BTEP Seminar Series

This session of the BTEP Coding Club introduces NIH CloudLab, a free 90-day sandbox environment partnered with AWS, Google Cloud, and Azure, and demonstrates how to run Nextflow pipelines, a powerful workflow management system for bioinformatics and data science, on Google Cloud Batch.

The Research to Accelerate Cures and Equity (RACE) for Children Act of 2017 requires companies developing targeted cancer drugs for adults to evaluate those drugs for applicability to pediatric cancer. The NCI-funded Pediatric Preclinical In Vivo Testing (PIVOT) consortium collaborates with industry partners to perform rigorous preclinical testing of novel targeted agents using in vivo models of common pediatric cancers.  As pediatric cancer is rare, assembling sufficient numbers of patients for clinical trials is challenging. It highlights the importance of effective preclinical testing for identifying promising agents and agents with low potential for improving treatment options for children with cancer.

ggplot2 is a popular R package for data visualization that uses layers to build high quality plots. There are over 100 packages that extend the functionality of ggplot2. This session of the BTEP Coding Club will focus on the package ggpubr, which facilitates plot customization and statistical integration, making it much easier to create publication ready plots with ggplot2. Specifically, this lesson will demonstrate how to visualize the results of common statistical tests (e.g., t-tests, ANOVA, Pearson correlation).  

Meeting Information:

https://cbiit.webex.com/cbiit/j.php?MTID=md5545c8b063ac2e0996ac7390c1ffc65
Wednesday, November 20, 2024 11:00 AM | 1 hour | (UTC-04:00) Eastern Time (US & Canada)
Meeting number: 2310 921 8299
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The accessibility of artificial intelligence/machine learning (AI/ML) tools has taken off in recent years. This democratization of advanced analytics has the potential to revolutionize predictive toxicology, especially for applications that generate massive, multimodal data. Realizing this promise will require tools tuned to learn from trusted sources that can evolve as new data emerge. This talk will describe such efforts at NIEHS using data that range in scale from lab-based behavioral experiments to epidemiological-scale geospatial data.

Dr. Blackshaw's work investigates the molecular mechanisms controlling neurogenesis and cell fate specification in the vertebrate forebrain, with a particular focus on the retina. He currently focuses on the use of comparative Single-Cell Multiomic Analysis to identify gene regulatory networks that control retinal development and injury-induced regeneration. He will describe recent work that has used insights from both studying both development and injury-induced neurogenesis in zebrafish to induce glia in mammalian retina to generate neurons.

In this session of the BTEP Coding Club, Emily Clough, PhD, GEO Curator, will explore updates to analysis tools available within the Gene Expression Omnibus (GEO), a public repository for gene expression and epigenomics data sets. In the past several years GEO has made major updates and additions to the online analysis tool GEO2R. Many new visualization plots have been added to explore results, and now human RNA-seq data are available for analysis.

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2318 999 6974

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Recent advances in artificial intelligence (AI) have revolutionized the use of hematoxylin and eosin (H&E)-stained tumor slides for precision oncology, enabling data-driven approaches to predict molecular characteristics and therapeutic outcomes. In my talk, I will present ENLIGHT–DeepPT, a novel two-step AI framework. The first step, DeepPT, leverages deep learning to predict genome-wide tumor mRNA expression from H&E slides. The second step, ENLIGHT, utilizes these inferred expression values to predict patient response to targeted and immune therapies. We validate this framework across 16 cohorts from The Cancer Genome Atlas (TCGA) and independent datasets, demonstrating successful prediction of true responders in five patient cohorts spanning six cancer types, with a 39.5% increased response rate and an odds ratio of 2.28.

In addition, I will introduce DEPLOY, a deep learning model designed to enhance the diagnosis of central nervous system (CNS) tumors by predicting tumor categories from histopathology slides. DEPLOY integrates three components: a direct classifier based on histopathology images, an indirect model that predicts DNA methylation profiles for tumor classification, and a model that uses patient demographics. Trained on a dataset of 1,796 patients and tested on independent cohorts of 2,156 patients, DEPLOY achieves 95% overall accuracy and 91% balanced accuracy. These results underscore the potential of DEPLOY to assist pathologists in classifying CNS tumors rapidly, offering a promising tool for improving diagnostic precision in clinical settings.

