Attendees will learn how to use Partek Flow to identify cell subtypes using both gene and protein expression in a peripheral blood mononuclear cell (PBMC) sample using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) technology. CITE-Seq is a multimodal single cell phenotyping method. CITE-Seq data analysis starts with importing a count matrix file, followed by QC and filtering data. Clustering analysis and dimension reduction techniques are then used to visualize and identify subtypes of cells. Differential expression detection among different subtypes will be demonstrated, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Basic knowledge of Partek Flow is required.

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Learn about data science education resources available through NIH and NCI, as well as online resources to learn about the data lifecycle, data management, data analysis and visualization, and other data science topics.
For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov
Check the Research Data Sources page on the Scientific Library website to learn more about research data online resources.

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Presenter:
Christina Curtis, Ph.D., M.Sc.
Dr. Christina Curtis is an Associate Professor in the Departments of Medicine (Oncology) and Genetics in the School of Medicine at Stanford University where she leads the Cancer Computational and Systems Biology Group and is Co-Director of the Molecular Tumor Board at the Stanford Cancer Institute. The Curtis laboratory is focused on the development and application of innovative experimental, computational, and analytical approaches to improve the diagnosis, treatment, and early detection of cancer. Dr. Curtis will be presenting on next generation sequencing and tumor initiation and progression.

This course walks through using and applying two popular, freely available genome browsers and how they can help bioinformatics analysis: the University of California Santa Cruz (UCSC) Genome Browser and the Integrative Genomics Viewer (IGV). Used to view the assembly of the complete genome in human or other species, these browsers are valuable tools for identifying and localizing genes/mutations. This course will demonstrate how to view different genome maps/tracks and make best use of them.

Feel free to install the IGV browser in advance from here – IGV download (the UCSV browser is web browser based).

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Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA’s knowledge & discovery tools to relate the most recent literature findings to your research. The demo session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics.
Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects.

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Presenter: Dr. Han Liang

Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to evaluate biomarkers and mechanisms underlying sensitivity and resistance to cancer therapy. The MD Anderson Cancer Center platform currently contains ~500 protein markers, covering all major signaling pathways. The Cancer Proteome Atlas (TCPA) we developed (http://tcpaportal.org) is the sole dedicated bioinformatics resource for RPPA data. Currently, it contains two analytic platforms: one contains >8000 patient samples; and the other contains >1500 cell line samples. We are developing the third component focusing on adaptive RPPA response given drug treatments. We have built an integrated data portal that contains user-friendly analytic and visualization tools for a broad biomedical community to utilize these data.To learn more please visit: https://tcpaportal.org/tcpa/
The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website https://itcr.cancer.gov/

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Overview:
Characterizing the transcriptomic profiles of individual cells by single-cell
RNA sequencing (scRNA-seq) has become a universal tool to identify
both known and novel cell populations, ushering science in a new era of
single cell biology. However, scRNA-seq utilizes dissociated cells with
results in the loss of spatial organization of the cell population being
analyzed.
It is therefore essential to complement scRNA-seq analysis with
RNAscope in situ hybridization (ISH) in order to obtain visual
confirmation of both single cell and spatial gene expression.
In this webinar, Dr. Ariel Levine from NIH NINDS will share her latest
publication using RNAscope HiPlex assay, to reveal spinal cord cell type
organization, validate a combinatorial set of markers for in-tissue spatial
gene expression analysis, and optimize the computational classification

Presented by:
Ariel Levine, Ph.D.
Earl Stadtman Investigator
National Institute of Neurological Disorders and Stroke

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Explanatory-artificial-intelligence (x-AI) has highlighted the growing need to integrate knowledge types into big data analytics. During this seminar, Dr. Bernhald Palsson will describe the three approaches used to progress knowledge in this field:
The use of Independent Component Analysis (ICA) to define independently modulated sets of genes in bacterial transcriptomes
The use of pangenome analysis for the thousands of bacterial genome sequences being generated
The use of machine learning methods for the analysis of antimicrobial resistance
Each of the approaches highlights an important advancement in the integration of knowledge types into x-AI. The first approach illustrates the principle of “getting answers to questions not asked”; the second shows “what is learned with scale”; and the third approach explains how mechanisms are built into genome-wide association studies (GWAS) using flux balance analysis (FBA).

