Introducing the ScPCA downstream analysis workflow!
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.
We put together the ScPCA downstream analysis workflow to filter, normalize, and perform dimensionality reduction, as well as incorporate initial clustering results to each processed sample/library object.
What the workflow does
The workflow takes in pre-processed gene expression data stored as a SingleCellExperiment object (filtered to remove empty cells/droplets), and performs the following steps:
- Filtering - Each library is filtered to remove any low quality cells using either miQC::filterCells() or through setting a series of manual thresholds (e.g. the minimum number of UMI counts). Genes found in a low percentage of cells in each library are also removed at this step.
- Normalization - Cells are normalized using the deconvolution method from Lun, Bach, and Marioni (2016). The normalized log counts are stored in the SingleCellExperiment object returned by the workflow.
- Dimensionality Reduction - Reduced dimensions are calculated using both principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). The embeddings from PCA and UMAP can be found in the SingleCellExperiment object returned by the workflow.
- Clustering - Cells are assigned to cell clusters using graph-based clustering. By default, louvain clustering is performed using the bluster::NNGraphParam() function with a default nearest-neighbors parameter of 10. Alternatively, walktrap graph-based clustering can be specified, and the number of nearest neighbors parameter can be altered if desired. Cluster assignments are stored in the SingleCellExperiment object returned by the workflow.
What the workflow does not do
The workflow is meant to be a user-friendly, ready-to-go workflow that will perform the core steps often needed when analyzing single-cell data. However, there are a few things that the workflow will not do:
- The workflow will not pre-process your data. Instead, you can see our scpca-nf workflow and ScPCA Portal docs for our suggested approach to pre-processing your data before implementing this workflow, which includes filtering to remove empty cells/droplets.
- The workflow will not convert your data into a SingleCellExperiment object, which means that if your data exists in other formats (e.g. a Seurat object), you will need to perform the initial conversion yourself.
- The workflow will not make decisions about your data. You will be provided with results based on the parameters used throughout the workflow, but we do not do the science for you! You will need to look at the results for yourself to interpret whether or not they are reasonable. Decision making can be highly dependent on the needs of individual researchers, so making such decisions for everyone would not be possible or beneficial.
Workflow requirements
To run the downstream analyses workflow on your own sample data, you will need the following:
- Single-cell gene expression data stored as SingleCellExperiment objects in RDS files. The workflow is set up to process data available on the Single-cell Pediatric Cancer Atlas portal and output from the scpca-nf workflow where single-cell/single-nuclei gene expression data is mapped and quantified using alevin-fry. For more information on this pre-processing, please see the ScPCA Portal docs. Note however that the input for this pipeline is not required to be scpca-nf processed output; the only requirement is that gene expression data be stored as a SingleCellExperiment object in an RDS file.
- A project metadata tab-separated value (TSV) file containing relevant information about the sample data necessary for processing, as described in the metadata file format section of the GitHub repository’s README file.
- A mitochondrial gene list that is specific to the genome or transcriptome version used for alignment. The workflow will use the mitochondrial gene list obtained from Ensembl version 104 by default. This gene list can be found in the reference-files directory of the Github repository.
- A local installation of Snakemake and either R or conda. (See more on this in the how to install the core downstream analyses workflow section.)
Expected output
For each SingleCellExperiment associated with an individual library, the workflow will return two files: a processed SingleCellExperiment object stored in an RDS file containing normalized data and clustering results, and a summary HTML report detailing the filtering of low-quality cells, dimensionality reduction, and clustering that was performed within the workflow. Below is an example of the expected output file structure.
The first output file is the *<library_id>_<filtering_method>_processed_sce.rds file, where `library_id` is the unique library and `filtering_method` is one of miQC or manual. This is the RDS file that contains the final processed SingleCellExperiment object, which contains the filtered, normalized data and clustering results. Clustering results can be found in the colData of the SingleCellExperiment object, stored in a metadata column named using the associated clustering type and nearest neighbors values.
The *<library_id>_<filtering_method>_core_analysis_report.html file is the html file that contains the summary report of the filtering, dimensionality reduction, and clustering results associated with the processed SingleCellExperiment object.
Walk through an example with us!
After ensuring that all of the required software is installed on your machine, as described in the how to install the core downstream analyses workflow section of the GitHub repository’s README file, we are ready to get started with the workflow!
Step 1 - Set up input data
Once you confirm that your single-cell gene expression data has been stored as SingleCellExperiment objects within RDS files, where each RDS file is associated with a single library id, you are ready to set up the project metadata file.
