| Title: | Automated MALDI Cell Assays Using Dose-Response Curve Fitting |
|---|---|
| Description: | 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>. |
| Authors: | Thomas Enzlein [aut, cre, cph]
|
| Maintainer: | Thomas Enzlein <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.4.47 |
| Built: | 2025-02-17 12:38:26 UTC |
| Source: | https://github.com/cranhaven/cranhaven.r-universe.dev |
Intensity matrix from MALDI mass spectrometry data of EOC cells treated with different concentrations of SAHA.
It is used to demonstrate the usage of MALDIcellassay.
data(Blank2022intmat)data(Blank2022intmat)
Matrix with concentrations of original spectra as rownames and m/z-values as colnames.
The concentrations include: 0, 0.04, 0.12, 0.37, 1.11, 3.33, 10 and 30 uM of SAHA at 4 replicates each.
The original spectra were trimmed to 400-900 Da mass-range to keep the file size small.
The peaks are the result of MALDIquant::intensityMatrix(Blank2022peaks, Blank2022spec)
Blank, M., Enzlein, T. & Hopf, C. LPS-induced lipid alterations in microglia revealed by MALDI mass spectrometry-based cell fingerprinting in neuroinflammation studies. Sci Rep 12, 2908 (2022). https://doi.org/10.1038/s41598-022-06894-1
Peaks from MALDI mass spectrometry data of EOC cells treated with different concentrations of SAHA.
It is used to demonstrate the usage of MALDIcellassay.
data(Blank2022peaks)data(Blank2022peaks)
A list of MALDIquant::MassPeaks-objects named with the respective concentration.
The concentrations include: 0, 0.04, 0.12, 0.37, 1.11, 3.33, 10 and 30 uM of SAHA at 4 replicates each.
The original spectra were trimmed to 400-900 Da mass-range to keep the file size small.
The peaks are the result of applying MALDIquant::detectPeaks to Blank2022spec with arguments SNR = 3, method = "SuperSmoother".
Blank, M., Enzlein, T. & Hopf, C. LPS-induced lipid alterations in microglia revealed by MALDI mass spectrometry-based cell fingerprinting in neuroinflammation studies. Sci Rep 12, 2908 (2022). https://doi.org/10.1038/s41598-022-06894-1
Object of class MALDIcellassay from MALDI mass spectrometry data of EOC cells treated with different concentrations of SAHA.
It is used to demonstrate the usage of MALDIcellassay.
data(Blank2022res)data(Blank2022res)
Matrix with concentrations of original spectra as rownames and m/z-values as colnames.
The concentrations include: 0, 0.04, 0.12, 0.37, 1.11, 3.33, 10 and 30 uM of SAHA at 4 replicates each.
The original spectra were trimmed to 400-900 Da mass-range to keep the file size small.
The peaks are the result of
fitCurve(spec = Blank2022spec, SinglePointRecal = TRUE, normMz = 760.585, alignTol = 0.1, normTol = 0.1)
Blank, M., Enzlein, T. & Hopf, C. LPS-induced lipid alterations in microglia revealed by MALDI mass spectrometry-based cell fingerprinting in neuroinflammation studies. Sci Rep 12, 2908 (2022). https://doi.org/10.1038/s41598-022-06894-1
MALDI mass spectrometry data of EOC cells treated with different concentrations of SAHA.
It is used to demonstrate the usage of MALDIcellassay.
data(Blank2022spec)data(Blank2022spec)
A list of MALDIquant::MassSpectrum-objects named with the respective concentration.
The concentrations include: 0, 0.04, 0.12, 0.37, 1.11, 3.33, 10 and 30 uM of SAHA at 4 replicates each. The original spectra were trimmed to 400-900 Da mass-range to keep the file size small.
