I’m terrible with names…but I’m using ontologies to try to be better
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.
There are many places in life where different people refer to the same object by different names: Is it a drinking fountain, a water fountain, a bubbler? We usually don’t have too much trouble with this, but it can certainly be confusing at times, and it can make searching for information or categorization difficult. To make things easier, we’d really like to have a single set of names that everybody uses with agreed-upon meanings. Then we would be able to search databases and articles for standard keywords and know that we were finding everything we needed. Well, that is the dream, anyway. As usual, the real world gets in the way.
This is why we can’t have nice things
Accepted names for cell types and diseases often change through time, and experimental methods are always being developed and evolving. For example, phenylketonuria was once known as Følling’s disease, or phenylalaninemia, and it is commonly referred to by the abbreviation PKU. Different biologists may have favorite names for their favorite cell types, or might use different levels of specificity depending on the study. We expect variation like this, but it can make searching across databases and papers for relevant information much more challenging!
So, if we can’t really expect everyone to agree on names, should we give up? Never! All we need to do is to create a database of all the possible names for each thing we care about and assign a code to track that set of names. Then we can look everything up in one place, and know that two different names really refer to the same cell type or the same disease. Easy! If we really wanted to go all-in we could even try to include in our database the relationships among the various entities that we cataloged: whether one cell type is a subtype of another, or ways in which diseases may be related depending on the pathways involved or the organs they affect.
What I’ve just described is an ontology, and they are so choice. If you have the means, I highly recommend picking one up. Unfortunately, building and maintaining an ontology is a tremendous amount of work, for all of the reasons you might expect (remember “everyone agrees”?). Happily, many ontologies already exist, ready for use, complete with dedicated teams behind them maintaining their contents and keeping everything up to date. Now it is our job to start to use them where we can.
Some workers spend their career developing ontologies and ontology-related tools, while few researchers (biologists and physicians) know how ontologies can accelerate their research.
(Rubin et al., 2008)
Ontologies I have known
One of the most famous ontologies in biology is the Gene Ontology, which was developed to describe the biological information that we might have about any given gene. Somewhat confusingly, genes themselves are not part of the ontology itself (naming is hard!), but instead the ontology contains terms like “hexose metabolic process” that describe individual elements of function, location, or biological process.
Just to continue the theme of naming challenges, a term like “hexose metabolic process” is not quite ideal as a consistent way to refer to a concept: for one thing, it only really works if you are speaking English, and we’d like to make our terms more universally useful. Furthermore, we might change the precise words we use for a concept, so something a bit more abstract might be useful for consistency. In most ontologies, this is done by assigning identifiers to each term, which usually consist of a prefix of a few letters followed by a number. For example, the term “hexose metabolic process” (and its synonyms) has the identifier “GO:0019318”.
When we want to describe a gene we can then use those identifiers to annotate everything we know about the gene (potentially assigning multiple annotations to each gene). If everybody does this as they learn new information about their favorite genes, and/or we have dedicated curators to keep the database up to date, we will end up with a large, interconnected database where we can look up genes by annotations and annotations by genes, across a wide and diverse range of studies.
When you start looking for them, ontologies start to show up all over the place. Experimental conditions can be described by the Experimental Factor Ontology, cell types by the Cell Ontology, diseases by the Disease Ontology, Human Phenotype Ontology, the Mondo Disease Ontology, among others.
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That last example may be a bit disconcerting: If there is more than one ontology that describes diseases, which one should you pick? I’m afraid I can’t easily answer that, as it probably depends on what other people in your field are using, which may not even be something you know. But if you can pick one, it is far more useful to have some defined terms and ontology identifiers than to have none, and it is usually possible to convert between ontologies if necessary.
Keeping track of all of the ontologies is a challenge in itself. Helpfully, EMBL's European Bioinformatics Institute maintains a searchable catalog of ontologies and their contents via their Ontology Lookup Service. The Ontology Lookup Service also maintains links among the various ontology terms, making it easier to translate between different ontologies that describe the same set of concepts, such as the various disease ontologies I just mentioned.
The Data Lab is diving in
Now, I don’t want you to think that ontologies are always easy to adopt, or that I have all the answers. I’m really just getting started with thinking about how best to take advantage of all of the value that ontologies promise in my own work. But as we go forward, the Data Lab is trying to incorporate ontologies more and more into the tools and databases that we build to encourage interoperability and utility.
