4/ Applied Bioinformatics buff.ly/3EM7Ibd by Istvan, the creator of biostars buff.ly/3EI2hKk. I've written a ChIP-seq chapter for biostars, and grab the PDF for free at buff.ly/3Z5daxT
5/ Introduction to Bioinformatics and Computational Biology buff.ly/3kyUyri by
Shirley Liu.
I am glad to contribute a little myself.
6/ data carpentry workshops buff.ly/3Y29lZ3 I am honored to serve as the genomics curriculum committee chair
Spatial transcriptome is the next wave after single-cell RNAseq. Resources to bookmark to get into the field π π§΅
1/ 8 Review papers:
* [The emerging landscape of spatial profiling technologies](buff.ly/3cwcApw)
* [The expanding vistas of spatial transcriptomics](buff.ly/3m1x9zb)
* [Exploring tissue architecture using spatial transcriptomics](buff.ly/3Sq7Z9f)
2/ [Statistical and machine learning methods for spatially resolved transcriptomics data analysis](buff.ly/3Ew47hx).
* [Spatial omics and multiplexed imaging to explore cancer biology](buff.ly/3koxYkU)
Git is the most popular tool for version control your source code.
But the learning curve can be steep.
Here are the 10 tips for you 𧡠π along with learning resource links.
compiled at buff.ly/3Jqxh4W
1/ Several basic commands will serve you a long way:
git clone
git add
git commit -m
git push
Those are enough to get you started. To be honest, those are still the most frequent commands I use.
2/ understand git and github. You use git to track files locally, and github can host your repos. You can start with the github skill page buff.ly/3tO2iaf
gitlab buff.ly/3JlGA69 is an alternative to github
1/ If someone tells you that you can learn computation overnight, it is a lie. What I wish I had known 10 years ago with resource linksπ𧡠#rstats#computationalbiology
2/ Spend time learning Linux commands. Fancy tools can become obsolete tomorrow, and Linux skills persist.
I started with this book linuxcommand.org/tlcl.php#unix
3/ Learn a language and learn it well. It does not matter if you pick R or python. If you can use it to solve your practical problems in the lab, that will be good. There is so much data wrangling to do, start with r4ds.had.co.nz#rstats