Presenter:
Dr. Bernhald Palsson is the Distinguished Galletti Professor of Bioengineering, the principal investigator of the Systems Biology Research Group in the Department of Bioengineering, and professor of pediatrics at the University of California, San Diego.

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Learn how to locate data sharing policies for NIH, other public and private research funders, and journal publishers, and find templates for creating data management plans through DMP Tool.
For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov
Check the Research Data Resources page on the Scientific Library website to learn more about research data online resources.

New computational opportunities and challenges have emerged within the cancer research and clinical application fields, as the size, source and complexity of cancer datasets have grown. Simultaneously, advances in computational capabilities, with exceptional growth in AI and deep learning, are reaching unprecedented scales.

This Sixth Computational Approaches for Cancer Workshop 2020 (CAFCW20) will bring together a wide-range of individuals including clinicians, cancer biologists, mathematicians, data scientists, computational scientists, engineers, developers, thought leaders and others with an interest in advancing the use of computation to better understand, diagnose, treat and prevent cancer. As an interdisciplinary workshop, the sharing of insight and challenges fosters collaborations and future innovations accelerating progress in computationally and data-driven cancer research and clinical applications.

High-performance computing (HPC) has been and will continue to be a key component of cancer research. Industry, academic and government interest is demonstrably high with ongoing commitments, new announcements, advances and new opportunities involving cancer and computing. One need only review recommendations provided by the National Cancer Moonshot Blue Ribbon Panel to confirm the increasingly visible and critical role computing and HPC in particular will play in accelerating cancer research objectives. As HPC-related efforts from projects funded through the 21st Century Cures Act begin to mature, the workshop will provide an ongoing avenue for new computational approaches involving HPC at all scales to be shared with the growing community.

The Computational Approaches for Cancer workshop series originated in early 2015, following observations that the topic of cancer was already pervasive at the SC conference, yet no venue at SC existed to bring the key community together. The response has been favorable for the first five workshops with over 80 participants in each of the first two years, expanding to an estimated 150 attendees at SC17 and at room capacity in SC18 and SC19. Enthusiasm for the workshop continues to grow with many ideas and challenges shared, collaborations envisioned and needs identified. The successful call for papers in SC17 resulted in proceedings published for Open Access in BMC Bioinformatics, a growing number of submissions in SC18 and a record number of submissions in SC19. At SC19, the best paper award was given to a team who has progressively presented their work at the series of SC Computational Approaches for Cancer workshops.

Questions? Contact cafcw@nih.gov

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The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify critical questions that will guide future research directions.

We are inviting current thought leaders in the field from the U.S. and Europe in order to promote intellectual discourse among colleagues on this subject. We anticipate a highly stimulating and interactive meeting and a published report to follow.

Sessions Include:

• Enhancer genetics.
• Enhancers in development.
• Enhancer biophysics.
• Enhancer in disease.
• Enhancers and ncRNA.

Registration is required in order to receive the WebEx link.

REASONABLE ACCOMMODATION
Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Romi Sawhney, at 240-760-6400, and/or the Federal Relay Service (1-800-877-8339). Requests should be made at least two business days in advance of the event.

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Learn how to find data standards through DCC Disciplinary Metadata, FAIRsharing.org, and NIH Common Data Elements (CDEs). For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov
Check Research Data Resources page on the Scientific Library website to learn more about research data online resources.

