# What is the relationship between differential analysis and hierarchial clustering?

I'm currently in an internship for R bioinformatics, where I'm writing software for single-cell RNA sequencing analysis. We're looking for differentially expressed genes between groups, but I don't understand the current process on exactly how to do this.

Right now, I have my current data: I have over 20,000 samples across about 850 genes, and I don't have any pre-determined or attached grouping data to go along with it.

I don't understand exactly how to begin the analysis or what the process might look like. I do have some useful packages like DESeq2 and NMF to help me, but I'm having a hard time understanding the relationship between differential analysis and hierarchical clustering.

From what I understand, differential analysis focuses on finding differences between groups... However, my data lacks all grouping information; I've been told to use NMF, but that causes me problems with memory usage and such, and I don't understand exactly how it works...

Hierarchical clustering sounds similar to differential analysis in terms of how it categorizes things, but I was told it was not very robust and trustworthy.

I feel like I need to ask a deeper question, but I don't know much about statistics, period. I'm just a simple freshmen/sophomore in college, and I'm clueless. Can anyone give me a hand?