Single Cell Analysis Boot Camp: Systems Biology Methods for Analysis of Single Cell RNA-Seq

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Single Cell Analysis Overview

Summer 2025 dates: Livestream, online training June 2-3, 2025; 10:00am - ~5:30pm EDT. 

Recently developed methods for scRNASeq analysis focus on the comparison of whole transcriptional profiles to separate hundreds or thousands of single cells into several distinct populations. These methods are largely unsupervised, allowing researchers to explore new and novel populations. Interpreting the biology of these novel populations is challenging and is a major focus of cutting-edge systems biology methodology that can deconvolve the high dimensional data into meaningful components.

This two-day intensive boot camp starts with a fast-paced training session on single cell data collection and basic analysis workflows on the first half-day, then continues with in-depth sessions on advanced methods for phenotyping single cell populations using systems-biology approaches. Led by a team that invented several of the methods used in network biology and single-cell transcriptome analysis, we demonstrate how to use network models to convert gene expression profiles into protein activity profiles, and how to transfer knowledge between established bulk datasets and novel single-cell data. We expect that, during this hands-on workshop, participants will acquire enough knowledge to plan and perform scRNAseq analyses in real-world aplications.

By the end of the workshop, participants will be familiar with the following topics:

• Gene Expression Analysis of scRNA data (pre-processing, quality control, filtering, normalization)
• Cluster Analysis
• Cell Type Identification
• Regulatory Network Analysis
• Master Regulator Analysis

Audience and Requirements

Investigators from any institution and from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are a few requirements to attend this training:

1. Each participant must have an introductory background in statistics.
2. Each participant must be familiar with basic R & Python.
3. Each participant must have a free, basic RStudio Cloud account prior to the first day of the workshop for lab sessions.
4. (Extra) Each participant will benefit from having a free basic Google/Gmail account to access Google Colab for additional material. 

R and Python Tutorials

Knowing basic R and Python platform and commands is required for the Boot Camp as noted in prerequisites above. This training will use RStudio Cloud (now called Posit). If you are new to R and Python or need a refresher, you can review the below tutorials to be well prepared:

1. R Programming Tutorial - Learn the Basics(link is external and opens in a new window): A free datalab.cc class on R fundamentals.
2. Once you create your free, basic RStudio Cloud account for the training: Primers on Programming Basics and Visualization Basics(link is external and opens in a new window).
3. SHARP Program RStudio Cloud Tutorial(link is external and opens in a new window): This self-paced tutorial from the Columbia SHARP Program walks through the Cloud Platform you will use at the training, as well as some basic exercises. We recommend this tutorial if you have not used the Cloud version of RStudio before, or if you are a beginner user of R.
4. Python Crash Course for Beginners(link is external and opens in a new window): A YouTube video on Python fundamentals.

If you have any specific questions about RStudio Cloud in the context of the Boot Camp, please email the Single Cell Team(link sends e-mail).

Instructors

Boot Camp Director: Luca Zanella, PhD, Department of Systems Biology, Columbia University. Luca earned his B.Sc., M.Sc., and Ph.D. in Chemical Engineering from the University of Padova, Italy. His doctoral research focused on the study of tumor-derived extracellular vesicles in cancer dissemination and metastasis using computational models. During the last year of his Ph.D., he joined Dr. Andrea Califano’s Lab at Columbia University, where he worked on the development and application of network-based methodologies to study tumor heterogeneity and cell-cell communication using single-cell RNA-seq data, in the context of aggressive prostate and pancreatic cancers. His current research goal is to develop and apply computational tools to dissect mechanisms of cancer cell adaptation from perturbational data at single-cell resolution.

