msresample

Resample signal with peaks

Syntax

[Xout, Intensitiesout] = msresample(X, Intensities, N)
msresample(..., 'Uniform', UniformValue, ...)
msresample(..., 'Range', RangeValue, ...)
msresample(..., 'RangeWarnOff', RangeWarnOffValue, ...)
msresample(..., 'Missing', MissingValue, ...)
msresample(..., 'Window', WindowValue, ...)
msresample(..., 'Cutoff', CutoffValue, ...)
msresample(..., 'ShowPlot', ShowPlotValue, ...)


Arguments

X	Vector of separation-unit values for a set of signals with peaks. The number of elements in the vector equals the number of rows in the matrix Intensities. The separation unit can quantify wavelength, frequency, distance, time, or m/z depending on the instrument that generates the signal data.
Intensities	Matrix of intensity values for a set of peaks that share the same separation-unit range. Each row corresponds to a separation-unit value, and each column corresponds to either a set of signals with peaks or a retention time. The number of rows equals the number of elements in vector X.
N	Positive integer specifying the total number of samples.

Description

Tip

Use the following syntaxes with data from any separation technique that produces signal data, such as spectroscopy, NMR, electrophoresis, chromatography, or mass spectrometry.

[Xout, Intensitiesout] = msresample(X, Intensities, N) resamples raw noisy signal data, Intensities. The output signal has N samples with a spacing that increases linearly within the range [min(X) max(X)]. X can be a linear or a quadratic function of its index. When you set input arguments such that down-sampling takes place, msresample applies a lowpass filter before resampling to minimize aliasing.

For the antialias filter, msresample uses a linear-phase FIR filter with a least-squares error minimization. The cutoff frequency is set by the largest down-sampling ratio when comparing the same regions in the X and Xout vectors.

Tip

msresample is particularly useful when you have signals with different separation-unit vectors and you want to match the scales.

msresample(..., 'PropertyName', PropertyValue, ...) calls msresample with optional properties that use property name/property value pairs. You can specify one or more properties in any order. Each PropertyName must be enclosed in single quotes and is case insensitive. These property name/property value pairs are as follows:

msresample(..., 'Uniform', UniformValue, ...), when UniformValue is true, it forces the vector X to be uniformly spaced. The default value is false.

msresample(..., 'Range', RangeValue, ...) specifies a 1-by-2 vector with the separation-unit range for the output signal, Intensitiesout. RangeValue must be within [min(X) max(X)]. Default value is the full range [min(X) max(X)]. When RangeValue values exceed the values in X, msresample extrapolates the signal with zeros and returns a warning message.

msresample(..., 'RangeWarnOff', RangeWarnOffValue, ...) controls the return of a warning message when RangeValue values exceed the values in X. RangeWarnOffValue can be true or false (default).

msresample(..., 'Missing', MissingValue, ...), when MissingValue is true, analyzes the input vector, X, for dropped samples. The default value is false. If the down-sample factor is large, checking for dropped samples might not be worth the extra computing time. Dropped samples can only be recovered if the original separation-unit values follow a linear or a quadratic function of the X vector index.

msresample(..., 'Window', WindowValue, ...) specifies the window used when calculating parameters for the lowpass filter. Enter 'Flattop', 'Blackman', 'Hamming', or 'Hanning'. The default value is 'Flattop'.

msresample(..., 'Cutoff', CutoffValue, ...) specifies the cutoff frequency. Enter a scalar value from 0 to 1 (Nyquist frequency or half the sampling frequency). By default, msresample estimates the cutoff value by inspecting the separation-unit vectors, X and XOut. However, the cutoff frequency might be underestimated if X has anomalies.

msresample(..., 'ShowPlot', ShowPlotValue, ...) plots the original and the resampled signal. When msresample is called without output arguments, the signals are plotted unless ShowPlotValue is false. When ShowPlotValue is true, only the first signal in Intensities is plotted. ShowPlotValue can also contain an index to one of the signals in Intensities.

Tip

LC/MS data analysis requires extended amounts of memory from the operating system.

• If you receive errors related to memory, try the following:

  • Increase the virtual memory (swap space) for your operating system as described in Resolve “Out of Memory” Errors.

• If you receive errors related to Java^® heap space, increase your Java heap space:

  • If you have MATLAB^® version 7.10 (R2010a) or later, see Java Heap Memory Settings.

  • If you have MATLAB version 7.9 (R2009b) or earlier, see https://www.mathworks.com/matlabcentral/answers/92813-how-do-i-increase-the-heap-space-for-the-java-vm-in-matlab.

Examples

Resample Mass Spectrometry Data

This example shows how to resample mass spec data.

Load a MAT-file, included with Bioinformatics Toolbox™, that contains mass spectrometry data, and then extract m/z and intensity value vectors.

load sample_hi_res;
mz = MZ_hi_res;
y = Y_hi_res;

Plot the original data.

plot(mz, y, '.')

Resample the spectrogram to have 10000 samples between 2000 and maximum m/z value in the data set, and show both the resampled and original data.

[mz1,y1] = msresample(mz, y, 10000, 'range',[2000 max(mz)],'SHOWPLOT',true);

Version History

Introduced before R2006a