last semester, we've learn how to analyse date by R on our bioinformatics class. Our class basically focused on the idea of analysing the data of multi-mics, my classmates have already choosed the topic of PCA, PLS, tSNE and WGCNA. Does anyone could suggest me a good topic according to this? I do really have trouble in choosing things, this presentation is so essential for me. Please guys, I am kind of exhausted. THX!
This thread may get closed because you have shown relatively little effort.
But before it does, I think it's important that you identify part of bioinformatics, or part of the R/bioconductor ecosystem that is important/interesting to you. It will be extremely hard for you to write an interesting talk on something unless that something sparks an interest for you.
Could you expand upon the purpose of the talk and give an outline of what the expected contents of the talks should be:
are you to present a data-analysis of your own,
are you to summarise a data-analysis method,
are you to able to discuss the software design / architecture underpinning this stuff,
are you able to discuss the social aspects or the publication aspects of coding and releasing packages.
You should take a step back and think about what the challenges in bioinformatics are in 2020, and (based on your question) how does R solve those problems. For me the biggest challenge is reproducibility.
I'm not the best source, as I'm behind on most of the active research, but I remember the feeling. For beginners it's not so easy to separate the wheat from the chaff, imo this would be your teacher's responsibility, though.
Let me give it shot: for data analysis milestones comparable with what you mention are
- short read alignment using burrows wheeler transformation, eg. bwa-mem or bowtie2.
- RNAseq analysis: DESeq2. EdgeR, and limma/voom for data analysis, STAR for alignment
None of this is trivial, though, but neither is PCA/PLS/tSNE. Good luck.