German Conference on Bioinformatics (GCB) 2020

WS7: Expression and Single-Cell Analyses, from Standard Methods to Predictive Modeling and Machine Learning

Instructors:

Dr. Ruth Hummel, Academic Ambassador, JMP Division of SAS
Dr. Russ Wolfinger, Director of Scientific Discovery and Genomics at SAS
Dr. Meijian Guan, Research Statistician Developer, JMP Division of the SAS Institute

Abstract:

Many commonly used techniques and analyses in genomics research are mine-fields of possible missteps, and the literature doesn’t often offer much guidance or clarity. What are good practices for data cleaning and preliminary explorations? Are you controlling for the appropriate factors and including the terms you need in your models? Are you considering predictive models and machine-learning models, and what can you gain from these? How do you use these methods appropriately?

In this workshop we will cover the basics (and the “intermediates”) of standard expression analysis, methods for single-cell expression, and advanced modeling with predictive and machine learning methods, with a focus on biomarker discovery through visualization, exploration, and streamlined workflows.

We will implement these techniques in JMP Genomics software, which allows for integrated and interactive graphics (such as PCA 3-D scatterplots, volcano plots, Manhattan plots), simple workflows (such as case-control or multifactor comparisons and single-cell cluster detection), complex workflows (such as Q-K mixed models to control for population and familial similarities in genetic data), and deep analysis options (such as survival analysis and advanced predictive modeling). JMP Genomics will speed up the analysis and discovery process so we can focus on the process, options, and interpretation and get to the results quickly.

We will cover:

• Using analysis methods for Next Generation Sequencing data to help find molecular candidates for new
• therapies, for medical advances and diagnostic tools and for molecular technologies.
• Using Expression Analysis to investigate molecular responses to perturbations in biological systems and
• normalize and analyze array data and summaries from NGS studies that use intensity, aligned reads and
• count data formats.
• Using single-cell RNA-sequencing (scRNA-seq) data to uncover new and rare cell populations, track
• trajectories of distinct cell lineages in development, plot the markers by cell type and similarity using t-SNE
• and UMAP methods, and reveal differentially expressed genes between specific cell types.
• Using predictive modeling and machine learning techniques to improve prediction accuracy and advance
• the speed of research gains., and use model visualizations to understand the nuances of these modeling

Target Audience:

This workshop is intended for researchers who want to learn more about how to analyze and visualize single-cell RNAseq, traditional RNAseq, microarray, arrayCGH, SNP, ChIP, methylation, miRNA, transcriptome, and related types of -omics data.

In addition, researchers who are looking for simpler-to-use yet flexible and deep -omics analytics software may find the use of JMP Genomics software of interest.

Requirements & Material:

Participants are encouraged to bring their laptops. All course materials will be made available to participants in advance of the session.

 

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