Digital Pathology has advanced significantly in the past decade, evolving beyond assisting pathologists to now informing molecular and genetic properties of tumors. Recent breakthroughs in machine learning and AI, particularly in big data and image analysis, have ushered in a new era of capabilities. This talk will present these advancements and demonstrate how our lab is developing tools to apply them, focusing on improving precision diagnostics and treatment of pediatric tumors. We'll conclude by exploring applications of these techniques to analyze and understand reproductive aging, showcasing the broad potential of next-generation digital pathology in medical research.

Dr. Turkbey will discuss radiology, pathology, and multimodal AI models his NCI lab has developed for prostate cancer diagnosis and prognosis prediction.

Telomere to telomere (T2T) genome assemblies represent a paradigm shift in comparative genomics, offering insights into chromosome structure, evolution, and function at the highest resolution. Dr. O'Neill's lab has made recent efforts employing long-read based genome assembly, coupled with epigenetic, functional and repeat analyses, which have afforded the opportunity to delineate key elements participant in centromere function and chromosome rearrangement. Using a comparative approach and long-read, gapless genome assemblies, their studies provide insight into the diversity, distribution, and evolution of repetitive regions that shape chromosome structure and evolution in human and in species groups experiencing rapid karyotypic change. 

Dr. Mardis is an internationally recognized expert in cancer genomics, with ongoing interests in the integrated characterization of cancer genomes, defining DNA-based somatic and germline interactions and RNA-based pathways, and immune microenvironments that lead to cancer onset and progression, specifically involving pediatric cancers. Most recently, her research has been oriented toward translational aspects of cancer genomics, specifically identifying how the cancer genome changes with treatment, including acquired resistance, the use of genomics in understanding immune therapy response, and the clinical benefit of cancer molecular profiling in the pediatric setting.

The Elemento lab combines Big Data analytics with experimentation to develop entirely new ways to help prevent, diagnose, understand, treat and ultimately cure disease. Our research involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning. We’re revolutionizing healthcare by developing innovative approaches to better predict, diagnose, treat, and prevent disease to improve clinical care for every patient.  

Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is hosted on Biowulf, the NIH high performance computing system and suitable for those with little command line knowledge to conduct analyses through a point-and-click interface utilizing Biowulf’s immense compute power, rather than a personal computer that may not have the power for analyzing large genomic datasets. This Coding Club helps scientists with no or limited experience get started using Partek Flow. Participants will learn to acquire access to, transfer data to, and import data into projects on the NIH Partek Flow server. A Partek Flow account is not required for participation.

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The Cancer Genome Atlas (TCGA) was a landmark cancer genomics program that molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types. This resulted in a massive open-source dataset that continues to uncover revelations regarding the molecular underpinnings of various cancers. This BTEP Coding Club session demonstrates how to access and download TCGA data from the Genomic Data Commons (GDC).  Other means of accessing, analyzing, and downloading TCGA data will also be discussed.  

The Brooks Lab developed a computational tool called FLAIR (Full-Length Alternative Isoform Analysis of RNA) to produce confident transcript isoforms from long-read RNA-seq data with the aim of alternative isoform detection and quantification. With an increase in the usage of long-read RNA-seq, there is a growing need for a systematic evaluation of this approach. We are part of an international community effort called the Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) to perform such an evaluation. The Brooks Lab is extending FLAIR to incorporate sequence variation, RNA editing, and RNA modification in isoform detection as well as detection of complex gene fusions from long-read sequencing data.

This talk will cover a scRNA-seq workflow available to NCI researchers on NIDAP. NIDAP, the NIH Integrated Data Analysis Platform, is a cloud-based and collaborative data aggregation and analysis platform that hosts user-friendly bioinformatics workflows. This platform allows researchers to use many of the open-source tools discussed in this seminar series without necessitating coding experience. This seminar will focus on the capabilities of this workflow for QC, annotation, and visualization of single-cell RNA-seq data.

An exciting opportunity at the intersection of the biomedical sciences and machine learning stems from the growing availability of large-scale multi-modal data (imaging-based and sequencing-based, observational and perturbational, at the single-cell level, tissue-level, and organism-level). Traditional representation learning methods, although often highly successful in predictive tasks, do not generally elucidate underlying causal mechanisms. Dr. Uhler will present initial ideas towards building a statistical and computational framework for causal representation learning and its applications towards identifying novel disease biomarkers as well as inferring gene regulation in health and disease.

