u/_DataFrame_

There is interest from me at least

I thought this wasn't a war?

Could this be done with an AVB interface? Like the MOTU stuff? I know you can transfer one way but idk about both ways.

For what it's worth, they decrease general inflammation and make you likelier to live longer, even without weight loss. You're not wrong that being overweight or obese increases the likelihood for tons of health issues but with GLP-1R agonists, it's more complicated than that.

Source: an expert in this field who is working to invent the next generation of drugs

Same here. No configuration, works great.

Do you have info on how the time and memory requirements scale with the matrix size? I have a new RNAseq project I'd like to try it on, but some of the objects are about 1M x 20k.

It probably matters a lot what packages you're using. As a counterpoint, I have spent so much more time dealing with packages in Python than R.

Agreed that R package management is chaos but for some reason, it always just works for me without environments. With Python, I have wasted so much of my life having to deal with just the "package X was compiled for Numpy version Y" error alone it made me hate it.

Though I think my hatred is mainly just for Conda. I've worked using uv a few times but not enough to judge

I'd try hdWGCNA (if using R). Run it per cell type, not per cluster. It will find modules of genes that generally move together, regardless of WT or KO status. Use the DME analysis to determine gene modules that significantly differ between WT and KO. Then run the top 100-200 genes through either GSEA or ORA.

I never get much use out of normal GO stuff like biological function or cellular compartment but I like the pathway stuff like KEGG. Or put your genes into PerturbSeqr and look at what drugs, knockouts, etc. mimic or anti-mimic (can't remember the term they use) your gene module.

Labyrinthis Stellarum

Agreed with others, this responsibility falls a lot on your PI.

But, for this purpose, it doesn't hurt to try a deep search with Perplexity (or one of the alternatives, I just haven't used them).

The hope would be that an AI model would be able to recognize differing terminology and still give you the right paper.

I used Perplexity early on and it was terrible. It kept inventing papers that didn't exist. It has gotten a lot better now and is generally trustworthy. As always, you'd still need to read the actual paper and verify.

You need some speakers. You won't hear anything until you get them

Oh wow, I already really wanted to see it but the RE7 reference doubles it.

I use Linux and FOSS tools for everything too but I think it's pretty silly to expect everyone to commit the time and effort to do that. I think the reality of the situation is that people would mess their stats up even worse when suddenly they have 10x options and potential failure points along the way.

It's pretty shameful that neither the university or PI pays for licenses for the basic software that 95% of scientists use every day.

I have never worked anywhere where either the university or lab didn't provide all

What? Who's afraid of email?

Of course you're not the PI? Do you mean first author? The PI secures the funding and runs the lab.

• Tokyo Gore Police (not quite horror but absolutely weird and is an amazing practical effects gore showcase)
• The Wolf House - what if an entire, full size house along with terrifying paper mache people were used as the medium for a stop motion film inspired by a nazi pedophile commune?
• Another vote for The Greasy Strangler, Baskin, and Abruptio

Find a better way to deal with the stress. Get therapy so things wouldn't be so self-destructive.

ggplot

OP, I had this exact same situation and got the postdoc despite the fact that the PI wasn't looking for another lab member and the fact that I'm not very impressive on paper.

Cold emailed one of the biggest names in my field, a person who is quoted so often in the NYT, Atlantic, podcasts etc. that it's not really a special thing in the lab anymore. That person asked me questions like this and waited a bit. But I finally got an email and interview a while later.

My key piece of advice is this: the lab you are applying to probably has several long term people who you also will interview with. Of course the science always matters but huge part of getting in is showing that you can get along with those people and integrate into the lab culture. They're the people you'll be working with every day and the PI takes their opinions into account.

An alternative suggested to me by a very experienced PI:

1. Run WGCNA for each cluster
2. Find gene modules that significantly differ by treatment - I think there's a function in hdWGCNA
3. Take the gene lists from significant modules and run through ORA or GSEA

Translation: most people don't think for themselves

You probably shouldn't ask reddit what your tattoo should be. Ideally, you would want a specific tattoo, not just a tattoo for the sake of having a tattoo.

With that said, something unique like a sick-ass panther might be nice.

Also, for location...sounds like upper arms, upper legs, torso. FYI your parents will find out regardless. Probably sooner than you're hoping, too.

Good luck!

Man I love the idea of NixOS and used it for several months but it is such a hassle sometimes. If you don't want to customize much it's great but doing things like changing certain config files, installing non-repo packages, and dealing the more obscure errors that show up when you're doing a rebuild are awful. They're things that seem conceptually simple but end up being monumental tasks.

Sometimes I'd have to use an LLM for my problems and even then it could take a couple hours to solve some small issue. I spent the first 2 months with it getting it set up to my liking. Then, one day, it refused to let me rebuild so I could update Signal (had to join a group video call at a specific time) because some other package now had a problem and I missed the call. Decided to move to CachyOS after that.

I feel like maybe I'll try it again in 5 years to see how things have changed though because I love the idea of what it could be.

Yea I'm very interested to see what warrants 190 samples. I'm about to start a 72 sample set with 20k target cells/sample that will be split into 3 tissues. I felt like I was doing pretty decent having 24 samples per tissue and 8 per treatment so 190 is nuts.

Also, it seems like a risky move to spend like $140k + on samples to be analyzed by someone who is fairly new at this.

For sure. That's why I suggested Scanpy but I just noticed OP said they're not interested in moving to Python. So it looks like Seurat sketch and bit packing methods are the way to go.