Register training material
5 materials found

Keywords: Exploratory-analysis  or metagenomics 

and

Authors: Charlotte Soneson @charlott...  or Eija Korpelainen @eija, eko... 


Material for the course RNA-seq data analysis with Chipster

This material covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis.

Scientific topics: RNA-Seq

Keywords: RNA-Seq, FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis

Material for the course RNA-seq data analysis with Chipster https://tess.elixir-europe.org/materials/material-for-the-course-rna-seq-data-analysis-with-chipster This material covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis. RNA-Seq RNA-Seq, FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis
Material provided by Charlotte Soneson

This folder contains material provided by Charlotte Soneson. The following material is included:

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Differential-expression, Statistical-model, Exploratory-analysis

Material provided by Charlotte Soneson https://tess.elixir-europe.org/materials/material-provided-by-charlotte-soneson This folder contains material provided by Charlotte Soneson. The following material is included: RNA-Seq RNA-Seq, Differential-expression, Statistical-model, Exploratory-analysis
Exercises for the course RNA-seq data analysis with Chipster

This practical covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis, using the free Chipster software. Material updated in Dec 2015.

Keywords: FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis

Exercises for the course RNA-seq data analysis with Chipster https://tess.elixir-europe.org/materials/exercises-for-the-course-rna-seq-data-analysis-with-chipster This practical covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis, using the free Chipster software. Material updated in Dec 2015. FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis
Lecture slides for the course RNA-seq data analysis with Chipster

This material covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis. It discusses also experimental design. Material updated in Dec 2015.

Keywords: FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis

Lecture slides for the course RNA-seq data analysis with Chipster https://tess.elixir-europe.org/materials/lecture-slides-for-the-course-rna-seq-data-analysis-with-chipster This material covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis. It discusses also experimental design. Material updated in Dec 2015. FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis
Exploratory analysis and downstream analysis

This lecture gives an overview of exploratory analysis (clustering) and supervised analysis (prediction/classification), as well as visualization methods (heatmaps/PCA) and gene set analysis. It also shows how to transform count data to make it more suitable to apply the traditional methods...

Keywords: Statistical-model, Exploratory-analysis

Exploratory analysis and downstream analysis https://tess.elixir-europe.org/materials/exploratory-analysis-and-downstream-analysis This lecture gives an overview of exploratory analysis (clustering) and supervised analysis (prediction/classification), as well as visualization methods (heatmaps/PCA) and gene set analysis. It also shows how to transform count data to make it more suitable to apply the traditional methods developed (e.g.) for microarray data. Statistical-model, Exploratory-analysis