Re-mapping with different parameters drastically improves the mapping, and enables identifying disease-specific cell states, such as altered smooth muscle cells in lungs of patients with IPF (reproducible across studies). MapQC thus helps you make the best use of your data!

Here’s an example of a mapping that failed. For some cell types (e.g. circled ct), the UMAP suggests the query mixes well with the reference. However, mapQC scores show this is not the case, and downstream-analysis indeed results in batch-effect driven conclusions.

This results in cell-level mapQC scores, with a score >2 indicating large distance to the reference. We expect controls in the query to be the same as in the ref., i.e. to show scores <2. In contrast, disease samples should show some high distance to the ref. (local scores >2).

How does it work? We use the control samples in the large-scale reference to obtain prior knowledge of normal inter-sample variation. We do this locally, such that we learn cell-state-specific inter-sample distances. We compare those to query sample distances to the reference.

We therefore developed mapQC, a method that takes as its input any query mapping to a large-scale reference, and outputs a cell-level mapQC score. The score will tell you if, and where, the mapped query contains batch effects, or if e.g. disease-specific variation was removed.

One commonly used metric, LISI, is highly sensitive to cell numbers, which in fact are independent of integration/mapping quality and should not affect metric outcome. Finally, all of the existing metrics lack a clear rationale for a cutoff between good and bad mappings.

Moreover, standard integration and mapping metrics fail to pinpoint these failures: they quantify the wrong things. Here’s an example, with one very poor and one good-quality mapping resulting in the same scores for several metrics, but not mapQC.

Analyzing your single-cell data by mapping to a reference atlas? Then how do you know the mapping actually worked, and you’re not analyzing mapping-induced artifacts? We developed mapQC, a mapping evaluation tool www.biorxiv.org/content/10.1... from the ‪@fabiantheis lab. Let’s dive in🧵

3/7 Atlases have value beyond individual datasets. Their diversity in samples, donors and conditions paint a more holistic picture of biology. Moreover, they are invaluable as a reference for analyzing new data, easing preprocessing and guiding interpretation.

1/7 Planning to build a single-cell atlas? Or wondering how atlases can be useful to your research? Read our guide on single-cell atlases www.nature.com/articles/s41... published in Nature Methods, by @lisasikkema.bsky.social, @khrovatin.bsky.social, Malte Luecken, @fabiantheis.bsky.social et al.