Course at capacity! Join waitlist for the next live-stream Single Cell Analysis Boot Camp: June 3-4, 2021.
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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Summer 2021 dates: Live-stream, online training June 3-4, 2021; 10am EDT - ~5pm 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 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)
- Cluster Analysis
- Cell Type Identification
- Regulatory Network Analysis
- Master Regulator Analysis
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:
- Each participant must have an introductory background in statistics.
- Each participant must be familiar with R.
- 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.
Knowing basic R platform and commands is required for the Boot Camp as noted in prerequisites above. If you are new to R or need a refresher, you can review the below tutorials to be well prepared:
Download R: R is the free software programming language we will use. Choose the correct version for your laptop: Mac/Windows
Download R Studio: R studio is free software that will help us develop programs in R. Choose the correct version for your laptop: Mac/Windows
How to Download R and R studio: A tutorial on how to download R and R Studio
How to Install a Package in R studio: Steps to install a package in R Studio
Best tutorial for Boot Camp Prep: R Programming Tutorial - Learn the Basics: A free datalab.cc class on R fundamentals
If you have any specific questions about R and R studio in the context of the Single Cell Boot Camp, please email us.
Lead Instructor: Pasquale Laise, PhD, Director of Single Cell Systems Biology, DarwinHealth Inc. and adjunct Associate Research Scientist, Systems Biology, Columbia University. Dr. Laise is currently the Director of Single Cell Systems Biology at DarwinHealth Inc. and an adjunct 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. After his graduation, he moved to the laboratory of Stem Cells Epigenetics at the European Institute of Oncology in Milan (Italy) where he studied the epigenetic mechanisms underlying the development and the progression of brain cancer, and deepened his studies in cancer computational biology. During his postdoctoral training, he was awarded several prestigious fellowships, including a travel grant from the Umberto Veronesi Foundation that allowed him to join the laboratory of Computational Biology at the University of California Los Angeles (UCLA) as a visiting postdoctoral fellow. At UCLA, Dr. Laise integrated computational and experimental strategies to study the genetic and epigenetic layers of ovarian cancer dysregulation. Dr. Laise completed his postdoctoral training in the Laboratory of Systems Biology, headed by Dr. Andrea Califano, at Columbia University in New York City. He studied regulatory-network-based methodologies for the systematic analysis and integration of multi-omics data, and soon joined the Department of Systems Biology as Asssociate Research Scientist. Dr. Laise has over 10 years of experience in cancer computational biology and has authored and co-authored multiple papers published in peer reviewed international journals.
Heeju Noh, PhD, Systems Biology, Columbia University.
Aleksandar Obradovic, MD/PhD Candidate, Systems Biology, Columbia University. Aleksander is an MD/PhD Candidate in the Columbia University Department of Systems Biology, jointly mentored by Drs. Andrea Califano and Charles Drake. He holds a BA in Computer Science (Biomedical informatics Track) and in Biology from Columbia, and has experience in computational immunology and high-throughput T-Cell Receptor Sequencing. His current research is in cancer immunotherapy and involves integrating single-cell transcriptomics data with network-driven computational methods and flow cytometry to characterize cell phenotypes in the tumor microenvironment, identify tumor-immune interactions across a range of treatment conditions, and identify potential drug synergies at the single-cell level.
Lukas Vlahos, PhD Candidate, Systems Biology, Columbia University. Lukas is currently a PhD student in the Califano lab where he is the primary developer of the PISCES pipeline, the lab's toolkit for single-cell analysis. He's applied this and other tools to a wide array of single-cell data, ranging from pancreatic ductal adenocarcinoma (PDAC) to developing murine lung epithelia. His current research focuses broadly on continuing to improve the lab's single-cell analysis tools, with a specific focus on the reconstruction of tissue geometry using only single-cell RNAseq data.
Jeremy Worley, PhD, 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.
Keynote Speaker: Benjamin Izar, MD, PhD, Assistant Professor of Medicine at the CUIMC.
Former Instructors and Keynote Speakers
Evan Paull, PhD, Systems Biology, Columbia University, Single Cell Analysis Boot Camp Instructor, 2019.
Peter Sims, PhD, Systems Biology, Columbia University; Keynote Speaker, 2019. 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.
Scholarships are available for the Single Cell Analysis Boot Camp.
COVID-19 Update: The Single Cell Analysis Boot Camp will no longer take place in person due to the COVID-19 pandemic. The Boot Camp will instead be a live-stream, remote training that takes place over live, online video on June 3-4, 2021 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training.
"Great balance of lecture and hands on material to get a basic understanding of scRNA-Seq analysis that can be applied to my own data." - Gabriel G., Data Analyst, 2019
"All of the teachers/speakers were highly knowledgeable and great communicators, their clear expertise was appreciated." - Anonymous Faculty member, 2019
"This was a well-constructed workshop to provide attendees the basic tools to run a bioinformatic workflow for a scRNA analysis on their own. The instructors were experts in the field, presented material that was well-balanced between lectures and hands-on labs, were attentive to address questions and ensured that all participants were able to run through the labs." - Maya D., Icahn School of Medicine at Mount Sinai, Postdoc, 2019
"Great analysis camp- gives code for all tools needed for basic and more advanced scRNAseq analysis. Great jumping off point for researchers experienced with bulk analysis to start with single cell analysis." - Anonymous Student, 2019
"I would highly recommend the Single Cell Analysis Boot Camp to anyone looking to learn more about analyzing single cell transcriptomic data. The course offers a good balance between improving conceptual understanding as well as learning how to apply specific analysis tools." - Maria S., Memorial Sloan Kettering Cancer Center, Graduate Student
"Great for those who are just entering to scRNA-seq field and want to understand the basics. Also good if you have background but feel less confident whether you analyze your data according to the standards on the field." - Anonymous Postdoc, 2019
|Early-Bird Rate (through 4/15/21)||Regular Rate (4/16/21 - 5/25/21)||Columbia Discount*|
|Faculty/Academic Staff/Non-Profit Organizations||$975||$1,175||10% off|
*Columbia Discount: This discount is valid for any active student, postdoc, staff, or faculty at Columbia University. To access the Columbia discount, email Columbia.scRNASeq@gmail.com for instructions and specify if you are paying by credit card, or internal transfer within Columbia.
Invoice Payment and Group Registrations: If you would prefer to pay by invoice/check, or would like to pay for a group of registrants, please email Columbia.scRNASeq@gmail.com with details.
Registration Fee: This fee includes course material, which will be made available to all participants both during and after the conclusion of the training.
Cancellations: 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 <14days 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 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 Columbia.scRNASeq@gmail.com. In the event Columbia must cancel the event, your registration fee will be fully refunded.
The Single Cell Analysis Boot Camp is hosted by Columbia University's SHARP Program at the Mailman School of Public Health.
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