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Open Access

Correction

Correction: An orchestra of machine learning methods reveals landmarks in single-cell data exemplified with aging fibroblasts

  • The PLOS One Staff

Notice of republication

An incomplete, earlier version of this article was published in error. The publisher apologizes for the error. This article was republished to correct for this error. Please download the article again to view the correct version. The originally published, uncorrected article and the republished, corrected article are provided here for reference.

Supporting information

S1 File. Originally published, uncorrected article.

https://doi.org/10.1371/journal.pone.0321051.s001

(PDF)

S2 File. Republished, corrected article.

https://doi.org/10.1371/journal.pone.0321051.s002

(PDF)

Reference

  1. . Rasbach L, Caliskan A, Saderi F, Dandekar T, Breitenbach T. An orchestra of machine learning methods reveals landmarks in single-cell data exemplified with aging fibroblasts. PLoS One. 2024;19(4): e0302045. pmid:38630692

Citation: The PLOS One Staff (2025) Correction: An orchestra of machine learning methods reveals landmarks in single-cell data exemplified with aging fibroblasts. PLoS ONE 20(3): e0321051. https://doi.org/10.1371/journal.pone.0321051

Published: March 25, 2025

Copyright: © 2025 The PLOS One Staff. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.