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

 

Course is at capacity! Join the waitlist for the next live-stream Single Cell Analysis Boot Camp on July 25-26, 2022. 

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.   

Subscribe for updates on registration and scholarship dates, deadlines, and announcements.


 
 

Single cell Analysis OVERVIEW

Summer 2022 dates: Live-stream, online training July 25-26, 2022; 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.

PREREQUISITES AND REQUIREMENTS

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 have a free, basic RStudio Cloud account prior to the first day of the workshop.

R TUTORIALS

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

If you have any specific questions about R and R studio in the context of the Single Cell Boot Camp, please email us.

Instructors

Lead Instructor: Lorenzo Tomassoni, PhD, Postdoctoral Research Scientist, Department of Systems Biology, Columbia University. Dr. Tomassoni holds a master’s degree in computer science and a PhD in Computational Systems Biology both from the University of Perugia, Italy. During the last year of his PhD he joined the Laboratory of Dr. Andrea Califano, at Columbia University in New York City, where he studied regulatory-network-based methodologies for the systematic analysis and integration of multi-omics data with specific focus on single-cell data. Then, as a Postdoctoral Research Scientist, he developed novel algorithms and approaches for the analysis of Single-Cell RNA Seq data and he is leading several projects in the field of drug discovery using human-related single-cell data with particular interest in brain and pancreatic tumors.

Ester Calvo Fernández, PhD Candidate, Departments of Pathology and Cell Biology and Systems Biology, Columbia University. 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. 

Aaron Griffin, Medical Scientist Training Program and Department of Systems Biology, Columbia University Irving Medical Campus. Aaron is an MD-PhD student in the Califano Laboratory; his graduate research focuses on leveraging novel computational methods to identify and target Master Regulators of drug resistance in lung cancer using tumor transcriptomic data. He is passionate about robust statistical inference and information theory and has applied these concepts to advance key components of the Califano Lab's major algorithms, including the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe), an information-theoretic algorithm which reverse-engineers context-specific transcriptional regulatory networks from gene expression data, and Nonparametric analytical Rank-based Enrichment Analysis (NaRnEA), a novel gene set analysis method which functionalizes regulatory networks to accurately measure transcriptional regulatory protein activity from gene expression data. His long-term career goal is to combine computational biology, clinical oncology, and next-generation sequencing technologies to advance personalized cancer diagnosis and treatment strategies as an independent physician-scientist.

Heeju Noh, PhD, Systems Biology, Columbia University. Dr. Heeju Noh obtained her master’s degree and PhD in Chemical and Bioengineering at ETH Zurich. For her Master’s, she focused on the cellular metabolic system to address the perturbation effects of growth conditions, and for her PhD, she was trained in computational modeling of gene regulatory systems for predicting targets of drugs and the effect of other external perturbations. In 2018 she joined Dr. Califano's lab at Columbia University as a postdoctoral researcher and has participated in multiple research projects involving single-cell data analysis. Examples include using gene regulatory modeling to identify cell subpopulations and indicate candidate drugs for targeting specific cell groups in human cancers and other devastating human diseases.

Lukas Vlahos, PhD Candidate, Department of 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.

KEYNOTE SPEAKER

Announcement coming soon.

Former Instructors and Keynote Speakers

Benjamin Izar, MD, PhD, Assistant Professor of Medicine at CUIMC; Keynote Speaker, 2021.

Pasquale Laise, PhD, Director of Single Cell Systems Biology, DarwinHealth Inc. and Adjunct Associate Research Scientist, Systems Biology, Columbia University. Single Cell Analysis Boot Camp Instructor, 2019 and 2021.

Evan Paull, PhD, Systems Biology, Columbia University, Single Cell Analysis Boot Camp Instructor, 2019. 

Peter Sims, PhD, Systems Biology, Columbia University; Keynote Speaker, 2019. 

SCHOLARSHIPS

Scholarships are available for the Single Cell Analysis Boot Camp.

LOCATIONS

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 July 25-26, 2022 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training.

Testimonials

"The Boot camp included a mix of pragmatic instruction regarding sample preparation, initial analyses for data quality, basic clustering and cell type assignments, and more advanced analyses to identify regulatory networks in single cell RNA Seq data.  My lab is new to single cell RNA Seq, and the Boot camp was on target for my needs." - Faculty member at University of Texas Health Science Center Houston, 2021

"Useful program that offered lots of tools, whether you're just starting scSeq analysis or have experience." - Postdoc at Emory University, 2021

"I learned so much! I came in with no experience, and now feel I have the tools to start working with some real data sets and am better equipped to understand publications that use this technology." - Postdoc at University of Colorado Boulder, 2021

"Well planned and wasn't overwhelming. The lectures before coding helped make sense of what we were doing." - Postdoc at Columbia University, 2021

Check out more testimonials here.

REGISTRATION FEES

  Early-Bird Rate (through 5/15/22) Regular Rate (5/16/22 - 7/13/22) Columbia Discount*
Student/Postdoc/Trainee $925 $1,025 10% off
Faculty/Academic Staff/Non-Profit Organizations/Government Agencies $1,025 $1,225 10% off
Corporate/For-Profit Organizations​ $1,225 $1,425 NA

 

*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 the following: 1) your registration category from the table above, 2) if you are paying by credit card, or internal transfer within Columbia, and 3) if an internal transfer, indicate the registration category from the table above, and if grant funds are being used.

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 the following details: 1) full attendee name(s) and applicable registration category from the table above, and 2) payment method (credit card, invoice, wire).

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. 

ADDITIONAL INFORMATION

 

 

Want updates on new Single Cell Analysis Boot Camp details or registration deadlines? Subscribe here.

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

The Single Cell Analysis Boot Camp is hosted by Columbia University's SHARP Program at the Mailman School of Public Health.

 

Additional Testimonials

"Thanks to this workshop, I now have the tools I need to better explore single cell analysis on my own moving forward. I feel more confident in taking ownership over my future single cell studies, from experimental design to quality control and further downstream analyses." - Student at Boston University School of Medicine, 2021

"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