MSstats (version 2.4.0)

quantification: Protein sample quantification or group quantification

Description

Model-based quantification for each condition or for each biological samples per protein in a label-free and label-based LC-MS, SRM and DIA experiment. Quantification takes the processed data set by dataProcess as input and automatically generate the quantification results (data.frame) with long or matrix format.

Usage

quantification(data, type="Sample", format="matrix")

Arguments

data

name of the (processed) data set.

type

choice of quantification. "Sample" or "Group" for protein sample quantification or group quantification.

format

choice of returned format. "long" for long format which has the columns named Protein, Condition, LonIntensities (and BioReplicate if it is subject quantification), NumFeature for number of transitions for a protein, and NumPeaks for number of observed peak intensities for a protein. "matrix" for data matrix format which has the rows for Protein and the columns, which are Groups(or Conditions) for group quantification or the combinations of BioReplicate and Condition (labeled by "BioReplicate"_"Condition") for sample quantification. Default is "matrix"

Details

• Sample quantification : model-based individual biological sample quantification for each protein. The label of each biological sample is a combination of the corresponding group and the sample ID. The same model with groupComparison will be used. However, if there is only one transition in a certain protein, the estimate of variation is NA. Therefore, the result may be unreliable.

Group quantification : model-based quantification for individual group or individual condition per protein. The same model with groupComparison will be used. The quantification for reference is the average among all reference intensities.

The quantification for endogenous samples is based on the log-intensities of model-based averaging of all endogenous transitions within a specific sample. The quantification for reference sample is based on the log-intensities of the model-based averaging among all reference transitions. The quantification of log-ratios of specific endogenous sample over reference sample can be obtained by the quantification of that endogenous sample minus the quantification of the reference sample.

The input of this function is the quantitative data from function (dataProcess). The example data is QuantData.

References

Ching-Yun Chang, Paola Picotti, Ruth Huttenhain, Viola Heinzelmann-Schwarz, Marko Jovanovic, Ruedi Aebersold, Olga Vitek. "Protein significance analysis in selected reaction monitoring (SRM) measurements." Molecular & Cellular Proteomics, 11:M111.014662, 2012.

Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek. "Statistical protein quantification and significance analysis in label-free LC-M experiments with complex designs" BMC Bioinformatics, 13:S16, 2012.

Examples

#Consider quantitative data (i.e. QuantData) from a yeast study with ten time points of interests, three biological replicates, and no technical replicates which is a time-course experiment. 
#Sample quantification shows model-based estimation of protein abundance in each biological replicate within each time point.
#Group quantification shows model-based estimation of protein abundance in each time point.

head(QuantData)

# Sample quantification

quantification(QuantData)

# Group quantification

quantification(QuantData, type="Group")