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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.

Dr. Wang will present virtually. Register now and join us via Webex.

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.

Speaker:

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 National Cancer Institute’s (NCI) Center for Biomedical Informatics and Information Technology (CBIIT) Data Science Seminar Series presents talks from innovators in the data science and cancer research communities.
To view upcoming speakers or view recordings for past presentations, visit Data Science Seminar Series. Follow the conversation @NCIDataSci and #DataSciSeminar. For more information about NCI’s Center for Biomedical Informatics and Information Technology (CBIIT) visit: datascience.cancer.gov

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.

Registration: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=ec198bc69667ba131397fb48e0c9b9708

Presenter: Christian Zinser PhD Head of Bioinformativs at Precigen Bioinformatics Germany
Description: This presentation will give you an overview of the Genomatix Genome Analyzer (GGA) functionalities and data background.

The GGA is Genomatix’s integrated solution for the analysis of Next Generation Sequencing (NGS) data, gene regulation, and pathway analysis. It includes a comprehensive genome annotation and data visualization, accessible in an intuitive web-based GUI.

The biological background data consisting of annotation and gene network data provided by ElDorado plus the transcription factor knowledge contained in MatBase lets researchers analyze and interpret their experimental results in a unique biological context for 26 different species. Differential expression analysis, gene network and pathway generation, regulatory frameworks, literature analysis and binding site motif definition are only a few of the tasks that can be performed.

POC: Daoud Meerzaman

Registration: https://cbiit.webex.com/cbiit/onstage/g.php?MTID=e51f19be5660107b4b92118dc48be1781

Presenter: Kevin Kendal PhD CEO of MacVector

Description: This workshop will focus on the analysis of Next Generation Sequencing data. It will cover alignment/assembly of NGS data to one or more reference sequences for (e.g.) RNA expression analysis, SNP detection and/or sequence confirmation as well as de novo assembly of short read (Illumina or IonTorrent) and/or long read (Sanger, PacBio or Oxford Nanopore) data for both modest sequences, like plasmids, and for entire genomes. We will also look at how you can use MacVector to identify and extract subsets of paired-end reads from large datasets so that you can focus on just those of interest to your project.

POC: Daoud Meerzaman