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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

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