MSstatsBioData: Datasets of published biological studies with DDA or SRM experiments

Package: MSstatsBioData
Author: Meena Choi
Date: October 2, 2017

Overview

This package provides the peak intensities datasets from seven biological investigations in DDA or SRM experiments. Experimental design, data acqusition and processing steps by spectral processing tools are as described in publications [1-5]. The datasets are formatted as MSstats required format (package MSstats). For usage of the data for differential abundance analysis, please refer to the MSstats vignette.

Overview of the datasets included in MSstatsBioData:

object description
DDA_cardio DDA dataset for cardiovascular disease study [1]
SRM_crc_training The training set from a study for subjects with colorectal cancer [2]
SRM_crc_validation The validation set from a study for subjects with colorectal cancer [2]
SRM_mpm_training The training set from a study of subjects with malignant pleural mesothelioma(MPM) [3]
SRM_mpm_validation The validation set from a study of subjects with malignant pleural mesothelioma(MPM) [3]
SRM_ovarian SRM dataset for a study of subjects with ovarian cancer [4]
SRM_yeast Time course investigation of central carbon metabolism of \emph{S. cerevisiae} [5]

Data structure

All datasets in the package are represented in a data.frame with 10 columns, as MSstats rsequired format :

column description
ProteinName Protein id.
PeptideSequence Peptide sequence or modified peptide sequence
PrecursorCharge Precursor charge
FragmentIon fragment
ProductCharge normalized microarray data, RNA-seq data or developmental effect score
IsotopeLabelType endogenous peptides (use L) or labeled reference peptides (use H)
Condition indicates groups of interest
BioReplicate a unique identifier for each biological replicate in the experiment
Run identifier of a mass spectrometry run
Intensity the quantified signal of a feature in a run without any transformation (the peak height or the peak of area under curve)

Data manipulation

Intensities are normalized in SRM_crc_training and SRM_crc_validation

Two steps-normalization in log2 transformed intensity were already performed for these two datsets as described in manuscript. First they are normalized by equalized median using heavy reference peptides. Second, endogenous peptides are normalized by two standard proteins (AIAG and FETUA). After normalization, intensity value was came back to original scale from log2 transformation. These normalizations were performed by each dataset. User do not need extra normalization.

Comments for some datasets

no missing in DDA_cardio

This DDA dataset had no missing values, unusally, because the procedure reported the background signal if a feature in a run was not detected.

How to use dataset

Here one example (SRM_yeast) of differential abundance analysis by MSstats will be shown. Other example will be available in help file for each datasets.

1.Read the data.

## load package
require(MSstatsBioData)

## require SRM_yeast data
data(SRM_yeast)
## look at first lines
head(SRM_yeast)
##   ProteinName PeptideSequence PrecursorCharge FragmentIon ProductCharge IsotopeLabelType Condition
## 1        ACEA EILGHEIFFDWELPR               3          y3             0                H         1
## 2        ACEA EILGHEIFFDWELPR               3          y3             0                L         1
## 3        ACEA EILGHEIFFDWELPR               3          y4             0                H         1
## 4        ACEA EILGHEIFFDWELPR               3          y4             0                L         1
## 5        ACEA EILGHEIFFDWELPR               3          y5             0                H         1
## 6        ACEA EILGHEIFFDWELPR               3          y5             0                L         1
##   BioReplicate Run    Intensity
## 1        ReplA   1  66472.38470
## 2        ReplA   1   5764.16228
## 3        ReplA   1 101005.16640
## 4        ReplA   1     61.65238
## 5        ReplA   1  90055.49930
## 6        ReplA   1    472.69180

2. Load MSstats package and process the data

## Example of using MSstats for differential abundance analysis.
require(MSstats)
input.proposed <- dataProcess(SRM_yeast, 
                            summaryMethod="TMP",
                            cutoffCensored="minFeature", 
                            censoredInt="0", 
                            MBimpute=TRUE,
                            maxQuantileforCensored=0.999)
##                        
##   Summary of Features :
##                          count
## # of Protein                45
## # of Peptides/Protein      1-2
## # of Transitions/Peptide   1-3
##                       
##   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
## 
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## set up the comparison that you want.
comparison1<-matrix(c(-1,1,0,0,0,0,0,0,0,0),nrow=1)
comparison2<-matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
comparison3<-matrix(c(-1,0,0,1,0,0,0,0,0,0),nrow=1)
comparison4<-matrix(c(-1,0,0,0,1,0,0,0,0,0),nrow=1)
comparison5<-matrix(c(-1,0,0,0,0,1,0,0,0,0),nrow=1)
comparison6<-matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
comparison7<-matrix(c(-1,0,0,0,0,0,0,1,0,0),nrow=1)
comparison8<-matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
comparison9<-matrix(c(-1,0,0,0,0,0,0,0,0,1),nrow=1)

comparison <- rbind(comparison1,comparison2,comparison3,
                    comparison4,comparison5,comparison6,
                    comparison7,comparison8,comparison9)
row.names(comparison) <- c("T2-T1","T3-T1","T4-T1",
                            "T5-T1","T6-T1","T7-T1",
                            "T8-T1","T9-T1","T10-T1")
## test between comparison you set up.
output.comparison <- groupComparison(contrast.matrix=comparison, 
                            data=input.proposed)
## 
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## output.comparison$ComparisonResult include the result of testing.
head(output.comparison$ComparisonResult)
##    Protein Label      log2FC         SE     Tvalue DF       pvalue   adj.pvalue issue MissingPercentage
## 1     ACEA T2-T1  0.32875677 0.24115611  1.3632529 18 1.896130e-01 2.752447e-01    NA                 0
## 10    ACH1 T2-T1  0.29936641 0.09533910  3.1400173 18 5.660892e-03 1.273701e-02    NA                 0
## 19    ACON T2-T1  0.15362516 0.02701301  5.6870805 18 2.152559e-05 8.805925e-05    NA                 0
## 28    ADH1 T2-T1 -0.08619979 0.02260686 -3.8129930 18 1.273964e-03 4.036070e-03    NA                 0
## 37    ADH2 T2-T1  0.23043035 0.26485775  0.8700155 18 3.957462e-01 4.813130e-01    NA                 0
## 46    ADH4 T2-T1  0.19962675 0.39593315  0.5041931 18 6.202414e-01 6.807527e-01    NA                 0
##    ImputationPercentage
## 1                     0
## 10                    0
## 19                    0
## 28                    0
## 37                    0
## 46                    0

References

[1] Clough, T. et al. (2009) Protein quantification in label-free LC-MS experiments. \emph{J. Proteome Res.}, 8, 5275–5284.

[2] Surinova, S. et al. (2015) Prediction of colorectal cancer diagnosis based on circulating plasma proteins. \emph{EMBO Mol. Med.}, 7, 1166–1178.

[3] Cerciello, F. et al. (2013) Identification of a seven glycopeptide signature for malignant pleural mesothelioma in human serum by selected reaction monitoring. \emph{Clin. Proteomics}, 10, 16.

[4] Huttenhain, R. et al. (2012) Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics. \emph{Sci. Tansl. Med.}, 4, 142ra94.

[5] Picotti, P. et ak. (2009) Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. \emph{Cell}, 138, 795–806.