scRNA-seq Workshop. An introduction to single cell RNA-seq data analysis
Location
Date
28 - 30 September 2022
Deadline
10/09/2022
General Chairs
Raffaele Calogero, Francesca Cordero, Marco Beccuti
Contact
Aims and Objectives
At the end of the course, you will be able to:
- understand the importance of experimental design to ask sensible biological questions at single cell level.
- Assess the quality of your data.
- Understand limits and strength of data reduction and clustering in scRNA-seq.
- Identify genes driving cluster formation in scRNA-seq.
- Extract biological knowledge from cluster-specific biomarkers.
Audience
This course is suitable for biologists who are new to single cell gene expression technology. Knowledge of statistics as well as advance computing skills are not necessary prior to attending the course.
Course Description
Tools for scRNA-seq data analysis
The course is based on the use of open-source software solutions. scRNAseq analyses will be performed using the tools available as part of the Reproducible Bioinformatics Project (http://reproduciblebioinformatics.
org/): rCASC
Experimental design
This section of the course discusses several criteria and principles of experiment design as well as related problems. Questions such as
i. which are the minimal requirements for a single cell RNA-seq experiment
ii. when a scRNA-seq is preferred to a bulk RNA-seq analysis
iii. how to structure a successful scRNA-seq experiment
will be addressed.
Quality control
This section will focus on quality controls for single cells sequence outputs. Approaches to check the quality of raw data will be presented as well as approaches to identify sequencing bias. All approaches will be practically tested on real data provided during the practical training sessions.
Data analysis theoretical knowledge
This part will provide the biologist with a general overview on the theory behind the computing tools used in single cell RNA-seq data. The purpose is to give only as much information as needed to be able to make an informed choice during the subsequent data analysis. The aim of the training module is to put things in the perspective of someone who analyzes single cell RNA-seq data, rather than offer a full treatment of the respective statistical/bioinformatics notions and techniques. No previous statistical knowledge is assumed.
Clustering single cell RNA-seq data
This section presents several data reduction and clustering methods used to depict the cell sub-populations present in a single cell experiment. The advantages and disadvantages of all methods are discussed in detail.
Extracting biological knowledge from bulk and single cell RNA-seq data
This session will focus on the extraction of biological knowledge from cluster’s data using tools like Sparsely-connected-autoencoders and omicsnet. Hierarchical clustering will be also used as tools to understand samples heterogeneity given a specific gene set signature.
Practical sessions
The course is structured to provide practical analysis skills to the students.
Datasets will be provided by Organisers.
Participants will have access to the computing infrastructure (https://hpc4ai.it/) for seven day after the end of the course. Special renting rate will be offered to participants to access to the scRNAseq computing infrastructure, for further info inquire to
raffaele.calogero@unito.it.
Dates Times and Locations
The RNA-seq workshop will last 3 days, in September 2022.
Day 1 28th September 9:00-17:00
Day 2 29th September 9:00-17:00
Day 3 30th September 9:00-17:00
Costs
The course is open to max 20 people. The registration to the course costs 220 euro and covers lunches, coffee breaks and social dinner.
Each participant should bring his/her own laptop, in case of need the organization can provide laptops.
For contact: raffaele.calogero@unito.it
Registration form: https://bit.ly/3qIzKNP
At the end of the course, you will be able to:
- understand the importance of experimental design to ask sensible biological questions at single cell level.
- Assess the quality of your data.
- Understand limits and strength of data reduction and clustering in scRNA-seq.
- Identify genes driving cluster formation in scRNA-seq.
- Extract biological knowledge from cluster-specific biomarkers.
Audience
This course is suitable for biologists who are new to single cell gene expression technology. Knowledge of statistics as well as advance computing skills are not necessary prior to attending the course.
Course Description
Tools for scRNA-seq data analysis
The course is based on the use of open-source software solutions. scRNAseq analyses will be performed using the tools available as part of the Reproducible Bioinformatics Project (http://reproduciblebioinformatics.
org/): rCASC
Experimental design
This section of the course discusses several criteria and principles of experiment design as well as related problems. Questions such as
i. which are the minimal requirements for a single cell RNA-seq experiment
ii. when a scRNA-seq is preferred to a bulk RNA-seq analysis
iii. how to structure a successful scRNA-seq experiment
will be addressed.
Quality control
This section will focus on quality controls for single cells sequence outputs. Approaches to check the quality of raw data will be presented as well as approaches to identify sequencing bias. All approaches will be practically tested on real data provided during the practical training sessions.
Data analysis theoretical knowledge
This part will provide the biologist with a general overview on the theory behind the computing tools used in single cell RNA-seq data. The purpose is to give only as much information as needed to be able to make an informed choice during the subsequent data analysis. The aim of the training module is to put things in the perspective of someone who analyzes single cell RNA-seq data, rather than offer a full treatment of the respective statistical/bioinformatics notions and techniques. No previous statistical knowledge is assumed.
Clustering single cell RNA-seq data
This section presents several data reduction and clustering methods used to depict the cell sub-populations present in a single cell experiment. The advantages and disadvantages of all methods are discussed in detail.
Extracting biological knowledge from bulk and single cell RNA-seq data
This session will focus on the extraction of biological knowledge from cluster’s data using tools like Sparsely-connected-autoencoders and omicsnet. Hierarchical clustering will be also used as tools to understand samples heterogeneity given a specific gene set signature.
Practical sessions
The course is structured to provide practical analysis skills to the students.
Datasets will be provided by Organisers.
Participants will have access to the computing infrastructure (https://hpc4ai.it/) for seven day after the end of the course. Special renting rate will be offered to participants to access to the scRNAseq computing infrastructure, for further info inquire to
raffaele.calogero@unito.it.
Dates Times and Locations
The RNA-seq workshop will last 3 days, in September 2022.
Day 1 28th September 9:00-17:00
Day 2 29th September 9:00-17:00
Day 3 30th September 9:00-17:00
Costs
The course is open to max 20 people. The registration to the course costs 220 euro and covers lunches, coffee breaks and social dinner.
Each participant should bring his/her own laptop, in case of need the organization can provide laptops.
For contact: raffaele.calogero@unito.it
Registration form: https://bit.ly/3qIzKNP
