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Topic brainstorm #2

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BrunoGrandePhD opened this issue Oct 20, 2017 · 7 comments
Open

Topic brainstorm #2

BrunoGrandePhD opened this issue Oct 20, 2017 · 7 comments
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@BrunoGrandePhD
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BrunoGrandePhD commented Oct 20, 2017

Comment below with topics that you would like to see included in this set of intermediate/advanced R tutorials for genomic data analysis. You don't need to know the topic, because someone else might be able to write the tutorial. Also, a topic can be anything in R that can be used for genomic data analysis, e.g. an R package.

We'll then create separate GitHub issues for each topic that will get assigned to someone.

@BrunoGrandePhD
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Here are a few R packages that I think deserve a genomic-focused tutorial.

  • Shiny for interactive visualization in genomics
  • data.table for efficiently manipulating large datasets
  • ggplot2 for visualizing genomic data
  • R Markdown for generating self-contained reports

@zhenyisong
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These are tools used in our project. But I am not sure what is biological story? RNA (mRNA or lncRNA)? ChIP-seq (Broad or narrow?) ? Whole Genome Analysis? Or cover all these issues?

@privefl
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privefl commented Oct 20, 2017

Does there exist a dataset with all these data (for example, trying to combine all these information)?

@BrunoGrandePhD
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Feel free to continue posting ideas of topics that should be converted into genomics-focused tutorials.

Once you're interested in developing a tutorial on one of the suggested ideas (or one of your own), create a new issue here. I'll be posting contribution guidelines shortly.

@BrunoGrandePhD
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Check out the contributing guidelines here:
https://github.com/hackseq/2017_project_5/blob/master/contributing.md

@BrunoGrandePhD
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Here are some additional ideas:

  • purrr for functional programming
  • sva for batch effect correction (despite being on Bioconductor, the vignette isn't that good)
  • Any number of unsupervised or supervised machine learning or clustering packages
  • Gene set enrichment analysis (despite there being multiple package for this, I always have a hard time to find a simple tutorial on how to do this)
  • feather for fast data frame reading and writing

@zhenyisong
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@BrunoGrande if sva and/or supervised learning, then choosing the dataset is picky. GSEA, I prefer using clusterProfiler.

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