Hello there,
I am working on a cancer research project where we are trying to understand the relationship between genetic dependencies and gene expression profiles across different cancer cell lines. We have been utilizing RNA seq data to analyze gene expression patterns; but we are interested in integrating this data with genetic dependency information from DepMap to uncover potential therapeutic targets.
I have been exploring the DepMap portal and datasets; but I am relatively new to bioinformatics and could use some guidance on a few points.
What are the best practices for integrating DepMap genetic dependency data with RNA seq gene expression data? Are there specific tools or pipelines that can facilitate this process; or would it be more effective to handle this integration manually? 
How should we handle normalization and scaling differences between RNA-seq data and genetic dependency scores to ensure that the integrated data is comparable and meaningful?
Once the data is integrated, what are some recommended approaches for interpreting the relationship between genetic dependencies and gene expression? Are there any specific statistical methods or visualization techniques that are particularly useful in this context?
Also, I have gone through this post; https://forum.depmap.org/t/using-depmap-transcriptomic-data-to-select-a-cell-line-where-a-gene-is-not-expressed-aws-devops/ which definitely helped me out a lot.
If possible; could you point me to any case studies or research papers that have successfully integrated DepMap data with RNA seq data? It would be incredibly helpful to learn from established methodologies and see how others have approached similar challenges.
Thank you in advance for your help and assistance. 
