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Statistical Analysis of Differential expression
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Interactive Online Companionship (IOC)
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STARTbio
ARTbio/startbio
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Interactive Online Companionship (IOC)
Interactive Online Companionship (IOC)
IOC on bulk RNAseq analysis
IOC on bulk RNAseq analysis
Provisional Program / Schedule
Week 0
Week 0
Introduction - Week-0
Week 0 exercises
Week 1
Week 1
Review on week-0 work
Analysis Overwiew
Analysis Overwiew
The Key idea
RNAseq librairies
RNAseq librairies
cDNA synthesis
Inserts and sequencing strategies
Sequence Quality and Read Filtering
Transcript Quantification
Statistical Analysis of Differential expression
Understanding of all your experimental procedures
Week 1 exercises
Week 1 exercises
Use case PRJNA630433
Use case PRJNA630433
Presentation
Data upload
Quality Control
Week 2
Week 2
Review on week-1 work
Read Mapping overview
Read Mapping overview
Reference genomes
Aligners
Splice-aware alignment
Week 2 exercices
Week 2 exercices
Library strandness
HiSAT2
STAR
UCSC visualisation
Week 3
Week 3
Review on week-2 work
Counting reads or fragments
Week 3 exercices
Week 3 exercices
FeatureCounts
HTSeq_counts
Count with STAR
Week 4
Week 4
Review on week-3 work
Differential Gene Expression Analysis
Week 4 exercices
Week 4 exercices
DESeq2
edgeR
limma
DE_manipulations
DE_manipulations
DESeq2
edgeR
limma
DEseq/edgeR/limma comparison
Volcano plots
Week 5
Week 5
Review on week-4 work
Gene Ontology Enrichment Analysis
Week 5 exercices
Week 5 exercices
GOseq
Week 6
Week 6
Review on week-5 work
Gene Set Enrichment Analysis (GSEA)
Week 6 exercices
Week 6 exercices
Intro to week 6 exercices
fGSEA
EGSEA
Week 7
Week 7
Review on week-6 work
Galaxy Workflows
Galaxy Workflows
Introduction
Week 7 exercices
Week 7 exercices
Build your workflow
Week 8
Week 8
Review on week-7 work
Recap
20min presentations
20min presentations
Trainee 1
Trainee 2
Trainne 3
External Materials
IOC on R
IOC on R
Provisional Program
Provisional Program
Schedule
Week 0 - Introduction
Week 1 - First steps
Week 2 - Learning vectors and more
Week 3 - Learning Lists
Week 4 - Two-dimensional objects
Week 5 - Level up your code
Week 6 - Tidyverse
Week 7 - Data visualisation
Reference Manual
Reference Manual
Variables
Vectors
Operators
Functions
Best Practices
Lists
Data Frames and Matrices
Data Import and Export
Apply & Co
Conditions
Manipulating data with tidyverse
Visualization
Storage room
IOC on Single-cell RNAseq analysis using Seurat in R
IOC on Single-cell RNAseq analysis using Seurat in R
Program / Schedule
Program / Schedule
Schedule
Week 0 - Introduction, Data Import & Annotation
Week 1 - Data Processing & High Variable Genes
Week 2 - Reduction of Dimensionality
Week 3 - Clustering
Week 4 - Differential Gene Expression
Week 5 - Enrichment Analysis (ORA)
Week 6 - Enrichment Analysis (GSEA)
Week 7 - Visualisation
Reference Manual
Reference Manual
Introduction
Prerequisites
Initialization of R analysis
Import data
Preprocessing
Reduction of dimensionality
Clustering
Cell population identification
Cell population identification
Introduction and markers identification
Markers Annotation
Markers Annotation
Presentation of annotation methods
Over-representation analysis
Gene Set Enrichment Analysis
Cluster annotation
Comparison between 2 populations
Differents visualizations of Seurat
References
Introduction to Deep-seq Analysis
Introduction to Deep-seq Analysis
INTRODUCTION
Illumina sequencing
Pacific Biosciences SMRT
Oxford Nanopore MinION
Sequencing facts
Small RNA library
RNA library
ChIP-seq library
Deep Sequencing Applications
Analysis Flowchart:
Various aspects of sequence data mining
Pratical part: Command lines
Align reads with bowtie
To be continued... with Galaxy
BOWTIE ALIGNMENT USING galaxy
1Omin2
1Omin2
Grep and Regex (1)
Python Decorators
Galaxy Trainings
Galaxy Trainings
Analyse des Génomes (2023)
Analyse des Génomes (2023)
PLAN
START A VIRTUAL MACHINE IN GOOGLE CLOUD ENGINE
DEPLOY A GALAXY SERVER IN THE VM
INSTALL GALAXY TOOLS
MANAGE YOUR GOOGLE VM
MANAGE YOUR GALAXY SERVER
LOAD TRAINING DATA
PREPARE A REFERENCE GENOME
Format conversion using command lines in your Google VM
Format conversion using a galaxy tool
Running a galaxy workflow
Why administering galaxy ?