Blank, M., Enzlein, T. & Hopf, C. LPS-induced lipid alterations in microglia revealed by MALDI mass spectrometry-based cell fingerprinting in neuroinflammation studies. Sci Rep 12, 2908 (2022). https://doi.org/10.1038/s41598-022-06894-1
Calculate Chauvenet's criterion for outlier detection
calculateChauvenetCriterion(x)calculateChauvenetCriterion(x)
x |
numeric, values (e.g. intensities) to test for outliers |
Note that, as for all outlier detection criteria: Excluding data points from your measurement should only be conducted with extreme care. Even if this (or any other) function tells you that a data point is an outlier, you might still want to have it in your sample population especially if you are not sure if your data is normal distributed. See Wikipedia for details of the algorithm.
logical vector, TRUE for detected outliers.
set.seed(42) #no outlier sample <- rnorm(n = 8, mean = 0, sd = 0.01) calculateChauvenetCriterion(sample) # introduce outlier sample[1] <- 1 calculateChauvenetCriterion(sample)set.seed(42) #no outlier sample <- rnorm(n = 8, mean = 0, sd = 0.01) calculateChauvenetCriterion(sample) # introduce outlier sample[1] <- 1 calculateChauvenetCriterion(sample)
Calculate the fit for a dose-response curve
calculateCurveFit(intmat, idx, verbose = TRUE, ...)calculateCurveFit(intmat, idx, verbose = TRUE, ...)
intmat |
Intensity matrix as generated by
|
idx |
Numeric vector of the mz indices to perform the fit. |
verbose |
Logical, print logs to console. |
... |
Additional arguments passed to |
List of curve fits.
data(Blank2022intmat) # for faster runtime we let it run on 5 peaks only fits <- calculateCurveFit(Blank2022intmat, idx = 1:5)data(Blank2022intmat) # for faster runtime we let it run on 5 peaks only fits <- calculateCurveFit(Blank2022intmat, idx = 1:5)
Calculate peak statistics
calculatePeakStatistics(curveFits, singlePeaks, spec)calculatePeakStatistics(curveFits, singlePeaks, spec)
curveFits |
list of curve fits as returned by |
singlePeaks |
list of MALDIquant::MassPeaks. |
spec |
list of MALDIquant::MassSpectrum. |
A tibble with peak statistics.
data(Blank2022intmat) fits <- calculateCurveFit(Blank2022intmat, idx = 1:5) peakstats <- calculatePeakStatistics(curveFits = fits, singlePeaks = Blank2022peaks, spec = Blank2022spec) head(peakstats)data(Blank2022intmat) fits <- calculateCurveFit(Blank2022intmat, idx = 1:5) peakstats <- calculatePeakStatistics(curveFits = fits, singlePeaks = Blank2022peaks, spec = Blank2022spec) head(peakstats)
Calculate strictly standardized mean difference (SSMD)
calculateSSMD(res, internal = TRUE, nConc = 2)calculateSSMD(res, internal = TRUE, nConc = 2)
res |
Object of class MALDIassay |
internal |
Logical, currently only the internal implementation,
using |
nConc |
Numeric, number of top and bottom concentrations to be used
to calculate the pseudo positive and negative control.
Only used if |
The strictly standardized mean difference (SSMD) is a measure of effect size. It is the mean divided by the standard deviation of a difference between the positive and negative control.
The SSMD can be easily be interpreted as it denotes the difference between positve and negative controls in units of standard deviation.
Numeric vector of strictly standardized mean differences (SSMD)
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) calculateSSMD(Blank2022res, nConc = 2)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) calculateSSMD(Blank2022res, nConc = 2)
Calculate V'-Factor
calculateVPrime(res, internal = TRUE)calculateVPrime(res, internal = TRUE)
res |
Object of class MALDIassay |
internal |
Logical, currently only the internal implementation,
using |
The V'-factor is a generalization of the Z'-factor to a dose-response curve. See M.-A. Bray and A. Carpenter, Advanced assay development guidelines for image-based high content screening and analysis for details. It is defined as:
with
In other words, is the standard deviation of residuals.
Note, we do not need to estimate the variance for the mean of the positive and negative value. So, this function uses the top and bottom asymptote directly instead of taking the top and bottom concentrations in consideration.
