I have scRNA sequencing data and I want to perform differential gene expression in macrophages. However, I want to include bioinformatics that could complement my studies. Is there any tool that I can include?
After performing all analyses regarding scRNA-seq, you can use other studies from a peer-reviewed journal and relate them to your subject to validate what you find, such as differences between treatments, the relationship between genes (co-expression) or enrichment to a specific pathway. To combine all these data, you can use any programming language or look for integrative tools.
There are several tools that you can use to perform differential gene expression analysis on your scRNA-seq data. Some popular options include:
DESeq2: This is a widely used tool for differential gene expression analysis of scRNA-seq data. It is available as an R package and has a user-friendly interface.
EdgeR: This is another popular tool for differential gene expression analysis of scRNA-seq data. It is also available as an R package.
limma: This is a tool for linear modeling of gene expression data and is particularly well-suited for scRNA-seq data. It is available as an R package.
MAST: This is a tool for flexible statistical analysis of scRNA-seq data. It is available as an R package.