Events by this organizer

This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of RStudio, including how to import data, create scripts, and run code. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services and Bioinformatics programs that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, students should be able to: list reasons for using R; describe the purpose of the RStudio Script, Console, Environment, and Plots panes; describe the various methods for finding help on R and RStudio; create a script in R and run code from a script and from the console; define the following terms as they relate to R: object, assign, call, function, and arguments; assign values to objects in R; learn about R objects; describe the major R data types; understand how to create and work with R vectors; describe the steps for importing data into R.

Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the data before the class. Please bring your laptop with R and RStudio installed.

Registration

This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, participants should be able to: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; and describe how to save data in R.

Participants are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class.

Registration

Participants will learn how to develop artificial intelligence (AI) applications focused on biomedical imaging datasets. The course will cover the entire AI pipeline from image exploration and labeling to development and deployment of predictive models on images using both machine learning and deep learning approaches. This includes annotating and exploring image datasets, exploring techniques for developing machine and deep learning models, optimizing the parameters of the deep networks, and automatically generating code for edge deployment of AI models.

This is an introductory level class. No installation of MATLAB is necessary.

Registration

QIAGEN’s Ingenuity Variant Analysis (IVA) combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based on published biological evidence and your knowledge of disease biology. IVA allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This workshop will focus on how to use IVA to upload datasets, efficiently use different filters within variant analysis to identify causal variants, and export data. The class will also review recent IVA updates.

The Human Gene Mutation Database (HGMD) database provides quick access to known published gene lesions responsible for human inherited disease. This session will explore how to query genes and mutations in HGMD and how to view the curated content.

Registration

Participants will learn how to develop artificial intelligence (AI) applications using MATLAB, even if they do not have a formal background in machine and deep learning. The goal of this course is to introduce tools and fundamental approaches for developing predictive models on biomedical signals. The course will cover the entire AI pipeline, from signal exploration to deployment, including: annotating time series biomedical signals automatically, creating deep learning models using Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) for biomedical signal data, creating machine learning models for biomedical signal data, applying advanced signal pre-processing techniques for automated feature extraction, and automatically generating code for edge deployment of AI models.

This is an introductory level class. No installation of MATLAB is necessary.

Registration

Register

In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class to assist researchers in writing a manuscript. Participants will become familiar with statistical issues that can arise as well as recommendations to prevent them. At the end of the class, participants should have a good sense of what to do and not to do when writing the statistical sections of a manuscript. Most of the examples will be related to clinical research; however, anyone can benefit from the tips shared. Plenty of time will be devoted for questions, and references will be provided for more in-depth self-study.
This class complements, How to Write a Research Paper Parts 1 & 2, as it specifically covers key points in writing the statistical portion of a manuscript – particularly for clinical research. Attendance to How to Write a Research Paper is not required to benefit from this class.

Instructor(s) — External Vendor:
Paul Wakim

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.

Register

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).

Register

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.

Register

Speaker: Alicia Lillich (NIH STAFF)

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 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)

Register

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

Register

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