Versioning enables researchers to track changes in coding projects. This Coding Club introduces Git (https://git-scm.com), an open-source software used to perform versioning on a personal computer. At the end of this class, participants will:

• Understand the importance of versioning
• Describe Git
• Know how to access Git
  • Be aware of resources that helps with Git installation on personal computer
  • Be aware of the availability of Git on Biowulf, the NIH high performance computing system
• Define repository
• Know the steps involved in the versioning process including
  • Initiating a new repository
  • Understanding the difference between tracked and untracked files
  • Excluding files from being tracked
  • Staging files with changes
  • Commiting changes and writing commit messages
  • Viewing commit logs
• Compare between versions of code
• Revert to a previous version of code

This seminar provides an overview of differential expression testing with Seurat. Topics to be covered include preparing data for differential gene expression, differential gene analysis between specific groups, differential gene analysis for cluster classification, SingleR for cell type annotation, and visualizing genes of interest.  

This lesson reviews many of the standard steps in a scRNA-Seq workflow: QC filtering, normalization, scaling, and clustering. 

This seminar provides an introduction to R in the context of single cell RNA-Seq analysis with Seurat.  In this seminar, attendees will learn about options for analyzing scRNA-Seq data, resources for learning R, how to import scRNA-Seq data, and how to create, examine, and access data stored in a Seurat object.  

Kimia Dadkhah, bioinformatics analyst (SCAF), will talk about Cell Ranger output and essential quality control metrics in single cell data analysis, how to interpret these and make informed decisions, and other considerations to keep in mind when assessing the quality of returned data from SCAF.  

Single cell RNA sequencing (scRNA-Seq) is becoming increasingly more common in biomedical research, but what is scRNA-Seq? How does it differ from other transcriptomic approaches (e.g., bulk RNA-Seq), and what are the potential applications, technologies, and workflows? This presentation will introduce learners to scRNA-Seq, answering the above and touching on additional topics such as methodological challenges, concerns, and best practices.  

Although generative artificial intelligence (AI), a’la ChatGPT, is receiving a lot of “attention” these days, there are many other options for using AI to support biomedical research.  In order to help analyze and interpret single cell genomics data, we have found that AI approaches that retain “explainability” are especially useful in providing functional insights into the underlying biological systems being studied, in this case, the human brain.

The Single Cell Analysis Facility (SCAF) is a CCR facility dedicated to the application of single-cell technologies in cancer research. Based on the NIH Bethesda main campus, SCAF aims to provide the broadest range of project support from consultation on experimental design, sequencing, and data analysis. Learn more about SCAF and the single-cell genomics technologies available to CCR investigators in this overview presentation. 

DAVID (the Database for Annotation, Visualization and Integrated Discovery) provides a comprehensive set of functional annotation tools for investigators to understand the biological meaning behind large lists of genes acquired from high-throughput assays such as RNA-Seq, Proteomics, Microarray, etc.  In this BTEP Coding Club session, developers from DAVID (Weizhong Chang and Brad Sherman) will give an overview of DAVID and provide training on key tools including functional annotation tools (table, chart, and clustering), gene functional classification, gene ID conversion, gene name batch viewer, and the newly developed ortholog conversion tool.

Artificial Intelligence (AI) is becoming increasingly ubiquitous in biomedical research, enabled by large datasets, new algorithms, and hardware improvements. In this session, Dr. Brian Ondov will introduce the basic principles of AI and describe how its various forms can help researchers in different ways, including image classification, sequence-based prediction, generative models, and language understanding. 

Versioning enables researchers to track changes in coding projects. This Coding Club session will introduce Git (https://git-scm.com), an open-source software used to perform versioning locally and enables users to upload code to web repositories such as GitHub. At the end of this class, participants will

• Be able to describe Git
• Be able to use Git to
  • Create coding projects 
  • Save and track changes to code
  • Upload code to GitHub
  • Revert to/view previous versions of code
  • Perform basic collaboration tasks

Installation of software is not needed to participate.

