Comparelcms simengine: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
Select variables that are different between related data sets, e.g. mass chromatograms from LC/MS data of different batches. | Select variables that are different between related data sets, e.g. mass chromatograms from LC/MS data of different batches. | ||
===Synopsis=== | ===Synopsis=== | ||
:y=comparelcms_simengine(data,filter_width) | :y=comparelcms_simengine(data,filter_width) | ||
===Description=== | ===Description=== | ||
COMPARELCMS_SIMENGINE determines which variables are different between different data sets. For example, after applying coda_dw to LC/MS data sets of highly related samples, such as the data of a good and a bad batch, the results will be very similar. comparelcms_engine takes the next step and extracts the mass chromatograms that are different. This function is normally not called by itself but by the function comparelcms_sim_interactive. The input argument data is a data cube with mode 1 the number of samples, mode two the number of spectra and mode 3 the number of variables, The optional input argument filter_width is used to smooth the columns of the data set in order to minimize the effect of small shifts, The output argument y contains the similarity indices of the variables. Variables with a low similarity index show the differences between the data sets. | COMPARELCMS_SIMENGINE determines which variables are different between different data sets. For example, after applying coda_dw to LC/MS data sets of highly related samples, such as the data of a good and a bad batch, the results will be very similar. comparelcms_engine takes the next step and extracts the mass chromatograms that are different. This function is normally not called by itself but by the function comparelcms_sim_interactive. The input argument data is a data cube with mode 1 the number of samples, mode two the number of spectra and mode 3 the number of variables, The optional input argument filter_width is used to smooth the columns of the data set in order to minimize the effect of small shifts, The output argument y contains the similarity indices of the variables. Variables with a low similarity index show the differences between the data sets. | ||
===Examples=== | ===Examples=== | ||
Determination of similarity indices with a filter of 7 data points. | Determination of similarity indices with a filter of 7 data points. | ||
y=comparelcms_simengine(data,7) | |||
===Algorithm=== | ===Algorithm=== | ||
The calculations are based on a similarity index of the minimum of the chromatograms (across the samples) and the maximum of the chromatograms. | The calculations are based on a similarity index of the minimum of the chromatograms (across the samples) and the maximum of the chromatograms. | ||
===See Also=== | ===See Also=== | ||
[[comparelcms_sim_interactive]] | [[comparelcms_sim_interactive]] | ||
Latest revision as of 19:45, 7 October 2008
Purpose
Select variables that are different between related data sets, e.g. mass chromatograms from LC/MS data of different batches.
Synopsis
- y=comparelcms_simengine(data,filter_width)
Description
COMPARELCMS_SIMENGINE determines which variables are different between different data sets. For example, after applying coda_dw to LC/MS data sets of highly related samples, such as the data of a good and a bad batch, the results will be very similar. comparelcms_engine takes the next step and extracts the mass chromatograms that are different. This function is normally not called by itself but by the function comparelcms_sim_interactive. The input argument data is a data cube with mode 1 the number of samples, mode two the number of spectra and mode 3 the number of variables, The optional input argument filter_width is used to smooth the columns of the data set in order to minimize the effect of small shifts, The output argument y contains the similarity indices of the variables. Variables with a low similarity index show the differences between the data sets.
Examples
Determination of similarity indices with a filter of 7 data points.
y=comparelcms_simengine(data,7)
Algorithm
The calculations are based on a similarity index of the minimum of the chromatograms (across the samples) and the maximum of the chromatograms.