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Difficulty level: Intermediate 

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Target audience: life scientists  or Biologists 

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Contributors: Björn Grüning  or Manimozhiyan Arumugam  or Celia van Gelder  or Marek Vrbacky  or Nils Peder Willassen  or Marek Suchanek  or Jalview Coordinator  or Derek Wright  or Juha Tornroos  or Eija Korpelainen @eija, eko... 


Single cell RNA-seq data analysis with R

This international hands-on course covers several aspects of single cell RNA-seq data analysis, ranging from clustering and differential gene expression analysis to trajectories, cell type identification and spatial transcriptomics. The course is kindly sponsored by the ELIXIR EXCELERATE...

Scientific topics: RNA-Seq

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

Resource type: course materials

Single cell RNA-seq data analysis with R https://tess.elixir-europe.org/materials/single-cell-rna-seq-data-analysis-with-r-26ec3ec0-8f43-47db-9788-7f8f63eb447b This international hands-on course covers several aspects of single cell RNA-seq data analysis, ranging from clustering and differential gene expression analysis to trajectories, cell type identification and spatial transcriptomics. The course is kindly sponsored by the ELIXIR EXCELERATE project. Note: You can find all the course material including the R code and data files in the course [GitHub](https://github.com/NBISweden/excelerate-scRNAseq) repository, and the lecture videos are available as a [YouTube playlist](https://www.youtube.com/playlist?list=PLjiXAZO27elC_xnk7gVNM85I2IQl5BEJN). Eija Korpelainen @eija, ekorpelainen@gmail.com RNA-Seq RNA-Seq, Single Cell technologies, scRNA-seq bioinformaticians Biologists
Text-mining exercises

Hands-on exercises using a variety of text-mining tools and databases based on text mining, to interpret the results from microbiome studies.

Scientific topics: Data mining, Natural language processing, Metagenomics, Microbial ecology

Text-mining exercises https://tess.elixir-europe.org/materials/text-mining-exercises Hands-on exercises using a variety of text-mining tools and databases based on text mining, to interpret the results from microbiome studies. Manimozhiyan Arumugam Data mining Natural language processing Metagenomics Microbial ecology Bioinformaticians Biologists