Meeting information:

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=meadb08ed71552393fe486073a7a7ffc5 
Meeting number:
2308 646 3414
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Versioning enables researchers to track changes in coding projects. This Coding Club session will introduce the versioning tool GitHub (https://github.com). At the end of this class, participants will

• Become familiar with options available for using GitHub at NCI
• Be able to use GitHub to
  • Create coding projects 
  • Track changes in code
  • Revert to a previous version of code
  • Collaborate with the project team

Installation of software is not needed to participate.

This class will be followed by one addressing versioning using Git on February 28, 2024 from 11 AM to 12 PM. See https://bioinformatics.ccr.cancer.gov/btep/classes/version-control-using-git for information and registration.

Meeting information:

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=meadb08ed71552393fe486073a7a7ffc5 
Meeting number:
2308 646 3414
Password:
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Documenting your data analysis is a crucial step toward making your research reproducible. In this session of the BTEP Coding Club, we will learn how to get started using Quarto with RStudio for report generation. 

This session introduces two versions of the R/ Bioconductor package OmicCircos to generate high-quality circular plots for visualizing multi-dimensional omics data:

1. coding in the R environment for programmers;
2. point-and-click OmicCircos R Shiny app on the Cancer Genomics Cloud (CGC) for non-programmers. 

Meeting number:2310 050 3184

Password:3sfNDMBq*66

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In this seminar, Dr. Furlan will share data using single cell genomic technologies after hematopoietic cell transplantation including the molecular approaches and computational tools they have used and developed as they relate to this field.

This October session of the BTEP Coding club will feature a tutorial on how to access data from GEO as well as how to submit data to GEO. 

There is an urgent need to take what we have learned in our new data-driven era of medicine, and use it to create a new system of precision medicine, delivering the best, safest, cost-effective preventative or therapeutic intervention at the right time, for the right patients.  Dr. Butte's teams at the University of California build and apply tools that convert trillions of points of molecular, clinical, and epidemiological data -- measured by researchers and clinicians over the past decade and now commonly termed “big data” -- into diagnostics, therapeutics, and new insights into disease.  Dr. Butte, a computer scientist and pediatrician, will highlight his center’s recent work on integrating electronic health records data from over 8 million patients across the entire University of California, and how analytics on this “real world data” can lead to new evidence for drug efficacy, new savings from better medication choices, and new methods to teach intelligence – real and artificial – to more precisely practice medicine.

The Trapnell Lab at the University of Washington's Department of Genome Sciences studies how genomes encode the program of vertebrate development and how that program goes awry in disease. We build new tools, technologies, and software for decoding this program from large-scale single-cell experiments.

This session of the BTEP Coding Club will focus on the tool rMATS for differential alternative splicing event detection from RNA-Seq data. This 1-hour demo will provide a detailed overview of rMATS including why you may want to use it, how to use it, and how to interpret and further use resulting outputs. 

https://rnaseq-mats.sourceforge.io/

Multivariate Analysis of Transcript Splicing (MATS)

MATS is a computational tool to detect differential alternative splicing events from RNA-Seq data. The statistical model of MATS calculates the P-value and false discovery rate that the difference in the isoform ratio of a gene between two conditions exceeds a given user-defined threshold. From the RNA-Seq data, MATS can automatically detect and analyze alternative splicing events corresponding to all major types of alternative splicing patterns. MATS handles replicate RNA-Seq data from both paired and unpaired study design.

While there is a positive correlation between cancer and aging, the mechanisms underlying this relationship remain unclear. Clonal hematopoiesis, a benign condition that is both associated with aging and predisposes to increased risk of development of blood cancers, presents an opportunity to understand the connection between cancer and aging. This seminar will discuss emerging discoveries of mechanisms acting within the hematopoietic stem cells as well as alterations in the bone marrow microenvironment that promote clonal hematopoiesis and transformation to blood cancers.

Heatmaps and volcano plots are common data visualizations in bioinformatic analyses of genomic data, such as bulk RNA-seq. While both plot types can be used to visualize gene expression, heatmaps can be used to examine expression data across samples, and in combination with clustering techniques, reveal potential patterns in the data. Volcano plots demonstrate the direction, distribution, and statistical significance of gene expression between experimental conditions (example tumor vs. non-tumor, or drug treated vs. non-treated). In this coding club, we will demonstrate how to construct these plots using the R/Bioconductor tools ComplexHeatmap and EnhancedVolcano.

