No matter how much you love what you do, this one thing gives you a 10x energy boost

#phd

Fun fact: it was supposed to be a quick one-month project on the intersection of ethics and single-cell research to produce a one-page comment. But we got carried away and wrote a bit more 😅 I hope you learn something useful! I certainly did when working on it. 10/10

Want to see more examples and details? Check out the full publication: nature.com/articles/s41...

Thanks to all co-authors, especially @theresawillem.bsky.social, who did most of the work,
Malte Lücken, who initialised the collaboration, and
@fabiantheis.bsky.social. 9/10

6. Result interpretation bias. The complexity of modern methods sometimes leads to wrong interpretation of the results. The literature knows examples of UMAP-based conclusions or praising useless models because of data leakage to the metrics. 8/10

5. Machine learning bias. Batch effects in the data, not considering outliers, limitations of the used models, or wrong metrics can all lead to incorrect results. 7/10

4. Single-cell sequencing bias. Some cell types are often missing in the data for technical reasons (e.g. neutrophils). And even for captured cells, we don't see all RNA copies because of the dropout. 6/10

3. Cohort bias. Number of donors in SC studies is still quite low (see previous post: x.com/shitov_happe..., sorry for X link). Moreover, most of the samples in the datasets come from individuals with European ancestry. This can limit the generalization of conclusions to other populations. 5/10

2. Clinical bias. Patients with different conditions are not sampled uniformly. Especially, "healthy" controls might not reflect a population norm well. Not everyone wants to donate a piece of their lung or a brain for science. 4/10

1. Societal bias. The samples likely come from clinics or research institutions with quite some money to run single-cell experiments. Not everyone might have access to them. Be careful when extrapolating your conclusions to the general population. 3/10

Recently, a number of methods emerged for working with single-cell data at the sample level. We call them sample (in a clinical context – patient) representation methods. They enable patient stratification, prognostic and diagnostic capabilities. But be aware of the biases! 2/10

When applying machine learning to human health data, it is not enough to just improve a metric by another percent. We have to go deeper. In our perspective in Nature Cell Biology, we discuss caveats and biases of human single-cell data analysis: nature.com/articles/s41...
🧵 1/10

How do biases affect machine-learning models of human single-cell data? And what can we do about it? In our new Perspective article, "Biases in machine-learning models of human single-cell data," published in Nature Cell Biology, we explore these pressing questions.

👉🏻 www.nature.com/articles/s41...

That led to amazing comebacks sometimes. An ace could massacre an entire group, but then meet a six and lose the army. Also it was fascinating to think about the best strategies where to put your strongest and weakest cards

We used to have a card game as kids. Everyone has the same set and puts cards on the floor face down. Players move step by step. When cards of enemies meet, faces are revealed and the higher in order card wins, the other one dies. The highest card (ace) can only be beaten by the weakest (six)

15 open PhD positions, including the one in our lab. The direction is precision medicine in COPD and Asthma using single-cell genomics and machine learning. Travelling across Europe and to Australia is included! www.respire-excel.eu/vacancies

#PhD #jobs

Only the avatar, master of all elements, could understand the biology in all its complexity

#science #comics #biology