Spin off a VM with your Google Cloud Account
EMERGENCY image
SUP MATERIAL
SUP MATERIAL
BOWTIE ALIGNMENT USING COMMAND LINES
BOWTIE ALIGNMENT USING GALAXY
SHARE VM IMAGE WITH CLASS STUDENTS
Run your Galaxy server
Run your Galaxy server
INTRODUCTION
STANDALONE GALAXY IN GCP
STANDALONE GALAXY IN IFB CLOUD
AFTER DEPLOYMENT
INSTALL GALAXY WITH ANSIBLE
LOAD INPUT DATA
PREPARE A REFERENCE GENOME
TEST YOUR GALAXY INSTANCE
IN CASE OF EMERGENCY
ADMIN TOOL KIT
Appendix 1: Getting a Google Cloud Engine Account
Appendix 2: Start and stop a Google virtual machine
Appendix 3: http access to the IFB cloud
Appendix 4: Galaxy software architecture
Appendix 5: Install a Galaxy server with Docker
Run a Galaxy workflow for somatic mutation detection in cancers
Run a Galaxy workflow for somatic mutation detection in cancers
INTRODUCTION
Search for chomosome translocations
Search for chomosome translocations
Introduction
Sequencing Protocol
Lumpy approach
Trainee connection
Prepare the input data history
Align reads to the human genome
lumpy analysis
Reformat VCF files for visualization in the UCSC genome browser
Compute the genome read coverage
Data Visualisation in UCSC Genome Browser
Generate workflow from an history
Run the Lumpy workflow
Reference-based RNAseq analysis
Reference-based RNAseq analysis
Introduction
Pretreatments
Pretreatments
Data upload
Quality control
Outline and general concepts for the RNAseq analysis
Outline and general concepts for the RNAseq analysis
RNAseq librairies - cDNA synthesis
RNAseq librairies - Inserts and sequencing strategies
The key idea in Reference-base Expression analysis
Reflecting on quality control & “filtering” in RNAseq analysis
Transcript Quantification
Statistical Analysis of Differential expression
Experimental procedures affect downstream analyses
Quality filtering
Quality filtering
Optional filtering of reads on sequence quality
Mapping
Mapping
Mapping
RNA STAR
HiSAT2
BAM inspection
Visualisation of read mapping
Analysis of the differential gene expression
Analysis of the differential gene expression
Introduction to read counting
Estimation of the strandness
Count the number of reads per annotated gene
Identification of the differentially expressed features - DESeq2
DESeq2 use
Volcano Plot
Visualization of the differentially expressed genes
Analysis of functional enrichment among the differentially expressed genes
Analysis of functional enrichment among the differentially expressed genes
Introduction
GO
Galaxy Workflows
Galaxy Workflows
Introduction
Example
Viruses Metagenomics
Viruses Metagenomics
Metavisitor
Prepare Metavisitor Galaxy instance for analyses
Prepare input data histories
Use Cases 1-1 to 1-4
Use Cases 2-1 and 2-2
Use Case 3-1
Use Case 3-2
Use Case 3-3
Install_Metavisitor
Install_Metavisitor
Intro
GalaxyKickStart
Docker
Access and Control Metavisitor Galaxy instance
Download TARGET-AML Data in Galaxy
Download TARGET-AML Data in Galaxy
Introduction
Fill your GDC cart
Edit files metadata
import files in Galaxy using the edited files metadata
Code Snippets
Code Snippets
Terminal Bash
Buid your JupyterHub and user kernel
The markdown emoji resource
Links
Statistical Analysis of Differential expression
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Statistical Analysis of Differential expression¶
Statistical Analysis of Differential expression¶