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

Most recent Single Cell Analysis Boot Camp in NYC: August 1-2, 2019

The Single Cell Analysis Boot Camp is a two-day intensive training of seminars and hands-on analytical sessions to launch students on a path towards mastery of scRNASeq data analysis methods used in health studies.   



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Single cell Analysis OVERVIEW

Summer 2019 dates: August 1-2; 9:00am - 5:00pm

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 in 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 who have 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.

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)
  • Gene networks reconstruction
  • Principle Component Analysis (PCA) and other dimensionality reduction techniques (e.g TSNE, UMAP)
  • Transcription factor and protein “master regulator” analysis
  • Transfer learning and machine learning between bulk and single cell datasets
  • Visualization of single cell data in RStudio

Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.


There are three prerequisites/requirements to attend this training:

  1. Each participant must have an introductory background in statistics
  2. Each participant must be familiar with R.
  3. Each participant must bring a laptop with R downloaded and installed prior to the first day of the workshop. R is available for free download and installation on Mac, PC, and Linux operating systems.


The Single Cell Analysis Boot Camp will take place on the Columbia University Irving Medical Campus (CUIMC) in New York City, specifically at Columbia Mailman School of Public Health, 722 W. 168th Street, Allan Rosenfield Building 8th Floor Auditorium. Please note that the entrance to the building is on the 10th floor (training is located two floors below entrance).

General transportation and lodging information can be found in the Getting Around sectionA PDF map of the Training location on the CUIMC campus will be available closer to the training. 


Pasquale Laise, PhD, Systems Biology, Columbia University. Dr. Laise is associate research scientist at Columbia University. He holds a  master’s degree in Biology and a PhD in Nonlinear Dynamics and Complex Systems (school of informatics, dept of Engineering) both from the University of Florence, Italy. Prior to joining the Califano lab at Columbia in 2017,  he was postdoctoral fellow in the Giuseppe Testa lab at the European Institute of Oncology in Milan, Italy and a visiting postdoc in the Jason Ernst lab at University of California, Los Angeles. His main research interests are in developing predictive models for cancer treatment by integrating multi-omics  and clinical data.

Evan Paull, PhD, Systems Biology, Columbia University. Dr. Paull is lead bioinformatician and associate research scientist in the Califano lab. He joined the group in 2016, bringing expertise in pathway modeling and integration of proteomics with genomics data in cancer, while having co-authored multiple Cancer Genome Atlas (TCGA) papers. His interest is in understanding the molecular mechanisms of cancer biology using machine learning and statistical models that can exploit data from multiple types of biological assays. Prior to joining Columbia, Evan obtained his B.S. in Mathematics from the University of California, Los Angeles, and his PhD in bioinformatics and biomolecular engineering from the University of California, Santa Cruz, under the mentorship of Dr. Joshua M. Stuart.

KEYNOTE & Guest LEcturers

Keynote Speaker: Peter Sims, PhD, Systems Biology, Columbia University. Dr. Sims is an Assistant Professor of Systems Biology at Columbia University and leads a laboratory that identifies new tools for single cell and cell type-specific analysis, focusing mainly on transcriptional and translational regulation. He serves as the Director of the Columbia Single Cell Analysis Core as well as the Associate Director of the J.P. Sulzberger Columbia Genome Center.

Guest Lecturer: Jeremy Worley, Ph.D, Systems Biology, Columbia University. Dr. Worley obtained his Ph.D in molecular biology under the mentorship of Andrew Capaldi and is currently an associate research scientist in the Califano lab at Columbia University. His experience in experimental biology includes functional genomics, molecular biology, biochemistry, and genome engineering. Since joining the Califano lab, he has been using various single-cell RNA sequencing technologies to study cancer. He developed the automated, plate-based scRNA-seq platform that is operated at the JP Sulzberger Columbia Genome Center through which it has been used by labs across the U.S. and internationally. Recently, he has been using enhanced CRISPR repressors coupled with scRNA-seq to study the regulatory modules that govern cell-state in cancer.


 Early-Bird Rate (through 6/15/19)Regular Rate (6/16/19 - 7/19/19)Columbia Discount*
Student/Postdoc/Trainee$975  $1,17510% off
Faculty/Academic Staff/Non-Profit Organizations​$1,175    $1,37510% off
Corporate/For-Profit Organizations​$1,375$1,575NA


*Columbia Discount: This discount is valid for any active student, postdoc, staff, or faculty at Columbia University. To access the Columbia discount, email for instructions.

Registration Fee: This fee includes course material, breakfast, lunch, and refreshment breaks. Course material will be provided to all participants after the workshop. Lodging and transportation are not included. 

Cancellations: Cancellation notices must be received via email at least 30 days prior to the workshop start date in order to receive a full refund, minus a $50 administrative fee. Cancellation notices received via email 14-29 days prior to the workshop will receive a 50% refund, minus a $50 administrative fee. Please email your cancellation notice to​. Due to workshop capacity, we regret that we are unable to refund registration fees for cancellations after these dates. 

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 to include them on attendee communications, updated registration forms, and materials. Should the substitute fall within a different registration category your credit card will be credited/charged respectively. Please email substitute inquiries to​.




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Questions? Email the Single Cell Analysis Boot Camp team here.

The Single Cell Analysis Boot Camp is hosted by Columbia University's Department of Environmental Health Sciences in the Mailman School of Public Health.

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