Ester Calvo Fernández(link is external and opens in a new window), PhD,PharmD, PostDoc Harvard University Medical School. Ester earned her PharmD from the University of Barcelona, in Barcelona, Spain. After that, she was a St. Baldrick's fellow at the University of Michigan for two years, where she studied in vivo characterization of modulators of Notch signaling in mouse models of Sonic Hedgehog medulloblastoma tumorigenesis. She is now a PhD candidate in Dr. Andrea Califano's lab with a strong background in pharmacology and molecular biology. Her current research focuses broadly on applying novel systems biology approaches to defining and targeting master regulator dependencies from single-cell RNA-seq in diffuse midline gliomas (DMG). Her research focuses on dissecting the heterogeneity of these tumors at the single cell level, defining tumor checkpoint modules representing pharmacologically accessible, mechanistic determinants of molecularly-distinct DMG cell states, and predicting and validating novel therapeutic strategies for preclinical and clinical testing to improve outcomes in this fatal disease. She is also involved in the interrogation of genome-wide, experimentally dissected druggable gene regulatory networks to elucidate several mechanisms of actions of these compounds at the single cell level, as well as in other Pancreatic Ductal Adenocarcinoma (PDA) and Non-Small Cell Lung Cancer single-cell studies.

Aleksandar Obradovic,(link is external and opens in a new window)MD/PhD, Columbia University. Dr. Obradovics research focuses on the development of novel approaches for analysis of single-cell transcriptional, TCR-Seq, and spatial data, applied toward an improved understanding of immunologic checkpoint inhibitor resistance mechanisms and discovery of synergistic combination-therapies.

Melania Franchini, PhD, Department of Systems Biology, Columbia University.  Melania earned his B.Sc. and M.Sc. in Medical Biotechnology and a Ph.D. in Computational and Quantitative Biology from the University of Naples, Italy. Her doctoral research focused on investigating vulnerabilities in EGFR-dependent breast cancer cells using single-cell lineage tracing and time-resolved computational analyses. In early 2024, Melania joined Dr. Andrea Califano’s Lab in the Department of Systems Biology for postdoctoral training. Her research focuses on overcoming drug resistance in rare pediatric sarcomas by targeting Tumor Checkpoint modules and exploring tumor microenvironment (TME) remodeling in prostate cancer to understand immunosuppression mechanisms after Master Regulators treatments.

Alessandro Vasciaveo, is an Assistant Professor of Computational Biology and Artificial Intelligence at the Sanford Burnham Prebys Medical Discovery Institute in San Diego U.S. His lab operates at the intersection of data science and precision medicine, leveraging advanced analysis and modeling of large scale single-cell datasets to uncover novel disease states and identify therapeutic targets for intervention.

Keynote Speaker: Kenneth P. Olive(link is external and opens in a new window), PhD, Associate Professor, Department of Medicine Columbia University. Dr. Olive is an associate professor in the department of medicine and director of the Oncology Precision Therapeutics and Imaging Core (OPTIC)(link is external and opens in a new window) Shared Resource. Dr. Olive's research is dedicated to finding a cure for pancreatic cancer. The Olive Lab performs preclinical therapeutics trials using advanced genetically engineered mouse models of pancreatic cancer, and employing advanced imaging technologies such as high-resolution ultrasound and optical imaging, pharmacokinetic and pharmacodynamic analyses, functional imaging, microscopy, biochemistry and molecular biology techniques to assess drug mechanisms and understand relevant signaling pathways.

Scholarships

Training scholarships are available for the Single Cell Analysis Boot Camp.

Locations

Summer 2025: Livestream, remote training that takes place over live, online video on June 2-3, 2025 from 10:00am - ~5:15pm EDT.  Please note this training is not a self-paced, pre-recorded online training.  

Testimonials

"The Single Cell Analysis Boot Camp gave me the confidence to think critically about how to analyze and interpret my scRNAseq data. The instructors were great at explaining concepts and did a great job of both covering analysis and big picture concepts." - Postdoc at Harvard Medical School, 2024

"Excellent boot camp for beginners and advanced users. Great team members with thoughtful and relevant questions for the instructors. An amazing learning experience with fantastic course materials and up-to-date pre-course literature reading. All the instructors were highly knowledgeable and dedicated to teaching, and I thoroughly enjoyed every session." - Faculty Member at Johns Hopkins University, 2024

"The boot camp consolidated years of valuable information in a digestible, effective, and easy-to-understand manner." - Faculty Member at Johns Hopkins Medicine, 2024

Registration Fees