Data Science Training Program
Ayushi Agrawal
Michela Traglia
Reuben Thomas
This event was rescheduled from the original date of May 5–6, 2024 to May 16–17, 2024.
This 2-day hands-on workshop will provide an introduction to a typical single-cell ATAC-seq protocol and focus on the data analysis steps to address unique computational challenges before the downstream analysis.
You’ll learn how to perform quality checks of raw data in the typical format provided by sequencing centers, how to process the data, perform dimensionality reduction for visualization, clustering and cell type annotation, generate a gene score matrix to identify cell types, and perform motif and peak enrichment using ArchR.
Prior experience with transcriptomics data or attendance at an scRNA-seq or bulk RNA-seq workshop is required.
Attendance at both sessions is required.
Requirements:
- Prior attendance at an scRNA-seq or bulk RNA-seq workshop, or experience with analysis of transcriptomics data
- Familiarity with R and RStudio (e.g., reading in files, working with lists and dataframes)
Visit the workshop site for more details and materials.
Details
Dates
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.