Numeric vector of V'-factors
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) calculateVPrime(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) calculateVPrime(Blank2022res)
Calculate Z'-factor of assay quality
calculateZPrime(res, internal = TRUE, nConc = 2)calculateZPrime(res, internal = TRUE, nConc = 2)
res |
Object of class MALDIassay |
internal |
Logical, currently only the internal implementation,
using |
nConc |
Numeric, number of top and bottom concentrations to be used
to calculate the pseudo positive and negative control.
Only used if |
The most common way to measure the quality of an assay is the so-called Z'-factor,
which describes the separation of the positive and negative control in terms of their standard deviations and .
The Z'-factor is defined as Ji-Hu Zhang et al., A simple statistical parameter for use in evaluation and validation of high throughput screening assays.
where and is the mean value of the positive (response expected) and negative (no response expected) control, respectively.
Therefore, the assay quality is independent of the shape of the concentration response curve and solely depend on two control values.
Note, if internal is set to TRUE, the nConc highest concentrations are assumed as positive control,
whereas the nConc lowest concentrations are used as negative.
| Value | Interpretation |
| Z' ~ 1 | perfect assay |
| 1 > Z' > 0.5 | excellent assay |
| 0.5 > Z' > 0 | moderate assay |
| Z' = 0 | good only for yes/no response |
| Z' < 0 | unacceptable |
Numeric vector of Z'-factors.
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) calculateZPrime(Blank2022res, nConc = 2)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) calculateZPrime(Blank2022res, nConc = 2)
Dashed gray lines indicate the mz used for re-calibration ± the tolerance. Red dashed line indicate the mz used for re-calibration and solid lines indicate peaks. The spectrum will show the peak used for re-calibration ± 10x the tolerance.
checkRecalibration(object, idx)checkRecalibration(object, idx)
object |
Object of class MALDIassay |
idx |
Numeric, index of spectrum to plot |
ggplot object
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) checkRecalibration(Blank2022res, idx = 1:8)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) checkRecalibration(Blank2022res, idx = 1:8)
Extract intensity using peaks as template
extractIntensity(mz, peaks, spec, tol)extractIntensity(mz, peaks, spec, tol)
mz |
numeric, mz values to be extracted from the peaks/spectra |
peaks |
MALDIquant::MassPeaks list |
spec |
MALDIquant::MassSpectrum list |
tol |
numeric, tolerance in Da |
MALDIquant::MassPeaks list with extracted intensities from spec at m/z of peaks = pseudo peaks. Useful in combination with sdMassSpectrum to get standard deviation of peaks as intensity matrix.
data(Blank2022peaks) data(Blank2022spec) int <- extractIntensity(mz = c(409, 423, 440), peaks = Blank2022peaks, spec = Blank2022spec, tol = 0.2) head(int)data(Blank2022peaks) data(Blank2022spec) int <- extractIntensity(mz = c(409, 423, 440), peaks = Blank2022peaks, spec = Blank2022spec, tol = 0.2) head(int)
Extract the spot coordinates
extractSpots(spec)extractSpots(spec)
spec |
list of MALDIquant::MassSpectrum or MALDIquant::MassPeaks objects |
Character vector of spot names. If multiple spots are used (e.g. for average spectra) they will be concatenate.
