Hello
First I will that confess I'm new to this area, I have experience in different ML supervised techniques but it's my first project in unsupervised learning.
I'm working with sc rna data set, relatively sparse and noisy (compared to non-biologic data sets I've seen).
My task is to create hierarchical clustering of the genes (by their count in the different cells).
I searched through awesome-single-cell, scrna tools and omni tools
All I found was clustering method for typing the different cells.
I tried to work with some basic methods like gmm or k-means with basic data imputations and got to mediocre results with long running times, I'm sure there has been some research in this field and there is no need for me to invent the wheel here.
I'm looking for some articles/tools/pieces of code or even some keywords that worth searching to find something relevant to this task.
Thanks in advance.
Moshe.
If you are working on scRNA data, it is highly recommended that you check Seurat PBMC tutorial https://satijalab.org/seurat/pbmc3k_tutorial.html
After you load the data in Seurat and do the normalization and scaling, you may create clustering heatmap by any tool, say using heatmap.2 https://www.rdocumentation.org/packages/gplots/versions/3.0.1/topics/heatmap.2
Thanks alot and sry for the very delayed reply. I did this tutorial and also searched online but I have failed to understand how can I utilize Seurat to create clusters classifying the genes, maybe it has a special name to so I miss it in my searches?
Thanks!