- Within refine.bio, our database of processed RNA expression data, we are working to incorporate ontology-based annotations from the MetaSRA project to allow for better search and filtering by disease, experimental factors, and cell lines.
- In the Single-cell Pediatric Cancer Atlas (ScPCA) project, we are working on adding individual cell annotations as Cell Ontology terms to allow for comparisons across different experiments and annotation methods. Because the Cell Ontology also encodes how different cell types are related to one another, this can allow us to more easily compare methods that might classify cells to different levels of specificity.
- We also continue to use Gene Ontology and other functional ontologies when performing gene set enrichment analyses of various types.
Hopefully I’ve convinced you that ontologies are, at the very least, an idea worth spending a bit of time exploring. Next time you are assigning cell types or categorizing samples, maybe you will take a bit of time to see if there is an ontology that is right for you and your data. It won’t solve all the challenges of naming things, but it is a good place to start.
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.
There are many places in life where different people refer to the same object by different names: Is it a drinking fountain, a water fountain, a bubbler? We usually don’t have too much trouble with this, but it can certainly be confusing at times, and it can make searching for information or categorization difficult. To make things easier, we’d really like to have a single set of names that everybody uses with agreed-upon meanings. Then we would be able to search databases and articles for standard keywords and know that we were finding everything we needed. Well, that is the dream, anyway. As usual, the real world gets in the way.
This is why we can’t have nice things
Accepted names for cell types and diseases often change through time, and experimental methods are always being developed and evolving. For example, phenylketonuria was once known as Følling’s disease, or phenylalaninemia, and it is commonly referred to by the abbreviation PKU. Different biologists may have favorite names for their favorite cell types, or might use different levels of specificity depending on the study. We expect variation like this, but it can make searching across databases and papers for relevant information much more challenging!
So, if we can’t really expect everyone to agree on names, should we give up? Never! All we need to do is to create a database of all the possible names for each thing we care about and assign a code to track that set of names. Then we can look everything up in one place, and know that two different names really refer to the same cell type or the same disease. Easy! If we really wanted to go all-in we could even try to include in our database the relationships among the various entities that we cataloged: whether one cell type is a subtype of another, or ways in which diseases may be related depending on the pathways involved or the organs they affect.
What I’ve just described is an ontology, and they are so choice. If you have the means, I highly recommend picking one up. Unfortunately, building and maintaining an ontology is a tremendous amount of work, for all of the reasons you might expect (remember “everyone agrees”?). Happily, many ontologies already exist, ready for use, complete with dedicated teams behind them maintaining their contents and keeping everything up to date. Now it is our job to start to use them where we can.
Some workers spend their career developing ontologies and ontology-related tools, while few researchers (biologists and physicians) know how ontologies can accelerate their research.
(Rubin et al., 2008)
Ontologies I have known
One of the most famous ontologies in biology is the Gene Ontology, which was developed to describe the biological information that we might have about any given gene. Somewhat confusingly, genes themselves are not part of the ontology itself (naming is hard!), but instead the ontology contains terms like “hexose metabolic process” that describe individual elements of function, location, or biological process.
Just to continue the theme of naming challenges, a term like “hexose metabolic process” is not quite ideal as a consistent way to refer to a concept: for one thing, it only really works if you are speaking English, and we’d like to make our terms more universally useful. Furthermore, we might change the precise words we use for a concept, so something a bit more abstract might be useful for consistency. In most ontologies, this is done by assigning identifiers to each term, which usually consist of a prefix of a few letters followed by a number. For example, the term “hexose metabolic process” (and its synonyms) has the identifier “GO:0019318”.
When we want to describe a gene we can then use those identifiers to annotate everything we know about the gene (potentially assigning multiple annotations to each gene). If everybody does this as they learn new information about their favorite genes, and/or we have dedicated curators to keep the database up to date, we will end up with a large, interconnected database where we can look up genes by annotations and annotations by genes, across a wide and diverse range of studies.
When you start looking for them, ontologies start to show up all over the place. Experimental conditions can be described by the Experimental Factor Ontology, cell types by the Cell Ontology, diseases by the Disease Ontology, Human Phenotype Ontology, the Mondo Disease Ontology, among others.
