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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 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.

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In this training, the attendees will learn how advanced pathway and network biology algorithms from the Computational Biology Methods for Drug Discovery (CBDD) toolkit can be applied to a broad range of OMICs datasets without the need for scripting skills. The instructor will provide use cases including disease mechanism reconstruction, drug mechanism of action elucidation, target discovery, biomarker identification, and integration of omics datasets. This class would be useful to clinicians and researchers/scientists in digging deep on the association of diseases, biomarkers, and drugs.

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Data Wrangling in R is the third class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of manipulating, analyzing and exporting data with the R tidyverse. 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, students should be able to: describe the purpose of Tidyverse packages; select certain columns or rows in a data frame; describe the function of the pipe operator; add new columns to a dataframe that are functions of existing columns; use the split-apply-combine concept for data analysis; use summarize, group by, and count to split a data frame into groups of observations, apply summary statistics for each group, and then combine the results; describe the concept of a wide and a long table format and for which purpose those formats are useful; describe the function of key-value pairs; reshape a data frame using the gather commands from the tidyr package; export a data frame to a .csv file.

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

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QIAGEN’s Ingenuity Variant Analysis (IVA) has been replaced by QIAGEN Clinical Insight Interpret – Translational (QCII-T), which 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. QCII-T allows you to interrogate variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. This class will focus on how to use QCII-T to upload datasets, efficiently use different filtering mechanisms to identify causal variants, and export data. Participants will also review feature changes migrating from Ingenuity Variant Analysis to QCII-T.