The Best Methods for Handling Massive Gene Expression Data

Technical
1

Hello there,

I am working on analyzing large scale gene expression data from the Allen Brain Atlas & i m Facing some challenges with data processing especially when handling such a massive dataset.

I am using Python for my analysis, primarily leveraging libraries like Pandas & NumPy but the sheer volume of data is causing performance issues; Also I have tried breaking the data into smaller chunks and using parallel processing but I am still facing significant slowdowns.

I want to hear from anyone who has experience working with similar large scale datasets. What are some best practices or tools you have found useful for optimizing performance during data preprocessing and analysis?

Also i have read this resorse/artical; https://community.brain-map.org/t/what-is-the-format-of-gene-expression-value-in-allen-human-brain-atlassf-cpq
but have not found any solution still need your advice. if you have any advice please share with me

My last ques is… Do you have any specific recommendations for methods of optimizing memory or accelerating computations? :thinking:

Thank you!

2

I don’t work with python, but to direct your question to the right people: can you clarify which atlas data you are using (e.g., original mouse brain atlas, single cell/nucleus RNA-seq, something else). I’d also encourage other folks from the community to share their insights!