Create a new TSV file, where each row corresponds to a unique library and the columns contain the information associated with that library, and save it in the project-metadata directory. Below is an example of the columns and values that should be in the project metadata TSV file. See more on the columns and their expected values at the metadata file format section of the Github repository’s README.
Step 2 - Run the workflow via command line
Now we can run the snakemake workflow via the command line. To run the workflow we will also need to specify a few parameters, such as where to store the output, the path to the project metadata file, and the path to the mitochondrial gene list. We specify this information using the--config flag which allows us to override their default values for the relevant variables found in the config.yaml file. Below is an example of running the workflow via the command line while modifying only the necessary config file variables.
Step 3 - Check output!
Upon successful completion of the workflow, you should have two output files per sample/library id specified in the project metadata file. Below is the output of the test sample data we ran.
How to get started!
If you are interested in trying the ScPCA downstream analysis workflow on your data or are just curious to learn a bit more about how it works, you can get started with the README associated with the GitHub repository! We also have provided documentation with further details on the processing steps and decisions made throughout the workflow.
Additionally, if you are interested in participating in usability testing of the downstream analysis workflow, please fill out this form and we will contact you when we are ready for testing!
If you have questions about the ScPCA portal, you can reach out to us at scpca@ccdatalab.org!
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.
We put together the ScPCA downstream analysis workflow to filter, normalize, and perform dimensionality reduction, as well as incorporate initial clustering results to each processed sample/library object.
What the workflow does
The workflow takes in pre-processed gene expression data stored as a SingleCellExperiment object (filtered to remove empty cells/droplets), and performs the following steps:
- Filtering - Each library is filtered to remove any low quality cells using either miQC::filterCells() or through setting a series of manual thresholds (e.g. the minimum number of UMI counts). Genes found in a low percentage of cells in each library are also removed at this step.
- Normalization - Cells are normalized using the deconvolution method from Lun, Bach, and Marioni (2016). The normalized log counts are stored in the SingleCellExperiment object returned by the workflow.
- Dimensionality Reduction - Reduced dimensions are calculated using both principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). The embeddings from PCA and UMAP can be found in the SingleCellExperiment object returned by the workflow.
- Clustering - Cells are assigned to cell clusters using graph-based clustering. By default, louvain clustering is performed using the bluster::NNGraphParam() function with a default nearest-neighbors parameter of 10. Alternatively, walktrap graph-based clustering can be specified, and the number of nearest neighbors parameter can be altered if desired. Cluster assignments are stored in the SingleCellExperiment object returned by the workflow.
What the workflow does not do
The workflow is meant to be a user-friendly, ready-to-go workflow that will perform the core steps often needed when analyzing single-cell data. However, there are a few things that the workflow will not do:
- The workflow will not pre-process your data. Instead, you can see our scpca-nf workflow and ScPCA Portal docs for our suggested approach to pre-processing your data before implementing this workflow, which includes filtering to remove empty cells/droplets.
- The workflow will not convert your data into a SingleCellExperiment object, which means that if your data exists in other formats (e.g. a Seurat object), you will need to perform the initial conversion yourself.
- The workflow will not make decisions about your data. You will be provided with results based on the parameters used throughout the workflow, but we do not do the science for you! You will need to look at the results for yourself to interpret whether or not they are reasonable. Decision making can be highly dependent on the needs of individual researchers, so making such decisions for everyone would not be possible or beneficial.
Workflow requirements
To run the downstream analyses workflow on your own sample data, you will need the following:
- Single-cell gene expression data stored as SingleCellExperiment objects in RDS files. The workflow is set up to process data available on the Single-cell Pediatric Cancer Atlas portal and output from the scpca-nf workflow where single-cell/single-nuclei gene expression data is mapped and quantified using alevin-fry. For more information on this pre-processing, please see the ScPCA Portal docs. Note however that the input for this pipeline is not required to be scpca-nf processed output; the only requirement is that gene expression data be stored as a SingleCellExperiment object in an RDS file.
- A project metadata tab-separated value (TSV) file containing relevant information about the sample data necessary for processing, as described in the metadata file format section of the GitHub repository’s README file.
- A mitochondrial gene list that is specific to the genome or transcriptome version used for alignment. The workflow will use the mitochondrial gene list obtained from Ensembl version 104 by default. This gene list can be found in the reference-files directory of the Github repository.
- A local installation of Snakemake and either R or conda. (See more on this in the how to install the core downstream analyses workflow section.)
