The goal of this course is to increase participants’ fluency in single-cell RNA-sequencing analysis and to provide concrete tools to tackle the most pertinent open questions in their research fields. Participants will be equipped to apply these lessons on single-cell analysis to their own data to enable correct biological interpretations. Participants will learn about the latest technologies and best practices of experimental methods and effective data generation, state-of-the-art algorithms used in single-cell data analysis, and tools for relating their computational results to make novel discoveries in their biological field.

The course will include detailed instruction on algorithms, applications of the analyses, and hands-on analysis, culminating in a group project and presentation re-creating a publishable figure from real-world single-cell data. Participants will receive daily personalized support and guidance from multiple leaders in the field and will have opportunities to build community and network with the instructors, guest lecturers, and fellow participants.

December 2-6, 2024

Participants are expected to be on site each day from 8:30am-5:30pm. No lodging will be provided.

Each day will begin at 8:30am with a light breakfast. Lunch is on your own from 12:00pm-1:15pm, and afternoon coffee and sweets & fruit will be available at 3:00pm. There will be a morning Q&A session from 9:00am, and each day will end with a second Q&A session from 5:00-5:30pm.

Day 1: Introduction to Single-Cell Data

• Introduction to the course
• Novel techniques in single-cell genomics
• Bulk RNA-seq introduction
• Experimental design for scRNA-seq
• Data structure and loading data onto Python, QC
• Pre-processing, normalizing data, feature selection, dimensionality reduction

Day 2: Single-Cell Data Visualization

• Ambient noise correction
• Overview of single-cell RNA-sequencing and its impact over the years
• Graphical representation of data
• Clustering, Doublet Detection
• Visualization and interpretation of data
• Differential expression analysis

Day 3: Cell Typing and Pseudotime Analysis

• Gene set enrichment analysis
• Cell typing
• Milo; SPECTRA; LIANA+
• scRNA-seq data and computation to study cellular behavior in disease context
• Pseudotime analysis
• Data denoising and imputation
• Beyond pedestrian scRNA-seq: Enrichment experiments

Day 4: Multi-Sample Analysis and Batch Effect

• Loading multiple samples
• Detecting and correcting batch effect
• Novel computational tools for analysis of scRNA-seq, scATAC-seq, and beyond
• Reading methods in a single-cell paper with a critical eye
• Additional data wrangling

Day 5: Hands-On Analysis to Recreate a Published Figure

Class is divided into groups, and each group spends the day working on their project and presenting to the class and the instructors

• Ronan Chaligne, PhD
  Associate Lab Member, Single Cell Analytics Innovation Lab
• Susan Ehrenkranz De Wolf, MD
  Assistant Attending, Department of Medicine
• Richard Koche, PhD
  Assistant Lab Member, Epigenetics Research Innovation Lab
• Neeman Mohibullah, PhD
  Associate Lab Member, Integrated Genomics Operation
• Ushma Neill, PhD
  Vice President, Scientific Education and Training
• Thomas Norman, PhD
  Assistant Member, Computational and Systems Biology Program
• Andrea Schietinger, PhD
  Member, Immunology Program
• Roshan Sharma, PhD
  Manager, Computational Biologist, Single Cell Analytics Innovation Lab
• Thalyana Stathis, PhD
  Associate Director, Office of Career and Professional Development

Course attendance will be capped at 24 participants.

Academic and industry researchers from all biomedical fields are welcomed and graduate students (who have passed their qualifying exam) & postdocs are encouraged to apply. Researchers from underrepresented racial and ethnic groups, and those with disabilities or from disadvantaged backgrounds, are also especially encouraged to apply. As no lodging will be provided, we will prioritize senior graduate students, postdocs, staff scientists, and junior faculty from local NYC-area universities and non-profit research institutions, and NYC-area industry scientists.

The course aims to educate wet-lab researchers to be able to independently analyze their own single-cell RNA-sequencing data in the future (they do not already need to have a dataset in hand). Participants are expected to already have programming fluency and be comfortable with basic data processing in R or Python. No time will be spent on the basics of Python, and the course will move at a fast pace; please do not register if you are not fluent in Python. However, participants with programming fluency in R will be brought up to speed in Python.

Participants are not expected to have had any previous exposure to single-cell data analysis tools. Pre-course content will situate participants with a basic introduction to Scanpy, a Python package designed for analysis of single-cell RNA-sequencing data, which hosts an annotated data structure (AnnData) aptly designed for handling such data.

Note: If you work at MSK, please do not apply to this course. Instead, look out for emails sent by the Office of Scientific Education and Training about applying for all of the courses that are offered internally.