That last example may be a bit disconcerting: If there is more than one ontology that describes diseases, which one should you pick? I’m afraid I can’t easily answer that, as it probably depends on what other people in your field are using, which may not even be something you know. But if you can pick one, it is far more useful to have some defined terms and ontology identifiers than to have none, and it is usually possible to convert between ontologies if necessary.
Keeping track of all of the ontologies is a challenge in itself. Helpfully, EMBL's European Bioinformatics Institute maintains a searchable catalog of ontologies and their contents via their Ontology Lookup Service. The Ontology Lookup Service also maintains links among the various ontology terms, making it easier to translate between different ontologies that describe the same set of concepts, such as the various disease ontologies I just mentioned.
The Data Lab is diving in
Now, I don’t want you to think that ontologies are always easy to adopt, or that I have all the answers. I’m really just getting started with thinking about how best to take advantage of all of the value that ontologies promise in my own work. But as we go forward, the Data Lab is trying to incorporate ontologies more and more into the tools and databases that we build to encourage interoperability and utility.
- Within refine.bio, our database of processed RNA expression data, we are working to incorporate ontology-based annotations from the MetaSRA project to allow for better search and filtering by disease, experimental factors, and cell lines.
- In the Single-cell Pediatric Cancer Atlas (ScPCA) project, we are working on adding individual cell annotations as Cell Ontology terms to allow for comparisons across different experiments and annotation methods. Because the Cell Ontology also encodes how different cell types are related to one another, this can allow us to more easily compare methods that might classify cells to different levels of specificity.
- We also continue to use Gene Ontology and other functional ontologies when performing gene set enrichment analyses of various types.
Hopefully I’ve convinced you that ontologies are, at the very least, an idea worth spending a bit of time exploring. Next time you are assigning cell types or categorizing samples, maybe you will take a bit of time to see if there is an ontology that is right for you and your data. It won’t solve all the challenges of naming things, but it is a good place to start.
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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.
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Applications are open for the Data Lab's next workshop! We will be holding a Reproducible Research Practices Course in-person on October 24-25, 2023. Instructors will introduce principles and techniques to achieve reproducible results in computational cancer research. We’ll show you the fundamentals of commonly-used approaches in reproducibility that you can apply to increase the impact of your research by making your findings more robust and reliable! To ensure that workshop attendees have a great hands-on experience, there will be a very limited number of seats available.
At the Center for Data-Driven Discovery in Biomedicine (D3b), I lead the Bioinformatics Translational Pediatric Oncology Team, a team of bioinformatics scientists. Our mission is to advance pediatric oncology research and precision medicine through collaboration and development of open-source analytical tools, frameworks, and data resources. In 1998, I lost my four year old cousin John Matthew to a brain tumor we now know was likely a diffuse intrinsic pontine glioma. So, it was bittersweet for me to see the Open Pediatric Brain Tumor Atlas (OpenPBTA) manuscript published in Cell Genomics on the last day of brain tumor awareness month this past year. But let’s rewind.
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In 2019, Alex’s Lemonade Stand Foundation (ALSF) established the Single-cell Pediatric Cancer Atlas (ScPCA) through awards for data generation and to create an atlas of single-cell gene expression profiles of pediatric cancers of different types and from different organ sites. The Data Lab launched the ScPCA Portal in 2022 to make uniformly processed, summarized single-cell and single-nuclei RNA-seq data and de-identified metadata available for download. The ScPCA Portal also supports other data modalities, such as bulk RNA-seq, CITE-seq, and spatial transcriptomics. The ScPCA Portal currently hosts data for over 500 pediatric tumor and patient-derived xenograft samples from more than 50 cancer types, and continues to grow. The Data Lab is seeking contributions to the ScPCA Portal from researchers with existing single-cell datasets.
We are excited to announce that our next workshop, Introduction to Single-cell RNA-Seq, will take place in-person from June 13-15, 2023! 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, annotating cell types, and more. The 3-day course will take place from 9am-5pm Eastern time in Bala Cynwyd, PA, just outside of Philadelphia. Travel reimbursement (up to a certain amount) is available for qualifying participants.
The Childhood Cancer Data Lab maintains a collection of uniformly processed single-cell data from pediatric cancer clinical samples and xenografts in the Single-cell Pediatric Cancer Atlas (ScPCA) Portal. Although access to preprocessed data saves researchers time, we know that the downloads from the ScPCA Portal are only the starting point. That’s why we’ve created downstream analysis workflows for commonly performed analyses. Instead of writing code wholesale, you can analyze data once you’ve configured these workflows.
