Courses


EPFL Courses

BIO-373: Genetics and Genomics

Lecturers

Jacques Fellay (Fellay's Lab)
Bart Deplancke (Deplancke's Lab)
Vincent Gardeux (Deplancke's Lab)

Summary

Content
This course covers first the classic genetics, followed by more advanced contemporary genomics techniques. This course also introduce the R programming language, followed by basic bioinformatics for the analysis of genomics data.

BIOENG-420: Single-cell Biology

Lecturers

David Suter (Suter's Lab)
Bart Deplancke (Deplancke's Lab)
Vincent Gardeux (Deplancke's Lab)
Petra C. Schwalie (Former Deplancke's Lab)

Summary

Motivation
The students are exposed to experimental and analytical approaches specific to single cell biology, with an emphasis on quantitative aspects.

Content
The course is organized in four parts, each containing alternating lectures and journal clubs presented by the students in a 1:1 ratio (see Teaching methods below for more details). Part 1 (weeks 1-4) will focus on the fundamental and biomedical research values of single cell genomic and transcriptomic analyses. Part 2 (weeks 5-8) will focus on dynamic analysis of gene expression, signaling and cell fate choices in single cells. Part 3 (weeks 9-10) will focus on engineering approaches to single cell analysis. Finally, part 4 (weeks 11-12) will focus on non-genetic heterogeneity in bacteria and its consequences. Week 13 will consist of a half-day symposium featuring external and internal speakers (week 13) and an oral exam (week 14).

Learning Outcomes
By the end of the course, the student must be able to:

  • Explain the limitations of bulk analysis that can be overcome by single cell analysis
  • Explain the advantages and limitations of single cell analysis in gathering quantitative data
  • Explain how single cell analyses can have diagnostic or biomedical value
  • Propose experimental approaches to investigate phenotypic heterogeneity in a cell population
  • Propose experimental approaches to investigate temporal fluctuations in gene expression
  • Propose experimental approaches to investigate cell fate choices and bacterial resistance to drugs at the single cell level

Tutorials / Workshops

SIB course: Single-cell RNA-Seq Analysis (28-30.11.2018)

Single-cell RNA-Seq Analysis

Organizers

Atul Sethi, Panagiotis Papasaikas, Sébastien Smallwood (FMI, Basel)
Vincent Gardeux (EPFL, Lausanne)

Summary

Motivation

Over the last few years, single-cell RNA-sequencing (scRNAseq) has emerged as a revolutionary tool with the potential to explore biological heterogeneity at the most basic level of organismal organization. Several questions inaccessible in the context of bulk RNAseq can now be addressed or at least probed in a meaningful manner. Examples include the possibility for detailed mapping of the cellular composition of tissue types and identification of novel cell-types, characterization of their individual transcriptomes, discrimination of transcriptional variation arising at the single-cell versus the cell-ensemble level and highlighting the contribution of individual cells to tissue differentiation, development and disease progression.

This promise comes along with multiple technical and computational challenges. While scRNAseq data is structurally similar to bulk RNA-seq, the paucity of starting material combined with multiple confounded sources of variance result in low signal to noise ratio exemplified by high abundance of zeroes in the gene expression matrices. In this new setting, existing techniques need to be modified or novel approaches need to be developed for downstream analyses.

Content

  1. A general overview of single-cell transcriptomics, and single-cell based sequencing technologies. In particular, we’ll discuss the limitations of bulk workflows that can be overcome with single-cell analyses, as well as the advantages and limitations of single-cell analyses in gathering quantitative data.
  2. A practical session highlighting several of the most critical and common issues associated with the computational analysis of scRNAseq data:
    • Characteristics, comparison and limitations of scRNAseq data generated from the different protocols and commercially available systems.
    • Data structures, pre-processing and quality control.
    • Data visualization and detection of biologically meaningful subpopulations
    • Differential gene expression.
    • Sources of biological and technical variation and circumvention of confounding effects.
    • Pseudotime ordering and trajectory estimation.
  3. A practical session featuring ASAP, a web-based portal for the interactive analysis of scRNA-seq datasets. It will highlight the major tools used for scRNAseq analysis, as well as their limitation. Participants are invited to bring their own datasets for an interactive analysis, if interested. Details will be provided as to how the field is evolving and what are the coming challenges, with a practical example from the Human Cell Atlas initiative.

This tutorial is designed as a guided conversation through scRNAseq analyses combining lecture and hands-on sessions. It intends to give audience a feel for the data and walk them through major analyses techniques and concepts using illustrative examples and R-scripts that are applicable/extendable to most commonly available types of scRNAseq data.

