Journal Article (Review Article)

DKFZ-2026-00024

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Interpretation, extrapolation and perturbation of single cells.

Dimitrov, D. (First author)DKFZ* ; Schrod, S. (First author)DKFZ* ; Rohbeck, M.DKFZ* ; Stegle, O. (Last author)DKFZ*

2026
Nature Publ. Group London

Abstract: Single-cell analyses have transitioned from descriptive atlasing towards inferring causal effects and mechanistic relationships that capture cellular logic. Technological advances and the growing scale of observational and interventional datasets have fuelled the development of machine learning methods aimed at identifying such dependencies and extrapolating perturbation effects. Here, we review and connect these approaches according to their modelling concepts (including representation learning, causal inference, mechanistic discovery, disentanglement and population tracing), underlying assumptions and downstream tasks. We propose a unifying ontology to guide practitioners in selecting the most suitable methods for a given biological question, with detailed technical descriptions provided in an online resource . Finally, we identify promising computational directions and underexplored data properties that could pave the way for future developments.

Note: #EA:B260#LA:B260# / epub

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Contributing Institute(s):

1. B260 Bioinformatik der Genomik und Systemgenetik (B260)

Research Program(s):

1. 312 - Funktionelle und strukturelle Genomforschung (POF4-312) (POF4-312)

Appears in the scientific report 2026

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Database coverage:
; BIOSIS Previews ; BIOSIS Reviews Reports And Meetings ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Life Sciences ; DEAL Nature ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 40 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection ; Zoological Record

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