Single Cell Analysis Boot Camp

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 three-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 applications.

Attendees will have the option to attend either the:

• First 2 days of this course.
• All 3 days of this course.
• Just the last day of this course.

The 3rd day of this course will be a high level session for people who have more advanced skills in Transcriptomics. We recommend a high level of knowledge in R to attend the 3rd day. 

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

Summer 2026: Livestream, remote training that takes place over live, online video on May 18-20, 2026 from 10:00am - ~5:15pm EDT.  Please note this training is not a self-paced, pre-recorded online training.  

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. 

Knowing basic R and Python platform and commands is required for the Boot Camp as noted in prerequisites above. This training will use Posit Cloud (formerly RStudio Cloud). 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: A free datalab.cc class on R fundamentals.
2. Once you create your free, basic Posit Cloud account for the training: Primers on Programming Basics and Visualization Basics.
3. SHARP Program RStudio Cloud Tutorial: 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: A YouTube video on Python fundamentals.

If you have any specific questions about Posit Cloud (formerly RStudio Cloud) in the context of the Boot Camp, please email the Single Cell Team.

Boot Camp Director: Luca Zanella, PhD, Department of Systems Biology, Columbia University. Luca earned his BSc, MSc, and PhD 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 PhD, 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, 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, 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 BSc and MSc in Medical Biotechnology and a PhD 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. 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.

2026 Keynote: Dr. Peter Sims, PhD, Systems Biology, Columbia University: The Sims laboratory develops experimental and computational tools for genomic analysis with applications in cancer biology, immunology, and neuroscience. Researchers in his lab apply these tools in many areas of biomedical research from the identification of cell type-specific drug responses in malignant brain tumors to the understanding tissue adaptation and function across immune cell lineages. Dr. Sims is also the Scientific Director of the Sulzberger Columbia Genome Center and co-directs the Precision Oncology and Systems Biology program in the Herbert Irving Comprehensive Cancer Center.

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

"Wonderful team and layout. Easy to follow and overall, quite welcoming and available. I am someone who came in with no preparation or background, yet I feel like I am leaving with a concrete guide on how to take the next steps. Definitely worth taking the course, no matter what your level of expertise is." - Clinical Fellow at Memorial Sloan Kettering Cancer Center, 2025

"Very helpful to get a sense of the process of scRNA-seq analysis and associated challenges. I'm looking forward to taking some time to do a deep dive into all the code." - Scientist at Relay Therapeutics, 2025

"Appreciated the presenters' expertise and willingness to answer all questions thoroughly, also offering to be available in the coming weeks to help with any lingering questions." - Faculty Member at Brigham and Women's Hospital, 2025

"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

"The Single Cell Analysis Boot Camp was incredibly helpful for understanding not only the basics of what single cell sequencing is and how it works, but the different information that can be gained and how to analyze the data to obtain this information. Very well worth the cost." - Student at University of Pittsburgh, 2023

"The Single Cell Analysis Boot Camp did an excellent job at providing a basic introduction to using scRNA-seq technologies and how this can be applied to our research. Instructors made difficult topics easy to understand." - Student at Albany Medical College, 2023

"This gives a great basis for understanding single-cell RNA-seq analysis. It's definitely not just another Seurat walkthrough." - Postdoc at Oregon Health & Science University, 2023

Registration Fee is based on your category and includes course material, which will be made available to all participants both during and after the conclusion of the boot camp.

2026 Registration Category Rates: 

• Student/Postdoc/Trainee: 
  • Early-bird rate: $1,195 
  • Regular rate: $1,395 
• Faculty/Academic Staff/Non-Profit Organizations/Government Agencies: 
  • Early-bird rate: $1,395 
  • Regular rate: $1,595 
• Corporate/For-Profit Organizations: 
  • Early-bird rate: $1,595 
  • Regular rate: $1,795 

$200 early-bird discount is automatically applied if you register before the April 15 deadline.

• $200 Early-bird Discount: This is automatically applied if you register before the April 15 early-bird deadline.  
• 10% Columbia Discount: This is valid for any active student, postdoc, staff, or faculty at Columbia University. If paying by credit card, use your Columbia email address during the registration process to automatically have the discount applied. If paying by internal transfer within Columbia, see below.  
• 10% Mailman Alumni Discount: This is valid for any individual who graduated from the Columbia University Mailman School of Public Health. To access the Mailman Alumni discount and receive a registration code, please email sharp_program@cumc.columbia.edu your graduation year and degree.  
• Group discounts are available for organizations sending 5+ participants. Please contact us directly at sharp_program@cumc.columbia.edu for more information.  

If paying by internal transfer within Columbia, submit this Columbia Internal Transfer Request form (link to form coming soon) to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.

If you would prefer to pay by invoice/check, please submit this Invoice Request form (link to form coming soon) to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.

Cancellation notices must be received via email at least 30 days prior to the training start date in order to receive a full refund, minus a $75 administrative fee. Cancellation notices received via email 14-29 days prior to the training will receive a 75% refund, minus a $75 administrative fee. Please email your cancellation notice to Columbia.scRNASeq@gmail.com. Due to workshop capacity and preparation, we regret that we are unable to refund registration fees for cancellations less than 14 days prior to the training.

If you are unable to attend the training, we encourage you to send a substitute within the same registration category. Please inform us of the substitute via email at least one week prior to the training so we can include them on attendee communications, gather registration details, and provide materials. Should the substitute fall within a different registration category (e.g., you are a faculty member and they are a postdoc), the credit card on file will be credited/charged respectively.  Please email substitute inquiries to Columbia.scRNASeq@gmail.com. In the event Columbia must cancel the event, your registration fee will be fully refunded.