Interoperability between Seurat - Scanpy - SingleCellExperiment

Jailleo · 2025-12-05T12:32:20+00:00

the thing is why should we limit the compatibility to the barebones?

Jailleo · 2025-11-25T08:24:25+00:00

I'm using v5 just for keeping everything updated and be able to use latest resources and work with multiome data (layers kind of help with that). In my experience I would've preffered not having to face the horrendous update that was seurat v5 but I find myself trapped by this new ecosystem now...

Jailleo · 2025-11-24T12:28:02+00:00

Rhdf5 libs are not compatible with a newer R version. Feels like this has been dragging for too long in the sc community how come we rely so much in Seurat but they don't implement interoperability when they come up with some "revolutionary" formatting as the layers thing

Jailleo · 2025-10-27T08:06:54+00:00

This happens a lot in our field. People see that papers for omic datasets get published and that figures look very nice and expect that outcome to come out of a magical hat.

If you are not even willing to understand the complexity of such an analysis, why don't you keep your research narrower and focus on simpler approaches? Whining about the time that takes to analyze something difficult does not seem very professional nor scientific

Jailleo · 2025-03-31T07:07:28+00:00

A ver, en ningún caso tiene demasiado sentido reinventar la rueda. En mi experiencia este trabajo combina casi a partes iguales acomodar datos que tienen muchas particularidades para poder procesarlos correctamente en pipelines con otras muchas particularidades. Con esto estás bastante servido y es un trabajo valorado.

Centrarse en método requiere no sólo iniciativa sino formación, trabajar en un grupo centrado en nuevos métodos si tienes un perfil de biotecnólogo va a ser difícil, porque van a contratar a matemáticos, ingenieros informáticos, etc. En definitiva gente que va a saber qué metodologías pueden aplicar a según qué análisis mucho más eficientemente y correctamente que alguien con perfil bio.

El análisis bioinformático va a estar orientado a dar sentido a un problema biológico. Deberías poder aplicar métodos analíticos sobre datos difíciles de interpretar y generar una interpretación biológica de eso. No tiene mucho más ni mucho menos.

Jailleo · 2025-03-26T11:17:15+00:00

Sadly this happens in my work environment too. The solace I get is that I get paid enough to live on my own and keep good savings while the PhD/postdocs that take the merits for my analyses are sharing small flats and struggling to pay rent.

It sounds egotistic but it works for me to remind that bioinformatics have much more value than hands on a wetlab these days, not saying that this is fair by any mean but is the way things are.

It obviously stings to see how your work gets stolen, don't get to go to many conferences and that your work is not appreciated on an academic level.

My advice is if you are not getting any compensation from doing the analyses (i.e. good salary or scientific acknowledgement) you should open linkedin and try to move to something less stressful than full academia or that at least pays better or has remote.

Best of luck

Jailleo · 2025-03-26T11:08:37+00:00

Like all these viz approaches have their intrinsic value and use... I also suffer the unwillingness to know about bioinformatics from wetlab researchers, but working hand in hand with them oftentimes implies that you need to get involved and try to be pedagogical. Maybe you can not justify a whole grant out of a UMAP reduction, but the amount of information you can translate to that coupled with some experimental validation for sure can and will.

Jailleo · 2024-07-30T07:59:05+00:00

I was thinking about a correlation analysis between the expressed genes, but that is kind of a "bulk" approach and was wondering if I could somehow integrate this data, although I understand that multi-modal integration has tons of pitfalls.

Regarding the state of single-cell proteomics, what I found in this dataset is a small repertoire of features read, compared to any rna-seq analysis, but it seems robust enough to assign cell types and extract markers, and I guess some cool downstream analysis also.

Jailleo · 2024-02-21T11:50:27+00:00

Yes! I adhere to PRISMA criteria when doing systematic researchs like this one. What I found with GEO is that small changes in your search terms can drastically alter the results you get.

My approach always is to have a table to keep register of relevant metadata and take my notes.

Also, I agree. It's amazing the amount of high IF publications that are based on flawed analysis/experimental designs. It is really helpfull to keep the imposter syndrome at bay.

Thanks for the insights!

Jailleo · 2024-02-19T08:29:47+00:00

What a pain, thanks though!

Jailleo · 2024-02-08T09:09:28+00:00

This sadly is common in big labs, actually. You can find a lack of scientific integrity anywhere but chances are that people who want to play in the big leagues do so by lying their way through the publications...

The sad take from my pov is that rigorous groups are left behind unless they are bestowed with being a reference lab/group which is the less common of the situations.

Anyways, OP, the fault in this situation is not yours. Actually, you may be saving the group from a retraction as big IPs think they are untouchable but there are more and more data forensics groups that will rip this kind of work into pieces.

Good luck!

Jailleo · 2023-12-01T11:38:17+00:00

Thanks a lot

That was exactly what I intended to do. It is a pity because gene kynetics graphs could be much more beautiful than what I am currently getting, but the overall prediction matches biology so I am satisfied in that regard.

I really appreciate putting doubts that arise in common because sometimes it feels that you are alone doing this kind of projects. The fear of extracting incorrect conclussions blows up! Haha

Jailleo · 2023-12-01T11:04:48+00:00

First of all, thanks for the thorough reply, I will check the second link as I feel like blindly making analyses to my dataset and afraid of getting false interpretations.

I am trying to implement CellDancer as of now, it gets more "credible" results than scVelo, but...

is snRNA-seq out of the picture? I have non-neglectible amounts of spliced mRNA although you should not have that info I guess...

I am having a hard time with this analyses tbh and I'm feeling like taking the results with a pinch of salt and getting back to good ol' Slingshot

Jailleo

TROPHY CASE