data(Blank2022spec) head(extractSpots(Blank2022spec))data(Blank2022spec) head(extractSpots(Blank2022spec))
Filter for high variance signals
filterVariance( vars, method = c("mean", "median", "q25", "q75", "none"), verbose = TRUE )filterVariance( vars, method = c("mean", "median", "q25", "q75", "none"), verbose = TRUE )
vars |
Numeric vector, variances of signals |
method |
Character, filtering method. One of "mean" (default), "median", "q25", "q75" (25 and 75% quantile) or "none". |
verbose |
Logical, print logs to console. |
Indices of spectra with a high variance
data(Blank2022intmat) # get variance of each peak vars <- apply(Blank2022intmat, 2, var) highVarIndicies <- filterVariance(vars, method = "mean", verbose = TRUE)data(Blank2022intmat) # get variance of each peak vars <- apply(Blank2022intmat, 2, var) highVarIndicies <- filterVariance(vars, method = "mean", verbose = TRUE)
Fit dose-response curves
fitCurve( spec, unit = c("M", "mM", "uM", "nM", "pM", "fM"), varFilterMethod = c("mean", "median", "q25", "q75", "none"), monoisotopicFilter = FALSE, averageMethod = c("mean", "median", "sum"), normMz = NULL, normTol = 0.1, alignTol = 0.01, binTol = 2e-04, SNR = 3, halfWindowSize = 3, allowNoMatches = TRUE, normMeth = c("mz", "TIC", "PQN", "median", "none"), SinglePointRecal = TRUE, verbose = TRUE )fitCurve( spec, unit = c("M", "mM", "uM", "nM", "pM", "fM"), varFilterMethod = c("mean", "median", "q25", "q75", "none"), monoisotopicFilter = FALSE, averageMethod = c("mean", "median", "sum"), normMz = NULL, normTol = 0.1, alignTol = 0.01, binTol = 2e-04, SNR = 3, halfWindowSize = 3, allowNoMatches = TRUE, normMeth = c("mz", "TIC", "PQN", "median", "none"), SinglePointRecal = TRUE, verbose = TRUE )
spec |
List of MALDIquant::MassSpectrum |
unit |
Character, unit of concentration. Used to calculate the concentration in Moles so that pIC50 is correct. Set to "M" if you dont want changes in your concentrations. |
varFilterMethod |
Character, function applied for high variance filtering. One of the following options |
monoisotopicFilter |
Logical, filter peaks and just use monoisotopic peaks for curve fit. |
averageMethod |
Character, aggregation method for average mass spectra ("mean" or "median") |
normMz |
Numeric, mz used for normalization AND for single point recalibration. |
normTol |
Numeric, tolerance in Dalton to match normMz |
alignTol |
Numeric, tolerance for spectral alignment in Dalton. |
binTol |
Numeric, tolerance for binning of peaks. |
SNR |
Numeric, signal to noise ratio for peak detection. |
halfWindowSize |
2ction. See |
allowNoMatches |
Logical, if normMz can not be found in a spectrum, proceed and exclude spectrum or stop |
normMeth |
Character, normalization method. Can either be "TIC", "PQM", "median" or "mz". If "mz" then the normMz is used. If none no normalization is done. |
SinglePointRecal |
Logical, perform single point recalibration to normMz |
verbose |
Logical, print logs to console. |
Object of class MALDIassay.
The most important slot is fits which contains the IC50 curve fits.
data(Blank2022spec) fitCurve(spec = Blank2022spec, SinglePointRecal = TRUE, normMz = 760.585, alignTol = 0.1, normTol = 0.1, varFilterMethod = "mean")data(Blank2022spec) fitCurve(spec = Blank2022spec, SinglePointRecal = TRUE, normMz = 760.585, alignTol = 0.1, normTol = 0.1, varFilterMethod = "mean")
Get all mz value of an MALDIassay-object
getAllMz(object, excludeNormMz = FALSE)getAllMz(object, excludeNormMz = FALSE)
object |
Object of class MALDIassay |
excludeNormMz |
Logical, remove normMz from list of mz values. |
numeric vector of mz values
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getAllMz(Blank2022res))# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getAllMz(Blank2022res))
Extract applied mz-shift
getAppliedMzShift(object)getAppliedMzShift(object)
object |
Object of class MALDIassay |
Numeric vector of mz-shits applied to spectra
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getAppliedMzShift(Blank2022res))# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getAppliedMzShift(Blank2022res))
Extract applied normalization factors
getAppliedNormFactors(object)getAppliedNormFactors(object)
object |
Object of class MALDIassay |
Numeric vector of normalization factors applied to spectra
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getAppliedNormFactors(Blank2022res))# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getAppliedNormFactors(Blank2022res))
Extract peaks of average spectra
getAvgPeaks(object)getAvgPeaks(object)
object |
Object of class MALDIassay |
List of MALDIquantMassPeaks
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getAvgPeaks(Blank2022res)[[1]]# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getAvgPeaks(Blank2022res)[[1]]
Extract average spectra
getAvgSpectra(object)getAvgSpectra(object)
object |
Object of class MALDIassay |
List of MALDIquantMassSpectrum
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getAvgSpectra(Blank2022res)[[1]]# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getAvgSpectra(Blank2022res)[[1]]
Get binning tolerance
getBinTol(object)getBinTol(object)
object |
Object of class MALDIassay |
Numeric, tolerance used for binning in Dalton.