We are excited to announce that our next virtual workshop, Introduction to Single-cell RNA-Seq, will run from May 15-19, 2023! 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.
Last year, the Data Lab launched the Single-cell Pediatric Cancer Atlas (ScPCA) Portal, which today holds uniformly processed single-cell gene expression data obtained from 8 separate labs, over 480 samples, and representing 38 cancer types. The portal is still growing as we continue to receive and process raw data from ScPCA investigators! All uniformly processed data is made available for download on the ScPCA Portal, giving researchers easy access to a growing database of summarized gene expression data and metadata to utilize for their own research. But how exactly did we make sure that all of the data was uniformly processed? And how are we able to ensure uniform processing for incoming samples as the portal continues to grow?
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!
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Welcome to the Data Lab’s November Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
Here at the Data Lab, we're all about, well, data! We believe that data sharing and accessibility is key to accelerating the research process, and ultimately to improving outcomes for childhood cancer patients. So, we were excited to learn that one of the goals of the NCI/NIH initiative, the Childhood Cancer Data Initiative (CCDI), is to build up a Data Ecosystem that will facilitate pediatric cancer researchers' ability to explore and collect data from disparate resources. Although this Ecosystem is still in the early stages, several components are already being developed and are available for researchers to use! One component that is particularly interesting to us is the CCDI's Childhood Cancer Data Catalog (CCDC).
Welcome to the October Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
Welcome to the September Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
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Welcome to the July Scientific Community Bulletin! Each month we share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Subscribe to our blog to be alerted about future Scientific Community Bulletin posts!
Welcome to the Childhood Cancer Data Lab’s new blog feature, the monthly Scientific Community Bulletin! At the start of each month, we will share upcoming opportunities from Alex’s Lemonade Stand Foundation (ALSF), the Data Lab, and other events that we have gathered from a variety of science and research organizations. Our goal is to promote learning opportunities and highlight some of the excellent resources that our community provides.
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The 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!
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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.
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The workshop will last from 9AM to 5PM on October 14th, 15th, and 16th at the CCDL offices at 1429 Walnut St Philadelphia, PA, 19102.
MultiPLIER is a machine learning approach that brings big data to bear on rare diseases. It’s also an example of the scientific approach and ethos of the CCDL, and the publication is a great opportunity to share how the CCDL is developing new technologies to accelerate research into cures for childhood cancers!
The Childhood Cancer Data Lab powered by Alex's Lemonade Stand Foundation is hosting a workshop to introduce childhood cancer researchers to reproducible analysis of bulk and single-cell transcriptomic data.
The Childhood Cancer Data Lab (CCDL), an initiative of Alex's Lemonade Stand Foundation develops tools, trainings, and methods to empower childhood cancer researchers. The work at the CCDL is focused and impactful. There are multiple opportunities and challenges for you to apply and grow your skills as a scientist or as an engineer.
The Childhood Cancer Data Lab powered by Alex's Lemonade Stand Foundation is hosting a workshop to introduce childhood cancer researchers to reproducible analysis of bulk and single-cell transcriptomic data.
At this hands-on, 3-day session held in Houston, researchers learned data science skills that could accelerate their own work. Drawing on skills learned at the workshop, childhood cancer researchers can perform basic analyses of their work to make informed decisions on how to proceed with their own research. Don’t just take our word for it, though. Read more about the workshop’s incredibly valuable benefits through its attendees’ perspectives.
The goal of our refine.bio project is to download, process, and make available gene expression datasets that can be analyzed together, or in parts, depending on a researcher’s need. Childhood cancer researchers need to be able to use data generated through multiple profiling technologies including microarrays and RNA-sequencing.
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Alex’s Lemonade Stand Foundation (ALSF) staunchly believes that stronger scientific sharing practices will accelerate the pace of discovery and finding cures for children with cancer. Robust sharing improves reproducibility, minimizes redundant studies and maximizes our return on research investment.
Earlier this year, Alex’s Lemonade Stand Foundation identified single-cell gene expression profiling as an opportunity to build an atlas of cell types within tumors that could be broadly reused by pediatric cancer researchers.
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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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