Automated MALDI Cell Assays Using Dose-Response Curve Fitting
Calculate Chauvenet's criterion for outlier detection
Calculate the fit for a dose-response curve
Calculate peak statistics
Calculate strictly standardized mean difference (SSMD)
Calculate V'-Factor
Calculate Z'-factor of assay quality
Check the recalibration of spectra from a MALDIassay object
Extract intensity using peaks as template
Extract the spot coordinates
Filter for high variance signals
Fit dose-response curves
Get all mz value of an MALDIassay-object
Extract applied mz-shift
Extract applied normalization factors
Extract peaks of average spectra
Extract average spectra
Get binning tolerance
Extract the concentrations used in a MALDIassay
Extract curve fits
Extract directory path
Get fitting parameters
Get the intensity matrix of single spectra for all fitted curves
Get the mz value associated with a mzIdx
Get mass shift for target mz
Get normalization factors from peak data.frame
Extract normalization method
Extract m/z used for normalization
Extract tolerance used for normalization
Extract peak statistics
Calculate remaining calibration error of a MALDIassay object
Extract peaks of single spectra spectra (before average calculation)
Extract the intensities of single spectra for a given mzIdx
Extract SNR used for peak detection
Get the spot coordinates of spectra
Extract variance filtering method
Check if object if of class MALDIassay
load bruker MALDI target plate spectra
load mzML spectra
Class MALDIassay
Normalize spectra and peaks
Apply normalization factors to spectra
Convert a list of peaks to a data.frame
generate ggplot objects for each of the curve fits in a MALDIassay obj...
Plot a peak of interest from a MALDIassay object
Compute standard-deviation spectra
Shift mass axis
Convert concentration to log10 and replace zero's
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>.
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