This session of the BTEP Coding Club will focus on strategies for overcoming errors, warnings, and other common problems with R code. In this 1-hour tutorial targeting beginner R users, we will discuss commonly observed errors, how to find help, and how to approach and debug R code. 

The Bhattacharya Lab at the UCSF Parnassus Campus is focused on the functional role of monocyte-derived macrophages in the onset and persistence of fibrosis in the lung. We are addressing the following major questions, with a goal of discovering new targets for therapy for acute lung injury and fibrosis:

• What molecules released by monocyte-derived macrophages and other immune cells signal to and activate pro-fibrotic programs in parenchymal cell types such as fibroblasts and epithelial cells?
• What reciprocal signals derive from these parenchymal cells to modify the immune response?
• How can this pathologic crosstalk be reversed to combat fibrosis and restore lung health?

Biowulf is the high-performance computing cluster (HPC) at NIH. In addition to its vast compute power, Biowulf has hundreds of bioinformatics tools and databases for analyzing Next Generation Sequencing (NGS) data. This coding club will provide participants the foundations for harnessing Biowulf’s computing power to analyze NGS data. Participants will learn to request computing resources on and to submit scripts to the Biowulf system. This class is not hands-on so no need to obtain a Biowulf account prior to attending.

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=m39e6aa973e1500fbac8d3516e23cfaf8

Meeting number:
2317 419 7733
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CellTypist was first developed as a platform for exploring tissue adaptation of cell types using scRNA-seq semi-automatic annotations. Now it's an open source tool for automated cell type annotations as well as a working group in charge of curating models and ontologies.

Single-cell technologies, such as single-cell RNA sequencing (scRNA-seq), have increased the resolution achieved in the study of cellular phenotypes, allowing measurements of thousands of different genes in thousands of individual cells. This has created an opportunity to begin understanding the dynamics of the prime biological processes undergone by cells, while requiring unique computational tools. In our lab, we develop novel and innovative computational methods for single-cell data analysis. - Theis Lab

Functional enrichment analysis is used to understand the biological context of gene lists or differential expression results. There are a multitude of tools available for this purpose. clusterProfiler is a popular R / Bioconductor package supporting over-representation analysis (ORA) and gene set enrichment analysis (GSEA) using up-to-date biological knowledge of genes and biological processes (GO and KEGG) and support for thousands of organisms. The latest version of clusterProfiler (v. 4.6.2) also provides a tidy interface for visualizing resulting output.

This May 2023 session of the BTEP Coding Club will provide an overview and demo of many of the key features of the clusterProfiler R package. 

This BTEP coding club will introduce beginners to Jupyter Notebook, a platform to organize code and analysis steps in one place. Jupyter Notebook can be easily installed or run in a web browser, and supports several languages such as R and Python. It provides a way to keep track of all steps in an analysis and a place for collaboration. Come learn what Jupyter Notebook can do for you. This class will not be hands-on so need to install anything to attend.

Azimuth is a web application that uses an annotated reference dataset to automate the processing, analysis, and interpretation of a new single-cell RNA-seq experiment. Azimuth leverages a 'reference-based mapping' pipeline that inputs a counts matrix of gene expression in single cells, and performs normalization, visualization, cell annotation, and differential expression (biomarker discovery). All results can be explored within the app, and easily downloaded for additional downstream analysis. - Satija Lab

The development of Azimuth is led by the New York Genome Center Mapping Component as part of the NIH Human Biomolecular Atlas Project (HuBMAP).

This webinar will be recorded and made available on the BTEP web site: https://bioinformatics.ccr.cancer.gov/btep/btep-video-archive-of-past-classes/ within 48 hours after the event ends. 

AI Models of Cancer and Precision Medicine: Building a Mind for Cancer

The long-term objective of the Ideker Lab is to create artificially intelligent, mechanistic models of cancer and neurodegenerative diseases for translation of patient data to precision diagnosis and treatment. We seek to advance this goal by addressing fundamental questions in the field: What are the genetic and molecular networks that promote disease, and how do we best chart these? How do we use knowledge of these networks in intelligent systems for predicting the effects of genotype on phenotype? – Ideker Lab, https://idekerlab.ucsd.edu/research/cancer/

This webinar will be recorded and made available on the BTEP web site: https://bioinformatics.ccr.cancer.gov/btep/btep-video-archive-of-past-classes/ within 48 hours after the event ends. 

