Data Science Training Program
Reuben Thomas
Michela Traglia
Ayushi Agrawal
This 3-session workshop will focus on the analysis of single-cell RNA-Seq data, starting from raw count matrices and progressing to network analysis of single-cell datasets. This 3-session workshop will focus on the analysis of single-cell RNA-seq data, starting from raw count matrices. You'll learn about quality control of the raw data, normalization, feature selection, dimensionality reduction, clustering, finding marker genes, and batch correction. You'll analyze data using R.
Attendance at all three sessions is required.
- April 29: 9:00am-12:00pm: Loading data, quality control, normalization, feature selection, dimensionality reduction
- April 29: 1:00-4:00pm: Dimensionality reduction (continued), clustering, finding marker genes
- April 30: 1:00-4:00pm: Advanced discussion on normalization, differential analysis, and batch-correction
Advanced: This is an advanced workshop in the RNA-Seq Analysis series. Prior experience with RNA-Seq analysis is required. See introductory and intermediate workshops:
Visit the workshop site for more details and materials
Details
Dates
9:00am–12:00pm
1:00pm–4:00pm
1:00pm–4:00pm
Location
OnlineContact(s)
The Gladstone Data Science Training Program was started in 2018 to provide trainees with learning opportunities and hands-on workshops to improve their skills in bioinformatics and computational analysis. This program offers a series of workshops throughout the year to enable trainees to gain new skills and get support with their questions and data.
Diversity, Equity, and Inclusion
At Gladstone, we are committed to providing events and professional development activities that resonate with our community’s diverse members. Our goal is to develop creative programming that encompasses a wide variety of ideas and perspectives to inspire, educate, and engage with everyone within our walls.
We want to effect positive change through our events and activities by providing a platform for discussions on important topics related to increasing diversity and inclusiveness in the sciences.