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21.
De Donno, C. et al.: Population-level integration of single-cell datasets enables multi-scale analysis across samples. Nat. Methods 20, 1683-1692 (2023)
22.
Rosenberger, F.A.* et al.: Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome. Nat. Methods 20, 1530-1536 (2023)
23.
Sandoval-Guzmán, T.: The axolotl. Nat. Methods 20, 1117-1119 (2023)
24.
Spitzer, H. ; Berry, S.* ; Donoghoe, M.* ; Pelkmans, L.* & Theis, F.J.: Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps. Nat. Methods 20, 1058-1069 (2023)
25.
Bandi, V.G.* et al.: Targeted multicolor in vivo imaging over 1,000 nm enabled by nonamethine cyanines. Nat. Methods 19, 353–358 (2022)
26.
Giansanti, P.* et al.: Mass spectrometry-based draft of the mouse proteome. Nat. Methods 19, 803-811 (2022)
27.
Lange, M. et al.: CellRank for directed single-cell fate mapping. Nat. Methods 19, 159–170 (2022)
28.
Luecken, M. et al.: Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41-50 (2022)
29.
Palla, G. et al.: Squidpy: A scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022)
30.
Matschinske, J.* et al.: The AIMe registry for artificial intelligence in biomedical research. Nat. Methods, DOI: 10.1038/s41592-021-01241-0 (2021)
31.
Moebel, E.* et al.: Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms. Nat. Methods 18, 1386-1394 (2021)
32.
Moebel, E.* et al.: Author Correction: Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms (Nature Methods, (2021), 18, 11, (1386-1394), 10.1038/s41592-021-01275-4). Nat. Methods, DOI: 10.1038/s41592-021-01349-3 (2021)
33.
Nothias, L.F.* et al.: Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020)
34.
Todorov, M.I. et al.: Machine learning analysis of whole mouse brain vasculature. Nat. Methods 17, 442-449 (2020)
35.
Büttner, M. ; Miao, Z.* ; Wolf, F.A. ; Teichmann, S.A.* & Theis, F.J.: A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43-49 (2019)
36.
Lotfollahi, M. ; Wolf, F.A. & Theis, F.J.: scGen predicts single-cell perturbation responses. Nat. Methods 16, 715-721 (2019)
37.
Qian, Y.* et al.: A genetically encoded near-infrared fluorescent calcium ion indicator. Nat. Methods 16, 171-174 (2019)
38.
Weigert, M.* et al.: Content-aware image restoration: Pushing the limits of fluorescence microscopy. Nat. Methods 15, 1090-1097 (2018)
39.
Azencott, C.* et al.: The inconvenience of data of convenience: Computational research beyond post-mortem analyses. Nat. Methods 14, 937-938 (2017)
40.
Buggenthin, F. et al.: Prospective identification of hematopoietic lineage choice by deep learning. Nat. Methods 14, 403–406 (2017)
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