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The goal of this meeting is to critically address the wealth of new data generated by bulk and single-cell molecular, imaging and computational approaches that are increasingly revealing how the genome folds to faithfully accommodate gene expression programs and cell fate decisions. The goal is also to advance an understanding of how transcriptional enhancers function, how to separate cause and effect, and to identify critical questions that will guide future research directions.

We are inviting current thought leaders in the field from the U.S. and Europe in order to promote intellectual discourse among colleagues on this subject. We anticipate a highly stimulating and interactive meeting and a published report to follow.

Sessions Include:

• Enhancer genetics.
• Enhancers in development.
• Enhancer biophysics.
• Enhancer in disease.
• Enhancers and ncRNA.

Registration is required in order to receive the WebEx link.

REASONABLE ACCOMMODATION

Individuals with disabilities who need Sign Language Interpreters and/or reasonable accommodation to participate in this event should contact Romi Sawhney, at 240-760-6400, and/or the Federal Relay Service (1-800-877-8339). Requests should be made at least two business days in advance of the event.

Register

What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study?

This is Part 1 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 1 will address the frequentist approach, and will cover the concepts of hypothesis testing, confidence intervals, Type I and Type II errors, statistical power, and p-values. Technical details will be kept to an absolute minimum.

Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 2 of this class series.

Instructor: Paul Wakim (External Vendor)

Artificial Intelligence (AI) and Machine Learning (ML) are hot topics today, but are already part of our everyday lives. This class will introduce the basic principles of AI and ML and the current state of this field. The instructor will share examples of AI used in common applications and health research. AI limitations and ethical considerations will also be discussed. This course is designed for individuals new to the topic or for those who need a refresher.

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Speaker: Alicia Lillich (NIH STAFF)

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RStudio will be planning quarterly webinars to support the HHS Data Science and Statistical Programming communities. The first installment will be focused on Creating Reproducible Data Science. Please join Alex Gold as he shares best practices and demonstrates project-based workflow, version control with git, creating templates and packages, and reproducing data science environments.

Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting.

For more information about RStudio in Life Sciences: https://rstudio.com/solutions/pharma/

Learn more about RStudio’s recommended professional data science solution for every team: https://rstudio.com/products/team/

Contact our team with any questions: life-sciences-healthcare@rstudio.com

Presenter:
Alex Gold

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What’s the difference between “regular” statistics (i.e. what you may have been using in the past several years) and the “new” Bayesian statistics? Which one should you use for your next study?

This is Part 2 of a two-part lecture series intended for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian. Part 2 will address the Bayesian approach, and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum.

Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study. You must register separately for Part 1 of this class series.

Speaker: Paul Wakim (External Vendor)

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Learn how to find NIH data repositories and other repositories through Re3Data. Learn about resources for locating datasets by searching across data repositories, searching individual repositories, and searching data publications. For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov
Check the Reseach Data Resources page on the Scientific Library website to learn more about research data online resources.

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Learn about the NCI cancer research data ecosystem, as well as repositories and data portals for cancer research data. For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov
Check the Research Data Resources page on the Scientific Library website to learn more about research data online resources.

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In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This two-part lecture series will help participants to better understand and prepare data, interpret results and findings, design and prepare studies, and better understand the results in published literature.
Part 1 will address considerations for the choice of statistical tests including the importance of study design and hypothesis, type of data and their distributions, and provide a brief review of important statistical features. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.
Although you may attend only one part of this series, attending both parts will give you a better understanding of the most used statistical tests in the biomedical literature. You must register separately for Part 2 of this class series.

Instructor(s) — External Vendor:
Ninet Sinaii

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In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This two-part lecture series will help participants to better understand and prepare data, interpret results and findings, design and prepare studies, and better understand the results in published literature.
Part 2 will describe the basic concepts for using common statistical tests such as Chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and some nonparametric tests. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.
Although you may attend only one part of this series, attending both parts will give you a better understanding of the most used statistical tests in the biomedical literature. You must register separately for Part 1 of this class series.

Instructor(s) — External Vendor:
Ninet Sinaii