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getBinTol(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getBinTol(Blank2022res)
Extract the concentrations used in a MALDIassay
getConc(object)getConc(object)
object |
Object of class MALDIassay |
Numeric vector, concentrations used in a MALDIassay
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getConc(Blank2022res))# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getConc(Blank2022res))
Extract curve fits
getCurveFits(object)getCurveFits(object)
object |
Object of class MALDIassay |
List, containing the data used to do the fits as well as the nlpr curve fit .
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) fits <- getCurveFits(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) fits <- getCurveFits(Blank2022res)
Extract directory path
getDirectory(object)getDirectory(object)
object |
Object of class MALDIassay |
List, containing the data used to do the fits as well as the nlpr curve fit .
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getDirectory(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getDirectory(Blank2022res)
Get fitting parameters
getFittingParameters(object, summarise = FALSE)getFittingParameters(object, summarise = FALSE)
object |
Object of class MALDIassay |
summarise |
Logical, remove everything other then npar and mz from result. |
tibble of fitting parameters for each fitted m/z-value
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getFittingParameters(Blank2022res, summarise = FALSE))# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getFittingParameters(Blank2022res, summarise = FALSE))
Get the intensity matrix of single spectra for all fitted curves
getIntensityMatrix(object, avg = FALSE, excludeNormMz = FALSE)getIntensityMatrix(object, avg = FALSE, excludeNormMz = FALSE)
object |
Object of class MALDIassay |
avg |
Logical, return single spectra intensity matrix (default) or average spectra intensity matrix |
excludeNormMz |
Logical, exclude normMz from intensity matrix. |
Note that the returned matrix only contains m/z values that were actually fitted.
If a variance filtering step was applied this will not include all m/z values.
If you wish to get a matrix of all m/z values use MALDIquant::intensityMatrix(getSinglePeaks(object)).
For average spectra intensity matrix with all m/z values use MALDIquant::intensityMatrix(getAvgPeaks(object), getAvgSpectra(object)).
A matrix with columns as m/z values and rows as concentrations/spectra
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getIntensityMatrix(Blank2022res, avg = TRUE, excludeNormMz = TRUE) )# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getIntensityMatrix(Blank2022res, avg = TRUE, excludeNormMz = TRUE) )
Get the mz value associated with a mzIdx
getMzFromMzIdx(object, mzIdx)getMzFromMzIdx(object, mzIdx)
object |
Object of class MALDIassay |
mzIdx |
numeric, index of mass of interest (see |
numeric, mz value
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getMzFromMzIdx(Blank2022res, mzIdx = 2)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getMzFromMzIdx(Blank2022res, mzIdx = 2)
Get mass shift for target mz
getMzShift(peaks, targetMz, tol, tolppm = FALSE, verbose = TRUE)getMzShift(peaks, targetMz, tol, tolppm = FALSE, verbose = TRUE)
peaks |
List of MALDIquant::MassPeak |
targetMz |
Numeric, target mass |
tol |
Numeric, tolerance around targetMz |
tolppm |
Logical, tolerance supplied in ppm |
verbose |
Logical, print logs to the console. |
List with two entries:
MzShift The mass shift for each spectrum
specIdx The index of the spectra with a match for targetMz
data(Blank2022peaks) getMzShift(Blank2022peaks, targetMz = 760.585, tol = 0.1, tolppm = FALSE)data(Blank2022peaks) getMzShift(Blank2022peaks, targetMz = 760.585, tol = 0.1, tolppm = FALSE)
Get normalization factors from peak data.frame
getNormFactors(peaksdf, targetMz, tol, tolppm = TRUE, allowNoMatch = TRUE)getNormFactors(peaksdf, targetMz, tol, tolppm = TRUE, allowNoMatch = TRUE)
