Hi everyone,

I'm analyzing pseudobulk data generated by summing gene expression across cells from different samples profiled with a spatial imaging platform. When I perform PCA on the pseudobulk matrix, PC1 explains an unusually large proportion of the total variance. In addition, all of the PC1 loadings are positive, which I also think is unusual.

Does this indicate a systematic technical bias (I have looked for differences in sequencing depth or cell numbers)? Or are there biological scenarios where this pattern would be expected? These are samples from malignant tissue.

Hi all,

Had an idea recently to do an interaction screen of one of our proteins of interest with proteins expressed in a certain cell type. This is obviously gonna be a large amount of proteins. I’ve seen some papers do similar things, but wanted to ask if anyone had any ideas on these sorts of workflows, specifically with regards to reducing runtimes (and thereby costs)

Specifically:

Any similar models that are significantly faster to run and have a similar accuracy?

How fast is MSA generation generally using sharding. Any other workflows that are significantly faster and still give good MSAs?

Thanks everyone!

I'm running an annotation/enrichment analysis on a GWAS study, and I'm sort of a bit lost/confused on something.

Originally, I only filtered the GWAS study for genome-wide significant variants using a standard p-value threshold, and also used LD-clumping to identify genomic loci.

However, it was also pointed out to me that these variants may not be independent, and that a single association signal may be represented many times, potentially leading to inflation in statistical significance.

I'm sort of unsure how to handle this. I tried a locus-pruning method which gave me way less variants and pretty poor coverage on my study and rendering a lot of my downstream analysis mute. I'm also confused since I haven't seen a lot of similar papers use this kind of filtering method. I did run LDSC too, and most of my findings were insignificant so it did kind of handle the genomic independence part. But I'm still not sure what the best practice is here.

Dear Reddits,

I have some experience with bioinformatics in general (I can open my Linux command line and feel I'm in The Matrix; it is very popular on Instagram, actually), but I don't know anything about modeling. I would like to know if it's SIMPLE (probably not) to determine the potential value (E value) of certain electron transfer proteins. I know AlphaFold can give you some kinda cool model, but I need to know if there is an easy way, or even a way, to just get the E value. Even if it's a not-so-realistic approximation, it would be nice. If it's going to take me more than 1 week, I pass. However, it might be good to know for future endeavors.
That
XOXO

Hi, so I am a undergrad working in a computational biology or molecular biology lab. For next semester my new project is in large part to create reproducible workflows/code and lab manuals for our lab. I taught myself to code and what i have on my laptop is... disorganized to say the least. I should learn how to do this. Currently I largely code using gemini and then tweak anywhere from most of to 25% of the code it writes. I almost always use hard coded paths if i can. Does anyone have any advice for where I could learn something like this, a textbook or website?

For context, my last project was to use AutoDock Vina for screening of 770,000 molecules I carefully downloaded and cleaned from ZINC database to 310,000. This library was based on previous experimental results on a new protien we are targeting in fungi. I also selected a new protien conformation to target based on some major errors in the protien the lab was using and a bunch of literature review. My next step will be to test against Dock6, a diffrent type of scoring algorithm. I wrote all of my own scripts for this and I imagine my first task will be to get them reproducible for another person to use.