data repositories but the two that store gene expression data are The Gene Expression Omnibus (GEO) and the Sequence Read Archive (SRA). Microarray data are stored in GEO. RNA-seq data are primarily stored in SRA; however, certain RNA-seq datasets without raw data available appear to be provided via GEO.
The European Molecular Biology Laboratory’s European Bioinformatic’s Institute (EMBL-EBI or EBI)
EBI is another large organization which also offers a lot of resources, tools, programs, and data repositories, but the two that store gene expression data are Array Express and The European Nucleotide Archive (ENA). Array Express and the ENA have a similar relationship to GEO & SRA at NCBI, though they serve as the primary data deposit location for investigators in Europe.
The DNA Data Bank of Japan (DDBJ)
The DDBJ seems to be smaller than the NCBI or EMBL-EBI in terms of data uniquely found at the DDBJ, but it does include the DDBJ Sequence Read Archive (DRA) which is relevant for this post.
The Sequence Read Archive
Over 30 years ago subsections of NCBI, EMBL-EBI, and DDBJ came together to form the The International Nucleotide Sequence Database Collaboration (INSDC). (source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013722/) The INSDC joined NCBI’s Genbank, EMB-EBI’s ENA, and DDBJ’s DRA together into a unified group “to ensure that all public domain nucleotide sequence data deposited in the archives is preserved as part of the scientific record and is accessible in standardized formats across the three sites through daily data exchange.” (source: https://academic.oup.com/nar/article/46/D1/D48/4668651). The INSDC has two primary offerings: “Raw data archives under the collaboration are known as the Trace Archive for raw data from capillary electrophoresis platforms and the Sequence Read Archive for raw and read alignment data from next-generation platforms.” (source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013722/).
You may have noticed that both NCBI and INSDC both have an offering called SRA and you may be wondering if these are the same thing. As far as I can tell, the answer is both yes and no. The INSDC’s SRA is a special database that is co-managed by NCBI, EBI, and DDBJ. Each of those organizations host a full copy of the database. EBI’s copy of the database is called ENA, DDBJ’s copy of the database is called DRA, and NCBI’s copy of the database is confusingly called SRA. Each of these organizations also can accept new submissions to the database.
If an experiment is first submitted to ENA, it and associated data objects will be prefixed with `ER` such as ERP008771, ERX1762259, or ERR1692631. If an experiment is first submitted to DRA, it and associated data objects will instead be prefixed with `DR` such as DRP000425, DRX000772, or DRR001175. If an experiment is first submitted to NCBI’s SRA, it and associated data objects will be prefixed with `SR` such as SRP060416, SRX1082691, or SRR2088722. If you’re asking what are the differences between ERP/DRP/SRP, ERX/DRX/SRX, and ERR/DRR/SRR data objects, then stay tuned. I’ll cover SRA’s data model in the next section.
Fun Fact: According to the SRA wikipedia page it used to stand for Short Read Archive, so if you ever see that it’s not entirely wrong.
Bonus: While researching this post, I came across China’s Genome Sequence Archive (GSA) which adheres “with data standards and structures of the INSDC” (source: https://www.sciencedirect.com/science/article/pii/S1672022917300025). However they do not appear to be replicating and contributing to the shared SRA repository and instead are maintaining their own collection. Downloading, processing, and serving data from GSA has already been added to our future plans, especially given the growth in Chinese investment in basic science.
Microarray Repositories
refine.bio harmonizes two different types of gene expression data: RNA-seq and microarray. SRA is where refine.bio downloads all its RNA-seq data. refine.bio downloads microarray data from both NCBI’s GEO and EMBL-EBI’s Array Express. These two repositories also have a somewhat convoluted relationship. Array Express used to replicate data from GEO on a weekly basis, so it contains a lot of data from there. This was true when we first started the refine.bio project, so our original idea was to only download microarray data from Array Express. However since then they have stopped replicating data, so we now download data from the source (Array Express / GEO) that it was originally uploaded to, which we determine by its identifier.
Fun Fact: Any accession code on Array Express that starts with “E-GEOD-” was duplicated from GEO. If you remove the leading “E-GEOD-” and replace it with “GSE”, you get the accession code used by GEO! For example, E-GEOD-7307 is the same experiment as GSE7307.
