Workshop: Mapping of Cell Types Data

Frameworks for Brain Cell Type Definition, Ontology, and Nomenclature Workshop: Mapping of Cell Type Data

In this virtual workshop, we will explore best practices for mapping single cell data to reference cell atlases. Mapping single cell data to reference cell atlases is complicated by batch effects between datasets, systematic differences in biological conditions of the samples, limited availability of computational resources, and sharing restrictions on raw data. While our focus is on transcriptomically-defined cell types of the brain, applications in other tissues and in the use of other data modalities will also be considered.

Thursday, May 12, 2022
8am-2pm Pacific Time

Registration: general attendee

General attendees are invited to listen to the full workshop and pose questions to the speakers. Registration is free.

Data challenge registration closed May 2.

Defining and naming cell types is a key problem of contemporary neuroscience. While the problem has preoccupied neuroscientists for over a century, it now has significantly increased attention due to our ability to collect cellular level data in a high-throughput manner. International consortia involved in molecular brain cell classification, including the BRAIN Initiative Cell Census Network (BICCN), the Human Cell Atlas (HCA), and the Human Biomolecular Atlas Program (HuBMAP) are generating and classifying cell types in all organ systems in the human body at a rapid pace. Standards for classification, naming, and data mapping are essential to form a common language of cell types to make sense of these enormous datasets, and to promote understanding between multiple domains, projects, and scientists.

Essential to our understanding of cell types and their functions is to catalog measured transcriptomes of cells belonging to each cell type, and to present this information in the form of comprehensive reference single cell atlases. Much like how reference genomes provide a map for locating the origin of sequencing reads, reference cell atlases can be used to map query cells to potential cell types in the reference atlas in order to rapidly characterize and compare relevant cell phenotypes. Reference atlases ultimately help quantify transcriptional heterogeneity that arises as a result of natural variation, aging, environmental influences, and disease. Large single-cell atlases comprising millions of cells across tissues, organs, donors, developmental stages, and conditions are now being generated by consortia such as the Human Cell Atlas and BICCN to serve as references for smaller-scale studies.

The workshop will include presentations about key state-of-the-art methods and techniques, and a comparison of results obtained by different methods in a hands-on data mapping challenge. To explore these mapping techniques, workshop participants will be provided with data mapping “challenges” to be completed before the workshop. Creative solutions to these challenges will have the opportunity to present to a national audience at the June 6, 2022, BRAIN Initiative Cell Census Network (BICCN) consortium meeting and participate in a published article detailing the challenge results.

Agenda     Data challenges     Organizers

Agenda

Workshop agenda is subject to change.

Session

Speakers

Time: Eastern

Time: Pacific

Zoom webinar opens to participants

 

10:55am

7:55am

Welcome and housekeeping

Workshop organizers

11:00-11:05am

8:00-8:05am

Introduction, workshop goals, and overview of data challenges

Mike Hawrylycz with Jesse Gillis, Gerald Quon, Richard Scheuermann, and Uygar Sümbül

11:05-11:20am

8:05-8:20am

Mapping cell types in a biological context

Ed Lein, Allen Institute for Brain Science

11:20-11:30am

8:20-8:30am

Invited talk: Geometric and Topological methods for dissection of cellular heterogeneity

Smita Krishaswamy, Yale School of Medicine

11:30am-12:00pm

8:30-9:00am

Data Challenge 1: Presentations and discussion

Data challenge teams and workshop organizers

12:00-12:45pm

9:00-9:45am

Invited talk: Data Integration and Transfer Learning for Merging Single Cell Transcriptomics Data Kathryn Roeder, Carnegie Mellon University 12:45-1:15pm 9:45-10:15am

Break

 

1:15-1:45pm

10:15-10:45am

Data Challenge 2: Presentations and discussion

Data challenge teams and workshop organizers

1:45-2:30pm

10:45-11:30am

Invited talk: Cell type homologies in the mammalian brain

Trygve Bakken, Allen Institute for Brain Science

2:30-3:00pm

11:30am-12:00pm

Invited talk: Iterative single-cell multi-omic integration using online learning

Joshua D. Welch, University of Michigan

3:00-3:30pm

12:00-12:30pm

Cell type data mapping: Strategic discussion

Workshop organizers, invited speakers, and data challenge participants

3:30-4:30pm

12:30-1:30pm

Closing remarks

Richard Scheuermann and Mike Hawrylycz

4:30-5:00pm 1:30-2:00pm

Data challenges

A key part of this workshop will be transcriptomic data mapping comparison of state-of-the-art approaches. We have prepared query and reference data sets based on a recent survey of brain-derived cell types and a BARseq2 dataset that jointly measures transcriptomic signatures of marker genes and spatial locations of neurons across a whole mouse cortex. Participants are asked to match cell types between the query and reference datasets and to identify cell types based on the expression data alone in the BARseq2 dataset and the cell types will then be assessed through their spatial patterns in the held-out physical locations. Participants may choose to approach Challenge 1, Challenge 2, or both. Data Challenge registration closed May 2.

Data challenge details

During the workshop:

  • Present and benchmark contemporary algorithms for accurately mapping single cell transcriptomic data to standard cell atlases and taxonomies.

  • Discuss approaches for intra-platform and cross-platform single cell transcriptomic data normalization.

  • Discuss the challenges with producing optimal cluster partitioning.

  • Review methods for multimodal association of single cell data including morphology and electrophysiology data.

  • Review methods for mapping spatial transcriptomics data to reference cell types defined from sc/snRNA-seq data.

  • Evaluate existing data mapping algorithms supplied by BICCN consortium members on various new datasets; plan a roadmap for development of improved data mapping algorithms and infrastructure.

  • Discuss results and best practices from the data atlas mapping challenge on key data sets from sc/snRNA-seq and spatial transcriptomics data.

Workshop organizers

Workshop support

  • NIH BRAIN Initiative grant, RFA-MH-19-146, A Community Framework for Data-driven Brain Transcriptomic Cell Type Definition, Ontology, and Nomenclature, 1RF1MH123220-01

  • NIH BRAIN Initiative Brain Cell Data Center (BCDC), RFA-MH-17-215, A Community Resource for Single Cell Data in the Brain, 1U24MH114827-01

  • Allen Institute for Brain Science

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