peaksdf |
data.frame with peaks information as generated by peaks2df() |
targetMz |
Numeric, target mass |
tol |
Numeric, tolerance around targetMz |
tolppm |
Logical, is the tolerance provided in ppm (TRUE) or Daltion (FALSE) |
allowNoMatch |
Logical, stop if targetMz is not fround in single spectrum? If TRUE spectra without targetMz match will be excluded. |
List with two entries:
norm_factor The normalization factor for each spectrum
specIdx The index of the spectra with a match for targetMz
data(Blank2022peaks) getNormFactors(peaks2df(Blank2022peaks), targetMz = 760.585, tol = 0.1, tolppm = FALSE)data(Blank2022peaks) getNormFactors(peaks2df(Blank2022peaks), targetMz = 760.585, tol = 0.1, tolppm = FALSE)
Extract normalization method
getNormMethod(object)getNormMethod(object)
object |
Object of class MALDIassay |
Character, normalization method used.
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getNormMethod(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getNormMethod(Blank2022res)
Extract m/z used for normalization
getNormMz(object)getNormMz(object)
object |
Object of class MALDIassay |
Numeric, m/z used for normalization
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getNormMz(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getNormMz(Blank2022res)
Extract tolerance used for normalization
getNormMzTol(object)getNormMzTol(object)
object |
Object of class MALDIassay |
Numeric, tolerance used for normalization
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getNormMzTol(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getNormMzTol(Blank2022res)
Extract peak statistics
getPeakStatistics(object, summarise = FALSE)getPeakStatistics(object, summarise = FALSE)
object |
Object of class MALDIassay |
summarise |
Logical, return summarized results (one result per mz and not per mz and spectra) |
A tibble with peak statistics (R², fold-change, CV%, etc.)
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getPeakStatistics(Blank2022res, summarise = TRUE))# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getPeakStatistics(Blank2022res, summarise = TRUE))
Calculate remaining calibration error of a MALDIassay object
getRecalibrationError(object)getRecalibrationError(object)
object |
Object of class MALDIassay |
A tibble containing statistics about remaining calibration error
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getRecalibrationError(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getRecalibrationError(Blank2022res)
Extract peaks of single spectra spectra (before average calculation)
getSinglePeaks(object)getSinglePeaks(object)
object |
Object of class MALDIassay |
List of MALDIquantMassPeaks
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getSinglePeaks(Blank2022res)[[1]]# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getSinglePeaks(Blank2022res)[[1]]
Extract the intensities of single spectra for a given mzIdx
getSingleSpecIntensity(object, mz_idx)getSingleSpecIntensity(object, mz_idx)
object |
Object of class MALDIassay |
mz_idx |
Integer, index of mz |
Numeric vector, intensities of mzIdx
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getSingleSpecIntensity(Blank2022res, 2))# see example for `fitCurve()` to see how this data was generated data(Blank2022res) head(getSingleSpecIntensity(Blank2022res, 2))
Extract SNR used for peak detection
getSNR(object)getSNR(object)
object |
Object of class MALDIassay |
Numeric, SNR used for peak detection
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getSNR(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getSNR(Blank2022res)
Get the spot coordinates of spectra
getSpots(object, singleSpec = TRUE)getSpots(object, singleSpec = TRUE)
object |
Object of class MALDIassay |
singleSpec |
Logical, extract the spot coordinates of single spectra (default) or from average spectra. |
character vector of spot coordinates. In case of average spectra multiple spots are concatenated.
