Topic: Data Science

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Researchers continue to face major challenges when comparing diverse single-cell RNA sequencing (scRNA-seq) data sets, because these data often are generated by different technologies from a variety of laboratories.

In this webinar, Dr. Charles Wang will address the need for guidelines to help choose algorithms for more accurate biological interpretations of varied data types acquired by different platforms.

Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, Dr. Wang compared different scRNA-seq platforms and several methods (preprocessing, normalization, and batch-effect correction) at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq data set characteristics (e.g., sample and cellular heterogeneity, the platform used, etc.) were critical in determining the optimal bioinformatics method. However, reproducibility across centers and platforms was high when appropriate bioinformatics methods were applied.

These findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

Presenter:

Charles Wang, M.D., Ph.D., M.P.H.
Dr. Charles Wang is a professor at the Loma Linda University School of Medicine and director of the Center for Genomics. Dr. Wang was the director of Clinical Transcriptional Genomics Core at Cedars-Sinai Medical Center; associate professor of medicine at the David Geffen School of Medicine at the University of California-Los Angeles; and director of the Functional Genomics Core at City of Hope. He is the recipient of several awards, including the American Association for Cancer Research–Bristol-Myers Squibb Young Investigator Award.

About the Data Science Seminar Series:

The CBIIT Data Science Seminar Series presents talks from innovators in the research and informatics community. Follow the conversation on Twitter with @NCIDataSci and #DataSciSeminar.

To see upcoming speakers or view recordings from past presentations, visit the CBIIT Data Science Seminar Series website.

Individuals with disabilities who need sign language interpreters and/or reasonable accommodation to participate in this event should contact Eve Shalley (240-276-5194, eve.shalley@nih.gov), and/or the Federal TTY Relay number (1-800-877-8339) at least 5 days in advance of the event.

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Faculty: Eliezer M. Van Allen, MD – Harvard University/Dana-Farber Cancer Institute/BROAD; NCI Cancer Moonshot IOTN

Moderator: Kellie N. Smith, PhD – Johns Hopkins School of Medicine

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

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Faculty:  Shannon McWeeney, PhD – Oregon Health & Science University; NCI Cancer Moonshot DRSN

Moderator:  Santosh Putta, PhD

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

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Faculty: Dana Pe’er, PhD – Memorial Sloan Kettering Cancer Center; NCI Cancer Moonshot HTAN
Moderator: Daniel Wells, PhD – Immunai

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

Register Now

Faculty: Michael Angelo, MD, PhD – Stanford University, Moonshot Cancer Atlas

Moderator: Carsten Krieg, PhD – Medical University of South Carolina

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

Register Now

Faculty: Evan Newell, PhD – Fred Hutchinson Cancer Institute

Moderator: Carsten Krieg, PhD – Medical University of South Carolina

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member