MALDIcellassay: Automated MALDI Cell Assays Using Dose-Response Curve Fitting
Conduct automated cell-based assays using Matrix-Assisted Laser Desorption/Ionization (MALDI) methods for high-throughput screening of signals responsive to treatments. The package efficiently identifies high variance signals and fits dose-response curves to them. Quality metrics such as Z', V', log2FC, and CRS are provided for evaluating the potential of signals as biomarkers. The methodologies were introduced by Weigt et al. (2018) <doi:10.1038/s41598-018-29677-z> and refined by Unger et al. (2021) <doi:10.1038/s41596-021-00624-z>.
Version: |
0.4.47 |
Depends: |
R (≥ 4.2) |
Imports: |
methods, ggplot2, nplr, dplyr, tidyr, forcats, scales, MALDIquant, MALDIquantForeign, tibble, svMisc, purrr |
Suggests: |
rmarkdown, knitr |
Published: |
2024-08-29 |
DOI: |
10.32614/CRAN.package.MALDIcellassay |
Author: |
Thomas Enzlein
[aut, cre, cph] |
Maintainer: |
Thomas Enzlein <t.enzlein at hs-mannheim.de> |
BugReports: |
https://github.com/CeMOS-Mannheim/MALDIcellassay/issues |
License: |
MIT + file LICENSE |
URL: |
https://github.com/CeMOS-Mannheim/MALDIcellassay |
NeedsCompilation: |
no |
Materials: |
README NEWS |
CRAN checks: |
MALDIcellassay results |
Documentation:
Downloads:
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