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3 materials found

Keywords: Differential expression  or scRNA-seq 

and

Authors: Jared Simpson  or Eija Korpelainen  or Maria Victoria .  or Julie Sullivan  or Friederike Ehrhart  or nekrut  or Michael Love 


Single cell RNA-seq data analysis using Chipster

This course introduces single cell RNA-seq data analysis. It covers the processing of transcript counts from quality control and filtering to dimensional reduction, clustering, and differential expression analysis. You will also learn how to do integrated analysis of two samples. We use Seurat v3...

Keywords: scRNA-seq

Resource type: Slides, Training materials

Single cell RNA-seq data analysis using Chipster https://tess.elixir-europe.org/materials/single-cell-rna-seq-data-analysis-using-chipster This course introduces single cell RNA-seq data analysis. It covers the processing of transcript counts from quality control and filtering to dimensional reduction, clustering, and differential expression analysis. You will also learn how to do integrated analysis of two samples. We use Seurat v3 tools embedded in the user-friendly Chipster software. scRNA-seq Biologists bioinformaticians
Single cell RNA-seq data analysis with Chipster

This course introduces single cell RNA-seq data analysis methods, tools and file formats. It covers the preprocessing steps of DropSeq data from raw reads to a digital gene expression matrix (DGE), and how to find sub-populations of cells using clustering with the Seurat tools. You will also...

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Single Cell technologies, scRNA-seq

Resource type: course materials, Video

Single cell RNA-seq data analysis with Chipster https://tess.elixir-europe.org/materials/single-cell-rna-seq-data-analysis-with-chipster-6cc8f0fb-1c92-444b-ab19-b04fe6454430 This course introduces single cell RNA-seq data analysis methods, tools and file formats. It covers the preprocessing steps of DropSeq data from raw reads to a digital gene expression matrix (DGE), and how to find sub-populations of cells using clustering with the Seurat tools. You will also learn how to compare two samples and detect conserved cluster markers and differentially expressed genes in them. The user-friendly Chipster software is used in the exercises, so no Unix or R experience is required and the course is thus suitable for everybody. Eija Korpelainen RNA-Seq RNA-Seq, Single Cell technologies, scRNA-seq Biologists bioinformaticians
RNA-seq data analysis: from raw reads to differentially expressed genes

This course material introduces the central concepts, analysis steps and file formats in RNA-seq data analysis. It covers the analysis from quality control to differential expression detection, and workflow construction and several data visualizations are also practised. The material consists of...

Scientific topics: Sequencing, RNA, Data architecture, analysis and design, Bioinformatics

Keywords: Bioinformatics, Differential expression, Ngs, Rna seq

RNA-seq data analysis: from raw reads to differentially expressed genes https://tess.elixir-europe.org/materials/rna-seq-data-analysis-from-raw-reads-to-differentially-expressed-genes This course material introduces the central concepts, analysis steps and file formats in RNA-seq data analysis. It covers the analysis from quality control to differential expression detection, and workflow construction and several data visualizations are also practised. The material consists of 10-30 minute lectures intertwined with hands-on exercises, and it can be accomplished in a day. As the user-friendly Chipster software is used in the exercises, no prior knowledge of R/Bioconductor or Unix ir required, and the course is thus suitable for everybody. Our book RNA-seq data analysis: A practical approach (CRC Press) can be used as background reading. The following topics and analysis tools are covered: 1. Introduction to the Chipster analysis platform 2. Quality control of raw reads (FastQC, PRINSEQ) 3. Preprocessing (Trimmomatic, PRINSEQ) 4. Alignment to reference genome (TopHat2) 5. Alignment level quality control (RseQC) 6. Quantitation (HTSeq) 7. Experiment level quality control with PCA and MDS plots 8. Differential expression analysis (DESeq2, edgeR) -normalization -dispersion estimation -statistical testing -controlling for batch effects, multifactor designs -filtering -multiple testing correction 9. Visualization of reads and results -genome browser -Venn diagram -volcano plot -plotting normalized counts for a gene -expression profiles 10. Experimental design Sequencing RNA Data architecture, analysis and design Bioinformatics Bioinformatics, Differential expression, Ngs, Rna seq Bench biologists Life Science Researchers 2015-12-04 2017-10-09