MALDIcellassay

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', log2-Fold-Change and the Curve response score (CRS) are provided for evaluating the potential of signals as biomarkers. The methodologies were introduced by Weigt et al. (2018) and first release as the MALDIcellassay-package in Unger et al. (2021).

Main Functionality

The primary function in this package is fitCurve() which not only performs logistic regression and variance filtering but also handles all necessary preprocessing steps:

• Spectral alignment ("single-point recalibration")
• Normalization by an internal standard or known endogenous signal

All these functions can also be called independently.

This package makes heavy use of the MALDIquant framework.

Installation

From CRAN

You can install the released version of MALDIcellassay from CRAN with:

install.packages("MALDIcellassay")

From GitHub

To install the development version of the package, use devtools:

# Install devtools if you haven't already
install.packages("devtools")

# Install MALDIcellassay from GitHub
devtools::install_github("CeMOS-Mannheim/MALDIcellassay")

Example Usage

Here's a simple example to get you started with the MALDIcellassay package:

library(MALDIcellassay)
library(MALDIquant)

# Load example data
data("Blank2022spec")

# Preprocess data
spec_prc <- MALDIquant::removeBaseline(Blank2022spec)
conc <- as.numeric(names(Blank2022spec))
names(spec_prc) <- conc

# Fit dose-response curve
res <- fitCurve(spec = spec_prc,
                SinglePointRecal = TRUE, 
                normMeth = "mz",
                varFilterMethod = "none",
                normMz = 760.585, 
                alignTol = 0, 
                normTol = 0.1, 
                verbose = FALSE)

# View the results
print(res)

For more detailed examples, check out the vignette. Also take a look at the pkgdown-site for more details on specific functions.

GUI

If you want to use MALDIcellassay interactively to explore your data, you might be interested in M²ara, which offers a Shiny-based interface. It is also available as a docker container and as a stand-alone Windows installer. You can find more details in the preprint on M²ara.

Contribution and Contact

We welcome contributions to MALDIcellassay! If you encounter any issues or have suggestions for improvement, please open an issue on GitHub or submit a pull request.

For any questions or further information, you can contact the maintainer:

Name: Thomas Enzlein
Email: [email protected]

Citation

If you use MALDIcellassay in your research, please cite:

Unger et al., 2021, "Label-free Cell Assays of Compound Uptake and Drug Action using MALDI-TOF Mass Spectrometry", Nature Protocols. DOI: https://doi.org/10.1038/s41596-021-00624-z.