Do you use excel's VLOOKUP function often to merge tables or search for subsets of data in large NGS data files? If so, you may be interested in a more programmatic solution. Join us for a lesson on performing VLOOKUP in excel followed by a more reproducible solution with R programming. Whether you are interested in merging a list of gene ids with a table of functional annotations or searching for unique matches of known T-Cell Receptor sequences among output from a 10X TCR sequencing run, this tutorial will likely be useful to you.  

This tutorial will kick off the BTEP Coding Club, which features monthly 1-hour tutorials of bioinformatics tools, software, or skills. Email us at ncibtep@nih.gov if you would like to see a topic featured by the BTEP Coding Club. 

The avalanche of easy-to-create genomics data has impacted almost all areas of medicine and science, from cancer patients and microbial diagnostics to molecular monitoring for astronauts in space. In this lecture, new discoveries from RNA- and DNA-sequencing with the FDA’s SEQC study show the ability of single-molecule methods to reveal rare alleles and provide more comprehensive epigenomics maps of patients and cancers. Also, recent technologies and algorithms from our laboratory and others demonstrate that an integrative, cross-kingdom view of patients (precision metagenomics) holds unprecedented biomedical potential to discern risk, improve diagnostic accuracy, and to map both genetic and epigenetic states, as well as clonal changes in mutations with clonal hematopoiesis. Finally, these methods and molecular tools work together to guide comprehensive, longitudinal, multi-omic views of human astronaut physiology and biology in the NASA Twins Study and several other missions with SpaceX and Axiom, which lay the foundation for future, long-duration spaceflight, including sequencing, quantifying, and engineering genomes to survive on other planets over the next 500 years (https://mitpress.mit.edu/books/next-500-years).

The efforts of our laboratory are split evenly between experimental and computational biology.  We develop new experimental methods to sequence single cells and isolate rare subpopulations and develop new analytical approaches to detect variants and apply statistical methods to these data sets.  We focus mainly on breast cancer to understand the role of clonal diversity in the evolution of invasion, metastasis and response to chemotherapy.  We are also using these tools to study rare tumor cell subpopulations including circulating tumor cells and cancer stem cells.  Our goal is to understand the role of clonal diversity in tumor evolution so that we can exploit this diversity for therapeutic vulnerabilities and improve diagnostic tools and the early detection of cancer.  We fully expect that applying these tools to human patients will lead to reduced morbidity in breast cancer.

Sarah Teichmann is co-founder and principal leader of the Human Cell Atlas (HCA) international consortium. The International Human Cell Atlas initiative aims to create comprehensive reference maps of all human cells to further understand health and disease.

The 37 trillion cells of the human body have a remarkable array of specialized functions, and must cooperate and collaborate in time and space to construct a functioning human. In this talk I will describe my lab’s efforts to understand this cellular diversity through a programme of cell atlasing. Harnessing cutting edge single cell genomics, imaging and computational technologies, we investigate development, homeostasis and disease states, at scale and in 3D, with a particular focus on immunity. I will illustrate the relevance of cell atlas-ing for engineering organoids and regenerative medicine, and will share new results providing insights into pacemaker cells from the sinoatrial node of this heart. Overall I hope to illustrate the power of single cell approaches in unlocking fundamental knowledge about the human body.

Making data reusable for discovery and shared analytics across domains is a laborious, specific-skill requiring task that most data providers do not have the resources, expertise, or perspective to perform. Equally challenged are the data re-users, who function in a landscape of bespoke schemas, formats, and coding – when they can get past understanding the licensing and access control issues. Making the most of our collective data requires partnerships between basic researchers, clinicians, patients, and informaticians, as well as sophisticated strategies to address a myriad of interoperability issues. This talk will review different communities endeavors towards these ends from across the translational spectrum.

Our goal is to understand how cellular heterogeneity encodes the molecular structure, function, and regulation of complex biological systems. Primarily using single cell genomics, we analyze systems by profiling their most fundamental units individually – a ‘bottom-up’ approach that allows us to study how diverse groups of cells work together to drive biological processes and behaviors. – Satija Lab

Since Dr. Satija will be presenting unpublished work in this webinar, it will not be recorded or distributed.