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Welcome to the ISMB2018_SingleCellTranscriptomeTutorial wiki!

Presenters Tyler Faits, Boston University, United States Matan Hofree, Broad Institute, United States Ayshwarya Subramanian, Broad Institute, United States Alex Tsankov, Broad Institute, United States Overview Single cell transcriptomics has emerged as a powerful tool to identify and interrogate novel cell types in homeostatic and perturbed states. Unlike bulk transcriptomics, single cell data provides resolution at the level of individual cells while working with much smaller quantities of RNA. As such, analysis of single cell RNA sequencing (scRNA-seq) data presents challenges of scale and technical noise, while providing the resolution necessary to pursue novel questions that earlier technologies did not allow.

The objective of the tutorial is to provide an overview of the laboratory and computational challenges involved in generating and analyzing scRNA-seq data. Participants will be introduced to popular molecular technologies for generating scRNA-seq data, and gain hands-on experience with existing software tools and computational methods for its analysis. The tutorial will briefly introduce approaches for preprocessing of scRNA-seq data, including demultiplexing, sequence alignment, and quality control. Then, starting from a cell x gene expression matrix, participants will learn standard methods to infer heterogeneity by identifying clusters of cells and perform analyses to assign cell identity and function. Participants will also be introduced to specialized analytical methods for exploring expression signatures of cell states, cellular differentiation trajectories, inference of cellular localization, and modern methods targeted towards better understanding of cancer biology. Analyses will be performed by executing commands in RStudio as well as leveraging newly developed point-and-click graphical R/Shiny interfaces.

Audience Familiarity with basic RNA-Seq data analysis and working knowledge of R.

Requirements The tutorial will utilize web/cloud-based computing infrastructure with all software preinstalled, such that the only user requirement will be a personal laptop with the Google Chrome web browser installed. From within the Chrome web browser, users will access RStudio and additional web-based utilities for computation.

Schedule Overview 9:00-9:45 am Introduction: Tutorial infrastructure setup; Technologies for scRNA-seq data generation; Description of course datasets, case study and analysis questions 9:45-10:15 am Quality-control and preprocessing; introduction to scRNA-seq data structures in R 10:15-11:00 am Basic analyses of scRNA-seq data; batch effect correction, clustering and inference of cell-types 11:00-11:15 am Coffee Break 11:15-11:45 am Cell cluster-based differential expression and pathway analysis 11:45 am-12:30 pm Interactive tools for visualization and scRNA-seq analysis 12:30-1:00 pm Specialized scRNA-seq applications, currently available resources, and data repositories