Learning objectives

  • Gain basic knowledge about scRNAseq protocols and kind of data produced by them.
  • Become familiar with basic data-structures used in scRNA-seq analysis, perform basic QC, filtering ( reads, cells, genes ), and normalization of scRNAseq data.
  • Detect possible sources of technical and biological confounding variables (e.g. library complexity, cell cycle, etc.). Apply techniques to remove or account for these confounders in subsequent analyses and evaluate their strengths and weaknesses.
  • Identify scRNAseq specific challenges in visualization and clustering for subpopulation detection, and population marker identification.
  • Perform differential gene expression analyses of genes across subpopulations, pseudotime ordering and trajectory estimation.
  • Implement aforementioned concepts with practical examples from publicly available scRNAseq datasets using custom R-scripts provided in the tutorial.
  • Evaluate the applicability of specific tools on different data types and problem settings/contexts.

[BC]² Basel Computational Biology Conference (12-15.09.2017)

Tutorial T7: Single-cell RNA-Seq Analysis

Organizers

Michael Stadler, Atul Sethi, Panagiotis Papasaikas (FMI, Basel)
Vincent Gardeux, Bart Deplancke (EPFL, Lausanne)

Tutorial Summary:

Motivation
Over the last few years, Single-cell RNA-sequencing (scRNAseq) has emerged as a revolutionary tool with the potential to explore biological heterogeneity at the most basic level of organismal organization. Several questions inaccessible in the context of bulk RNAseq can now be addressed or at least probed in a meaningful manner. Examples include the possibility for detailed mapping of the cellular composition of tissue types and identification of novel cell-types, characterization of their individual transcriptomes, discrimination of transcriptional variation arising at the single-cell versus the cell-ensemble level and highlighting the contribution of individual cells to tissue differentiation, development and disease progression.

This promise comes along with multiple technical and computational challenges. While scRNAseq data is structurally similar to bulk RNA-seq, the paucity of starting material combined with multiple confounded sources of variance result in low signal to noise ratio exemplified by high abundance of zeroes in the gene expression matrices. In this new setting existing techniques need to be modified or novel approaches need to be developed for downstream analyses.

A. Tutorial session consisting of two parts:

  1. A general overview of single-cell transcriptomics, and single-cell based sequencing technologies. In particular, we’ll discuss the limitations of bulk workflows that can be overcome with single-cell analyses, as well as the advantages and limitations of single-cell analyses in gathering quantitative data.
  2. A practical session highlighting several of the most critical and common issues associated with the computational analysis of scRNAseq data:
    • Characteristics, comparison and limitations of scRNAseq data generated from the different protocols and commercially available systems
    • Data pre-processing and quality control
    • Data visualization and detection of biologically meaningful subpopulations
    • Differential gene expression
    • Sources of biological and technical variation and circumvention of confounding effects.

This tutorial is designed as a guided conversation through scRNAseq analyses combining lecture and hands-on sessions. It intends to give audience a feel for the data and walk them through major analyses techniques and concepts using illustrative examples and R-scripts that are applicable/extendable to most commonly available types of scRNAseq data.

B. Workshop session consisting of short talks from the members of the 3 groups discussing specific analysis tools for scRNAseq data

Expected Goals

  • Gain basic knowledge about scRNAseq protocols and kind of data produced data by them.
  • Perform basic QC, filtering ( reads, cells, genes ), and normalization of scRNAseq data.
  • Detect possible sources of technical and biological confounding variables (e.g. library complexity, cell cycle, etc.). Apply techniques to remove or account for these confounders in subsequent analyses and evaluate their strengths and weakness.
  • Identify scRNAseq specific challenges in visualization and clustering for subpopulation detection, and population marker identification.
  • Implement aforementioned concepts with practical examples from publicly available scRNAseq datasets using custom R-scripts provided in the tutorial.
  • Evaluate the applicability of specific tools on different data types and problem settings/contexts.

ASAP hands-on session

ASAP

ASAP (asap.epfl.ch) is a web portal for the interactive analysis of single-cell RNA-seq data

  • Provides the Human Cell Atlas (HCA) with a common interface for reproducible and interactive analysis of data
  • Centralized computational resources: Ruby-on-rails server hosted at EPFL + any C/R/Python/Java-implemented tool
  • Job queuing management: delayed-jobs gem
  • Currently ~1900 projects from registered users (~400 registered users)
  • Supported by the Chan-Zuckerberg Initiative (CZI)

Exercise dataset

Please download the dataset for the course: Raw count matrix

Description: Dataset of 91 cells containing 3 different cell types, one of which can be divided in 2 or 3 subpopulations.
Thus, a good PCA/tSNE plot should show 3-4 or better 5 clusters of cells.
Your task is to uncover correctly the different cell types present in the dataset.

DOWNNLOAD: FEATHER FILE