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 π§΅ 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 linksππ§΅ #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