Expected output
For each SingleCellExperiment associated with an individual library, the workflow will return two files: a processed SingleCellExperiment object stored in an RDS file containing normalized data and clustering results, and a summary HTML report detailing the filtering of low-quality cells, dimensionality reduction, and clustering that was performed within the workflow. Below is an example of the expected output file structure.
The first output file is the *<library_id>_<filtering_method>_processed_sce.rds file, where `library_id` is the unique library and `filtering_method` is one of miQC or manual. This is the RDS file that contains the final processed SingleCellExperiment object, which contains the filtered, normalized data and clustering results. Clustering results can be found in the colData of the SingleCellExperiment object, stored in a metadata column named using the associated clustering type and nearest neighbors values.
The *<library_id>_<filtering_method>_core_analysis_report.html file is the html file that contains the summary report of the filtering, dimensionality reduction, and clustering results associated with the processed SingleCellExperiment object.
Walk through an example with us!
After ensuring that all of the required software is installed on your machine, as described in the how to install the core downstream analyses workflow section of the GitHub repository’s README file, we are ready to get started with the workflow!
Step 1 - Set up input data
Once you confirm that your single-cell gene expression data has been stored as SingleCellExperiment objects within RDS files, where each RDS file is associated with a single library id, you are ready to set up the project metadata file.
Create a new TSV file, where each row corresponds to a unique library and the columns contain the information associated with that library, and save it in the project-metadata directory. Below is an example of the columns and values that should be in the project metadata TSV file. See more on the columns and their expected values at the metadata file format section of the Github repository’s README.
Step 2 - Run the workflow via command line
Now we can run the snakemake workflow via the command line. To run the workflow we will also need to specify a few parameters, such as where to store the output, the path to the project metadata file, and the path to the mitochondrial gene list. We specify this information using the--config flag which allows us to override their default values for the relevant variables found in the config.yaml file. Below is an example of running the workflow via the command line while modifying only the necessary config file variables.
Step 3 - Check output!
Upon successful completion of the workflow, you should have two output files per sample/library id specified in the project metadata file. Below is the output of the test sample data we ran.
How to get started!
If you are interested in trying the ScPCA downstream analysis workflow on your data or are just curious to learn a bit more about how it works, you can get started with the README associated with the GitHub repository! We also have provided documentation with further details on the processing steps and decisions made throughout the workflow.
Additionally, if you are interested in participating in usability testing of the downstream analysis workflow, please fill out this form and we will contact you when we are ready for testing!
If you have questions about the ScPCA portal, you can reach out to us at scpca@ccdatalab.org!
Related Post
In March 2022, we launched the Single-cell Pediatric Cancer Atlas (ScPCA) Portal to make uniformly processed single-cell and single-nuclei RNA-Seq data widely available to the childhood cancer research community. Initially, all data available on the Portal was generated through grants funded by Alex’s Lemonade Stand Foundation (ALSF) as part of the ScPCA project. But enabling access to ALSF-funded data was just the beginning of our vision.Sharing is key to ensuring the Portal’s continued growth. Our sights were set on allowing more pediatric cancer researchers to contribute data to the ScPCA Portal.
The Data Lab has just launched the brand new Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project! This open, collaborative project aims to analyze data from the ScPCA Portal, which currently holds 500 samples from over 50 pediatric cancer types. We are seeking contributors with diverse skills and expertise to join the project!
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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!
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Are you attending the American Association for Cancer Research (AACR) annual meeting in Orlando, FL this year? Visit Alex's Lemonade Stand Foundation (ALSF) at booth 369 in the exhibit hall from April 16-19! You'll find information about ALSF's grants program, the Childhood Cancer Data Lab and more. The Data Lab will also be holding office hours during select time slots.
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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.
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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.
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Alex’s Lemonade Stand Foundation (ALSF) staunchly believes that stronger scientific sharing practices will accelerate the pace of discovery and finding cures for children with cancer. Robust sharing improves reproducibility, minimizes redundant studies and maximizes our return on research investment.
Earlier this year, Alex’s Lemonade Stand Foundation identified single-cell gene expression profiling as an opportunity to build an atlas of cell types within tumors that could be broadly reused by pediatric cancer researchers.
This year was a big one for the CCDL. In our mission to empower pediatric cancer experts poised for big discoveries with the knowledge, data and methods to reach them we launched a software product, developed and delivered training workshops on single-cell and bulk RNA-seq analysis, and hired our data science team among other milestones.