GEO is an NCBI offering. It does not include raw RNA-seq data that can be retrieved from SRA. However, there are some experiments that mix RNA-seq and microarray data in the GEO database. These will sometimes have the raw microarray data associated with them, but only metadata or processed data for RNA-seq data. However, using the metadata it is possible to find the same sample in SRA to get the raw data. As far as we have seen so far, Array Express does not host any RNA-seq data.
The Full Picture
If you have gotten confused trying to follow along as I explained the relationships between INSDC, NCBI, EMBL-EBI, DDBJ, and their various sub-organizations and offerings, we’ve put together this diagram which may help:
The SRA Data Model
Each these organizations do a lot more than just host the Gene Expression data we need for refine.bio, but that is outside the scope of this post. However, I promised to explain the SRA data model. To be clear, for the rest of this post when I say “SRA” I am referring to INSDC’s SRA instead of NCBI’s SRA. Everything will apply to both, but every member of the INSDC uses the same data model for their SRA databases.
Let’s start with the six types of SRA metadata objects, then we can get into how they relate to each other:
Run - A metadata object that directly represents the file generated by sequencing. Accessions for Runs have the prefix SRR/ERR/DRR.
Experiment - Metadata about how the sequencing was performed. Accessions for Experiments have the prefix SRX/ERX/DRX.
Sample - A description of biologically or physically unique specimens. Directly corresponds to a BioSample. Accessions for Samples have the prefix SRS/ERS/DRS.
Study - A description of the research effort that required the sequencing. Directly corresponds to a BioProject. Accessions for Studies have the prefix SRP/ERP/DRP.
Submission - Metadata about the submission of the data to SRA. Accessions for Submissions have the prefix SRA/ERA/DRA.
Analysis - A representation of an analysis that was submitted to SRA about the data. refine.bio does not survey or store metadata objects of this type.
(source: https://www.ddbj.nig.ac.jp/dra/submission-e.html)
The simplest image we’ve seen of the SRA data model comes from the ENA’s page on programmatically submitting data, although unfortunately it leaves out Submissions:
The description on that page is also one of the better descriptions of how the different objects relate to each other. The key takeaways for understanding this data model are:
- A single Sample can be used by more than one Experiment, so there is a one-to-many relationship between Samples and Experiments.
- Multiple Experiments can be grouped together to form a Study, so there is also a one-to-many relationship between Studies and Experiments.
- One Experiment can be run through the sequencing apparatus multiple time to generate multiple Runs, so there is a one-to-many relationship between Experiments and Runs.
- A Run has a one-to-one relationship with actual files containing reads, unless the Run was Paired-End, in which case there will be two files (assuming the FASTQ format was used).
The one metadata object type that is omitted here is Submission. The documentation is a bit less clear about the links for Submissions, most likely because they are generally linked up automatically during the submission process, which is what that documentation is about. However, Submission objects aren’t that involved. They are actually representing the submission of the data to SRA and therefore they can be linked to any of the other objects. They don’t have too much information other than linking everything together explicitly.
Simplifying for the Future
This pretty much covers what we know about gene expression repositories that wasn’t very easy for us to find and learn while developing refine.bio. We hope that as refine.bio discovers, downloads, processes, and makes available more and more datasets that fewer and fewer people will need to understand these arcane secrets. However, we think it’s important that people are able to use these repositories directly and we hope this post makes that a bit easier.
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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.
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.
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.
When the Data Lab launched the Single-cell Pediatric Cancer Atlas (ScPCA) Portal in 2022, we knew it was only the beginning! We started by making data easily available for the research community and received an overwhelmingly positive response. But we know firsthand from training hundreds of pediatric cancer researchers in analysis that making data available is just the first step. We’re increasing the impact of the Portal by listening to the growing ScPCA community. Now more researchers can contribute datasets, new features are continuously being developed, and we started an open, collaborative project to further explore the available data! Here’s a look back at how we’ve enhanced the ScPCA Portal in 2024.
Launching the Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project in April 2024 was a highlight of our year! This community-driven initiative aims to analyze data from the ScPCA Portal, which currently holds 700 samples from over 55 pediatric cancer types. The project is a step forward in advancing our knowledge of pediatric cancers through single-cell analysis, and we're excited to expand OpenScPCA in 2025! To that end, we're reflecting on some of our recent accomplishments and how we can keep that momentum going into next year.
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