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) # spots per spectrum getSpots(Blank2022res, singleSpec = TRUE) #spots per concentration getSpots(Blank2022res, singleSpec = FALSE)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) # spots per spectrum getSpots(Blank2022res, singleSpec = TRUE) #spots per concentration getSpots(Blank2022res, singleSpec = FALSE)
Extract variance filtering method
getVarFilterMethod(object)getVarFilterMethod(object)
object |
Object of class MALDIassay |
Character of variance filtering method used
# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getVarFilterMethod(Blank2022res)# see example for `fitCurve()` to see how this data was generated data(Blank2022res) getVarFilterMethod(Blank2022res)
Check if object if of class MALDIassay
isMALDIassay(object)isMALDIassay(object)
object |
object to text |
logical, TRUE if object is of class MALDIassay
x <- 1 # FALSE isMALDIassay(x) # TRUE isMALDIassay(Blank2022res)x <- 1 # FALSE isMALDIassay(x) # TRUE isMALDIassay(Blank2022res)
load bruker MALDI target plate spectra
loadSpectra(Dir, filter = NA, nameSpectra = TRUE, verbose = TRUE)loadSpectra(Dir, filter = NA, nameSpectra = TRUE, verbose = TRUE)
Dir |
Character, parent directory of spectra. |
filter |
Character vector, filter out spectra which match the given vector. |
nameSpectra |
Logical, if TRUE the spectra in the resulting list will be named according to the dirname. |
verbose |
Logical, print logs to the console. |
List of MALDIquant::MassSpectra
dataDir <- system.file("extdata", package="MALDIcellassay") unzip(file.path(dataDir, "example-raw-spectra.zip")) loadSpectra("example-raw-spectra/") unlink("example-raw-spectra/", recursive = TRUE)dataDir <- system.file("extdata", package="MALDIcellassay") unzip(file.path(dataDir, "example-raw-spectra.zip")) loadSpectra("example-raw-spectra/") unlink("example-raw-spectra/", recursive = TRUE)
load mzML spectra
loadSpectraMzML(Dir, filter = NA, nameSpectra = TRUE, verbose = TRUE)loadSpectraMzML(Dir, filter = NA, nameSpectra = TRUE, verbose = TRUE)
Dir |
Character, parent directory of spectra. |
filter |
Character vector, filter out spectra which match the given vector. |
nameSpectra |
Logical, if TRUE the spectra in the resulting list will be named according to the dirname. |
verbose |
Logical, print logs to console |
List of MALDIquant::MassSpectra
dataDir <- system.file("extdata", package="MALDIcellassay") loadSpectraMzML(file.path(dataDir, "Koch2024mzML"))dataDir <- system.file("extdata", package="MALDIcellassay") loadSpectraMzML(file.path(dataDir, "Koch2024mzML"))
A class for holding MALDI assay related information.
object |
MALDIassay. |
Normalize spectra and peaks
normalize(spec, peaks, normMeth, normMz, normTol)normalize(spec, peaks, normMeth, normMz, normTol)
spec |
List of MALDIquant::MassSpectrum |
peaks |
List of MALDIquant::MassPeaks |
normMeth |
Character, normalization method. Options are "TIC", "median" and "mz". |
normMz |
Numeric, mz used to normalize. |
normTol |
Numeric, tolerance around normMz. |
List of lists of normalized MALDIquant::MassSpectrum, normalized MALDIquant::MassPeaks,
normalization factors as well as indicies of spectra containing the normMz in case of normMeth = "mz",
data(Blank2022spec) data(Blank2022peaks) norm <- normalize(Blank2022spec, Blank2022peaks, normMeth = "mz", normMz = 760.585, normTol = 0.1) # normalization factors norm$factordata(Blank2022spec) data(Blank2022peaks) norm <- normalize(Blank2022spec, Blank2022peaks, normMeth = "mz", normMz = 760.585, normTol = 0.1) # normalization factors norm$factor