I’m a scientist at Sage Bionetworks, a nonprofit research organization in Seattle, WA. My work focuses on a family of rare pediatric diseases (NF): neurofibromatosis type 1, type 2, and schwannomatosis.
Our particular process is designed to source opportunities from our team members and external stakeholders, convert those opportunities into a set of potential goals, and then select the goals that we expect will most advance our mission.
The ability to restore scroll position is often critical for website usability. It helps users keep the flow of navigation when going back and forth between different pages. Most modern browsers take care of restoring the scroll position automatically, but it doesn’t always work for Single Page Applications where the content is generated on the client’s side, often asynchronously.
Carnegie Mellon University Libraries is partnering with the Childhood Cancer Data Lab (CCDL), founded by Alex’s Lemonade Stand Foundation, to host a Data Analysis workshop using CCDL materials.
The CCDL will have a team of scientists at the American Association for Cancer Research 2020 Annual Meeting in sunny San Diego! Our team members are excited to talk to researchers studying pediatric cancer at Booth 1601.
To help keep pediatric cancer research moving forward, here are 3 ways the CCDL is helping the research community during this time: refine.bio, virtual workshops, and the Open Pediatric Brain Tumor Atlas project.
We know that pandemic-related university closures mean that the demand for opportunities for pediatric cancer researchers to increase their analytical skills has never been higher. As such, we are delighted to announce a pilot virtual workshop running from May 4-8, 2020!
Here at the Childhood Cancer Data Lab, we value transparency and the practice of open science. Much of the work we’ve done and the products that we build hinge on the generosity and openness of other scientists. In this post, as part of National Brain Tumor Awareness month, we want to talk about a project that our science team has been working on over the last few months (and to do so in a way that aligns with our values).
The workshop will take place on June 22 - 26, 2020 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
When the CCDL (along with everyone else) realized that we would have to conduct our bioinformatics training workshops remotely, we had to make some quick decisions about how we were going to do it. Most of the instructional materials for our in person workshops were already online, so we knew we had a good base to work from. We just needed to figure how to adapt the live instruction.
At Alex’s Lemonade Stand Foundation’s Childhood Cancer Data Lab, we’re excited to be helping out with an upcoming event hosted by the Children’s Tumor Foundation. If you participate, you may meet members of our team who are mentoring and judging.
The workshop will take place on March 22 - 26, 2021 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation
The workshop will take place on June 28- July 2, 2021 from noon to 5pm eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
Hack4Rare is a virtual event that calls for healthcare startups, developers, solutions architects, and hackathon enthusiasts to join researchers, clinicians and patients in developing solutions built around a number of rare diseases including neurofibromatosis, PTEN Hamartoma Tumor Syndrome, RASopathies and Desmoid Tumors.
The workshop will take place on September 20 - 24, 2021 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
Introducing refine.bio examples. Here, users can access a variety of example analyses implemented in R, such as clustering and heat maps, differential expression analysis, and pathway analysis, for use with refine.bio data.
The workshop will take place on November 1-5, 2021 from noon to 5pm eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with our staff available for consultation.
I work at the Childhood Cancer Data Lab, where we use very big data to find cures for childhood cancers. To move data around the internet at very high speeds, we are forced to use a proprietary software suite called Aspera. If somebody could make a Free Software alternative, the future of the internet would be way more awesome! Best of all, you can be the one to do it!
When my daughter Alex was diagnosed with cancer and throughout her battle, we saw how our community of people rallied around our family. No one knew quite how to help, but they were willing to do whatever was needed to ease the burden we faced.
'Work smarter not harder’ is useless advice if you don’t know how to ‘work smarter’. But the Childhood Cancer Data Lab's work and processes may be the smartest I’ve ever had the pleasure of learning and adopting.
The Data Lab will hold our first virtual workshop of the year from March 14-18, 2022!In this workshop, we will introduce researchers studying pediatric cancer to the R programming language, the Tidyverse R packages for data science, single-cell RNA-seq data analysis, and pathway analyses.
At the Data Lab, we are big proponents of automating the boring stuff so we can spend more time thinking about the fun stuff. But how exactly do we do that, and what does it mean to automate the boring stuff?
November marked the final Childhood Cancer Data Lab training workshop for 2021. We held four week-long virtual workshops this year, teaching 88 researchers the data science skills they need to examine their own data.
Before working as a Data Scientist at the Childhood Cancer Data Lab, I spent time in my PhD and post-doctoral fellowship in two very different research environments. Each had their own unique way of doing research. I found that some things worked really well and others were not as successful.
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