MS analysis in R

Description Usage Arguments Details Value Author(s) References

Massifquant is a Kalman filter (KF) based feature detection for XC-MS data in centroid mode.

1 2	massifquant(scans, info, ppm, minIntensity, minScans, consecMissedLim, criticalVal, combine, checkBack)

`scans`	A list of matrices with two columns. First column holds the mz values for a single scan, second column holds the intensities.
`info`	A data.frame with information for each scan. Should minimally hold a retentionTime, seqNum and msLevel column.
`ppm`	The minimum estimated parts per million mass resolution a feature must possess.
`minIntensity`	The minimum threshold for the maximum intensity of a feature that must be met.
`minScans`	The minimum feature length in number of scans that a peak must have.
`consecMissedLim`	Integer: Suggested values:(1,2,3). While a feature is in the proces of being detected by a Kalman Filter, the Kalman Filter may not find a predicted centroid in every scan. After 1 or more consecutive failed predictions, this setting informs Massifquant when to stop a Kalman Filter from following a candidate feature.
`combine`	Integer: set to 1 if apply t-test union on segmentation; set to 0 if no t-test to be applied on chromatographically continous features sharing same m/z range. Explanation: With very few data points, sometimes a Kalman Filter stops tracking a feature prematurely. Another Kalman Filter is instantiated and begins following the rest of the signal. Because tracking is done backwards to forwards, this algorithmic defect leaves a real feature divided into two segments or more. With this option turned on, the program identifies segmented features and combines them (merges them) into one with a two sample t-test. The potential danger of this option is that some truly distinct features may be merged.
`checkBack`	Integer: set to 1 if turned on; set to 0 if turned off. The convergence of a Kalman Filter to a feature's precise m/z mapping is very fast, but sometimes it incorporates erroneous centroids as part of a feature (especially early on). The "scanBack" option is an attempt to remove the occasional outlier that lies beyond the converged bounds of the Kalman Filter. The option does not directly affect identification of a feature because it is a postprocessing measure; it has not shown to be a extremely useful thus far and the default is set to being turned off.
`criticalValue`	Numeric: Suggested values: (0.1-3.0). This setting helps determine the the Kalman Filter prediciton margin of error. A real centroid belonging to a bonafide feature must fall within the KF prediction margin of error. Much like in the construction of a confidence interval, criticalVal loosely translates to be a multiplier of the standard error of the prediction reported by the Kalman Filter. If the features in the XC-MS sample have a small mass deviance in ppm error, a smaller critical value might be better and vice versa.

This algorithm's performance has been tested rigorously on high resolution LC/OrbiTrap, TOF-MS data in centroid mode. Simultaneous kalman filters identify features and calculate their area under the curve. The default parameters are set to operate on a complex LC-MS Orbitrap sample. Users will find it useful to do some simple exploratory data analysis to find out where to set a minimum intensity, and identify how many scans an average feature spans. The "consecMissedLimit" parameter has yielded good performance on Orbitrap data when set to (2) and on TOF data it was found best to be at (1). This may change as the algorithm has yet to be tested on many samples. The "criticalValue" parameter is perhaps most dificult to dial in appropriately and visual inspection of peak identification is the best suggested tool for quick optimization. The "ppm" and "checkBack" parameters have shown less influence than the other parameters and exist to give users flexibility and better accuracy.

A data.frame with a row for each detected peak. The data.frame has the following columns: mz, mzmin, mzmax, scmin, scmax, length, intensity, maxint and peak. The peak column holds the sequential intensity values for each peak.

Christopher Conley and Thomas Lin Pedersen

Conley, C. J., Smith, R., Torgrip, R. J. O., Taylor, R. M., Tautenhahn, R., & Prince, J. T. (2014). Massifquant: open-source Kalman filter-based XC-MS isotope trace feature detection. Bioinformatics. doi:10.1093/bioinformatics/btu359

thomasp85/MSsary documentation built on May 31, 2019, 11:11 a.m.