quantification: Protein sample quantification or group quantification

Description Usage Arguments Details Value Author(s) References Examples

View source: R/Quantification.R

Description

Model-based quantification for each condition or for each biological samples per protein in a targeted Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) 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

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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

Value

data.frame as described in details.

Author(s)

Meena Choi, Olga Vitek.

Maintainer: Meena Choi (mnchoi67@gmail.com)

References

Meena Choi, Ching-Yun Chang, Timothy Clough, Daniel Broudy, Trevor Killeen, Brendan MacLean and Olga Vitek. "MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments" Bioinformatics, 30(17):2524-2526, 2014.

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

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# 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.

QuantData<-dataProcess(SRMRawData)
head(QuantData$ProcessedData)

# Sample quantification

sampleQuant<-quantification(QuantData)
head(sampleQuant)

# Group quantification

groupQuant<-quantification(QuantData, type="Group")
head(groupQuant)

Example output

 DONE : Incomplete rows for missing peaks are added with intensity values=NA. 

** Log2 endogenous intensities under cutoff = 3.776  were considered as censored missing values.
** Log2 endogenous intensities = NA were considered as censored missing values.
** Use all features that the dataset origianally has.
                       
  Summary of Features :
                         count
# of Protein                 2
# of Peptides/Protein      2-2
# of Transitions/Peptide   3-3
                      
  Summary of Samples :
                           1 2 3 4 5 6 7 8 9 10
# of MS runs               3 3 3 3 3 3 3 3 3  3
# of Biological Replicates 3 3 3 3 3 3 3 3 3  3
# of Technical Replicates  1 1 1 1 1 1 1 1 1  1

 Summary of Missingness :

  # transitions are completely missing in at least one of the conditions : 0


  # run with 75% missing observations: 0


 == Start the summarization per subplot...

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |======================================================================| 100%

 == the summarization per subplot is done.
   PROTEIN             PEPTIDE TRANSITION                    FEATURE LABEL
1     IDHC     ATDVIVPEEGELR_2      y7_NA      ATDVIVPEEGELR_2_y7_NA     H
3     IDHC     ATDVIVPEEGELR_2      y8_NA      ATDVIVPEEGELR_2_y8_NA     H
5     IDHC     ATDVIVPEEGELR_2      y9_NA      ATDVIVPEEGELR_2_y9_NA     H
7     IDHC DQTNDQVTVDSATATLK_2     y10_NA DQTNDQVTVDSATATLK_2_y10_NA     H
9     IDHC DQTNDQVTVDSATATLK_2     y11_NA DQTNDQVTVDSATATLK_2_y11_NA     H
11    IDHC DQTNDQVTVDSATATLK_2      y8_NA  DQTNDQVTVDSATATLK_2_y8_NA     H
   GROUP_ORIGINAL SUBJECT_ORIGINAL RUN GROUP SUBJECT INTENSITY SUBJECT_NESTED
1               1            ReplA   1     0       0 84361.083            0.0
3               1            ReplA   1     0       0 29778.102            0.0
5               1            ReplA   1     0       0 17921.293            0.0
7               1            ReplA   1     0       0  4481.229            0.0
9               1            ReplA   1     0       0  1871.042            0.0
11              1            ReplA   1     0       0  2640.060            0.0
   ABUNDANCE FRACTION originalRUN censored SuggestToFilter
1   15.85586        1           1    FALSE               0
3   14.35353        1           1    FALSE               0
5   13.62096        1           1    FALSE               0
7   11.62125        1           1    FALSE               0
9   10.36120        1           1    FALSE               0
11  10.85792        1           1    FALSE               0
  Protein  1_ReplA   1_ReplB   1_ReplC   2_ReplA  2_ReplB   2_ReplC   3_ReplA
1    IDHC  5.57705  6.811034  6.909093  6.373069  6.59575  6.349843  6.140378
2    PMG2 10.75693 10.647513 10.530769 10.057710 10.47072 10.613419 10.667443
    3_ReplB   3_ReplC   4_ReplA   4_ReplB   4_ReplC   5_ReplA   5_ReplB
1  6.600981  7.033944  6.790116  6.174932  7.313115  7.260217  7.209141
2 10.654398 10.419662 10.773446 10.535321 10.696021 10.461630 10.945504
    5_ReplC  6_ReplA   6_ReplB   6_ReplC  7_ReplA  7_ReplB  7_ReplC  8_ReplA
1  6.519457 9.653219  9.542782  9.553527 12.55638 12.65310 12.55085 12.72204
2 10.572543 9.943402 10.595059 10.587633 10.56620 10.72296 10.37506 10.22503
   8_ReplB  8_ReplC  9_ReplA  9_ReplB  9_ReplC 10_ReplA 10_ReplB 10_ReplC
1 12.78281 12.80676 12.66386 12.72873 12.66681 12.72029 12.77645 12.71629
2 10.45177 10.39042 10.27202 10.57575 10.39904 10.40899 10.60595 10.34241
  Protein         1         2         3         4         5         6        7
1    IDHC  6.811034  6.373069  6.600981  6.790116  7.209141  9.553527 12.55638
2    PMG2 10.647513 10.470722 10.654398 10.696021 10.572543 10.587633 10.56620
         8        9       10
1 12.78281 12.66681 12.72029
2 10.39042 10.39904 10.40899

MSstats documentation built on Feb. 28, 2021, 2:01 a.m.