If you are looking for tools to carry out differential gene expression analysis, check out my top gene expression analysis tools and bioinformatic resources.
If you are new here, don’t forget to check out Biostatsquid’s guided tutorials and step-by-step guides for differential gene expression data analysis. You might also want to check my Youtube channel to follow coding tutorials and explanations with me!
Here are some of my favourite resources that really helped me when analysing gene expression data!
Gemma
Gemma is a great platform which offers tools and database for ‘meta-analysis of functional genomics data’. Basically, you can search for genes, experiments, phenotypes, gene expression datasets and much more! Worth taking a look.
If you are have analysed gene expression data before, you have probably heard of Gene Expression Omnibus or GEO. GEO is a public functional genomics data repository containing datasets, profiles and analysis for many gene expression analysis! Definitely worth checking out.
ImmGen
To be honest, I had never used ImmGen before writing this post, but it’s really good! Here you can find gene expresion profiles for mouse and human samples, datasets, population comparisons, interactive displays of the modules of co-regulated genes across immunological cell-types and much more!
ArrayExpress
ArrayExpress stores data from high-throughput functional genomics experiments (so basically gene expression data) which is available to re-use:)
Stemformatics
Stemformatics is a great webpage to visualise gene expression datasets. You will find a number of ways to explore the data, as well as many intuitive tools for visualising the data if you are not ready to code yet or just want to get a nice graphical overview.
What did you think of these gene expression analysis resources and tools?
Are there any other resources you would like to see here?
If you have recommendations on more bioinformatic tools for beginners, intermediate or advance levels, leave a comment in the comments section below so I can add it to the list:)
Hi! I use Gene Pattern to do GSEA analysis frequently. How does this compare to the tools you provided? Is there a better platform you recommend.
Hi sorry, I haven’t used Gene Pattern but I think that is a more user-friendly option if you don’t know how to code. However, if you know (or are willing to learn!) some R basics, I definitely recommend gsea package:)