Topic brainstorm - Githubissues

hackseq / 2017_project_5

Developing advanced R tutorials for genomic data analysis

https://hackseq.github.io/2017_project_5/

MIT License

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Topic brainstorm #2

Open BrunoGrandePhD opened 6 years ago

BrunoGrandePhD commented 6 years ago

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 commented 6 years ago

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 commented 6 years ago

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 commented 6 years ago

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

BrunoGrandePhD commented 6 years ago

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 commented 6 years ago

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

BrunoGrandePhD commented 6 years ago

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 commented 6 years ago

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