Apply normalization factors to spectra
normalizeByFactor(spec, factors)normalizeByFactor(spec, factors)
spec |
List of MALDIquant::MassSpectrum or MALDIquant::MassPeaks |
factors |
Numeric vector of normalization factors. See getNormFactors(). |
List of normalized Spectra or Peaks
#' data(Blank2022peaks) normFactors <- getNormFactors(peaks2df(Blank2022peaks), targetMz = 760.585, tol = 0.1, tolppm = FALSE) normPeaks <- normalizeByFactor(Blank2022peaks, normFactors$norm_factor)#' data(Blank2022peaks) normFactors <- getNormFactors(peaks2df(Blank2022peaks), targetMz = 760.585, tol = 0.1, tolppm = FALSE) normPeaks <- normalizeByFactor(Blank2022peaks, normFactors$norm_factor)
Convert a list of peaks to a data.frame
peaks2df(peaks)peaks2df(peaks)
peaks |
(list of) MALDIquant::MassPeaks |
Data.frame with peak data
data(Blank2022peaks) peakdf <- peaks2df(Blank2022peaks[1:2]) head(peakdf)data(Blank2022peaks) peakdf <- peaks2df(Blank2022peaks[1:2]) head(peakdf)
generate ggplot objects for each of the curve fits in a MALDIassay object
plotCurves(object, mzIdx = NULL, errorbars = c("none", "sd", "sem"))plotCurves(object, mzIdx = NULL, errorbars = c("none", "sd", "sem"))
object |
object of class MALDIassay |
mzIdx |
numeric, indicies of mz values to plot (see |
errorbars |
character, add error bars to plot. Either standard error of the mean ( |
list of ggplot objects
data(Blank2022res) plotCurves(Blank2022res, mzIdx = 2, errorbars = "sd")data(Blank2022res) plotCurves(Blank2022res, mzIdx = 2, errorbars = "sd")
Plot a peak of interest from a MALDIassay object
plotPeak(object, mzIdx, tol = 0.8)plotPeak(object, mzIdx, tol = 0.8)
object |
object of class MALDIassay |
mzIdx |
numeric, index of mass of interest (see |
tol |
numeric, tolerance around peak to plot |
ggplot object
data(Blank2022res) plotPeak(Blank2022res, mzIdx = 2)data(Blank2022res) plotPeak(Blank2022res, mzIdx = 2)
This is a fork from sgibb's MALDIquant::averageMassSpectra() function. It is now able to compute "standard-deviation spectra".
sdMassSpectrum(l, labels, ...)sdMassSpectrum(l, labels, ...)
l |
list, list of MassSpectrum objects. |
labels |
list, list of factors (one for each MassSpectrum object) to do groupwise averaging. |
... |
arguments to be passed to underlying functions (currently only mc.cores is supported). |
Returns a single (no labels given) or a list (labels given) of standard-deviation spectra as MassSpectrum objects.
data(Blank2022spec) sdMassSpectrum(Blank2022spec, labels = names(Blank2022spec))[[1]]data(Blank2022spec) sdMassSpectrum(Blank2022spec, labels = names(Blank2022spec))[[1]]
Shift mass axis
shiftMassAxis(spec, mzdiff)shiftMassAxis(spec, mzdiff)
spec |
List of MALDIquant::MassSpectrum or MALDIquant::MassPeaks |
mzdiff |
Numeric vector, see getMzShift() |
List of MALDIquant::MassSpectrum or MALDIquant::MassPeaks with shifted mass axis.
data(Blank2022spec) # raw mz head(Blank2022spec[[1]]@mass) # shifted mz shifted <-shiftMassAxis(Blank2022spec[1:2], c(0.5, 0.5)) head(shifted[[1]]@mass)data(Blank2022spec) # raw mz head(Blank2022spec[[1]]@mass) # shifted mz shifted <-shiftMassAxis(Blank2022spec[1:2], c(0.5, 0.5)) head(shifted[[1]]@mass)
Convert concentration to log10 and replace zero's
transformConc2Log(conc)transformConc2Log(conc)
conc |
numeric, concentrations. |
numeric, log10 transformed concentrations
transformConc2Log(c(0.1, 0.01,0.001))transformConc2Log(c(0.1, 0.01,0.001))