Really handy overview!
Already fig 1 I could see myself sharing frequently to explain differences between whe blood and plasma omics 👇

In biological data finding meaningful ways to do training-test splits without leaking information can be tricky as readouts can be highly correlated, causing over-optimism.
Here a nice example based on protein language models 👇

Really impressive study showing an actual successful end-to-end rare disease example how to use IPSC + repurposable drug screening followed up by increasing complex diseases models (and functional analyses) to find a drug that is successfully tested in patients.

Congratulations 💪

The link seems not fully accurate - the silk paper starts a bit later with the Serin section at page 93:
babel.hathitrust.org/cgi/pt?id=hv...

They derive formulas from burning experiments. They also get some kind of structure notation (p96) and compare to to Cystein, Alanine and Glycine.

This looks really interesting!
Maybe such an easy way to efficiently load genomic data helps to prevent users to convert efficient structured data format such as vcf into less structured csv just for easier data access.

Really comprehensive and nicely put together review of the state of mass spec based rare disease research 👌

Also reminds me when hiking in Northern Lapland wilderness where we set camp in the evening and took water from a stream 20m from the coast - just to discover a whale carcass 5m upstream the next morning 😅
Luckily we did not go for the raw water experience but cooked it before drinking...

As a Swiss it is absolutely usual to have untreated drinking water straight from an underground spring.
The bit BUT: the water quality is continuously monitored and fields around spring are especially protected (eg no manure allowed, see www.bafu.admin.ch/bafu/de/home... ).

Method of the Year 2024: spatial proteomics - Nature Methods Approaches for profiling the spatial proteome in tissues are the basis of atlas-scale projects that are delivering on their promise for understanding biological complexity in health and disease.

Spatial proteomics is chosen Nature Methods's Method of the year
www.nature.com/articles/s41...

Having worked all my PhD helping establishing and pushing the boundaries of one of these technologies, I am obviously quite pleased to see this kind of recognition of the field ☺️

Method of the Year 2024: spatial proteomics - Nature Methods Approaches for profiling the spatial proteome in tissues are the basis of atlas-scale projects that are delivering on their promise for understanding biological complexity in health and disease.

I could not be more thrilled to announce the Nature Methods @naturemethods.bsky.social Method of the Year is Spatial Proteomics! Please see our editorial as a roadmap to the fantastic content in this special issue! www.nature.com/articles/s41...

ah ok, I missunderstood this point.
Actually like this it does make lots of sense - thanks a lot for clarifying 👌

Declaration of conflict of interest:
I love dark chocolate, thus am more likely to report studies supporting this lifestyle.

Based on hunderd thousands of health professionals and nurses monitored long term by self reporting questionaires.

Chocolate intake and risk of type 2 diabetes: prospective cohort studies Objective To prospectively investigate the associations between dark, milk, and total chocolate consumption and risk of type 2 diabetes (T2D) in three US cohorts. Design Prospective cohort studies. ...

"Conclusions: Increased consumption of dark, but not milk, chocolate was associated with lower risk of T2D. Increased consumption of milk, but not dark, chocolate was associated with long term weight gain. "

www.bmj.com/content/387/...

Really impressive technological feat 💪

Not sure if I agree on the point that clustering on proteomics and doing DE on transcriptomics is not double dipping: if the two are moderately correlated are they really independent enough to fully avoid the issue?

An interesting choice ot the bandwith of the 2D KDE..

Topological data analysis with Mapper Introduction Topological data analysis (TDA) is a recent branch of data analysis that uses topology and in particular persistent homology. TDA is used to extract meaning from the shape of data. In th...

Is the Mapper algorithm already used for single cell clustering?
www.quantmetry.com/blog/topolog...
I really like the concept of coarse grouping based on a reduced dimension combined with clustering in original space.