quantification: Protein sample quantification or group quantification
In MSstats: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments
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
1
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 : individual biological sample quantification for each protein. The label of each biological sample is a combination of the corresponding group and the sample ID. If there are no technical replicates or experimental replicates per sample, sample quantification is the same as run summarization from dataProcess. If there are technical replicates or experimental replicates, sample quantification is median among run quantification corresponding MS runs.
Group quantification : quantification for individual group or individual condition per protein. It is median among sample quantification.
The quantification for endogenous samples is based on run summarization from subplot model, with TMP robust estimation.
The input of this function is the quantitative data from function (dataProcess).
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.
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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
1 | 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 : individual biological sample quantification for each protein. The label of each biological sample is a combination of the corresponding group and the sample ID. If there are no technical replicates or experimental replicates per sample, sample quantification is the same as run summarization from dataProcess. If there are technical replicates or experimental replicates, sample quantification is median among run quantification corresponding MS runs.
Group quantification : quantification for individual group or individual condition per protein. It is median among sample quantification.
The quantification for endogenous samples is based on run summarization from subplot model, with TMP robust estimation.
The input of this function is the quantitative data from function (
dataProcess).
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # 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
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