Load MSstatsTMT first. Then you are ready to start MSstatsTMT
# ## Install MSstatsTMT package from Bioconductor
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("MSstatsTMT")
library(MSstatsTMT)This vignette summarizes the introduction and various options of all functionalities in MSstatsTMT.
MSstatsTMT includes the following three steps for statistical testing:
PDtoMSstatsTMTFormat, MaxQtoMSstatsTMTFormat, SpectroMinetoMSstatsTMTFormat, OpenMStoMSstatsTMTFormat and PhilosophertoMSstatsTMTFormat.proteinSummarizationgroupComparisonTMTMSstatsTMT performs statistical analysis steps, that follow peptide identification and quantitation. Therefore, input to MSstatsTMT is the output of other software tools (such as Proteome Discoverer, MaxQuant and so on) that read raw spectral files , identify and quantify peptide ions. The preferred structure of data for use in MSstatsTMT is a .csv file in a long format with at least 9 columns representing the following variables: ProteinName, PeptideSequence, Charge, PSM, Channel, Condition, BioReplicate, Mixture, Intensity. The variable names are fixed, but are case-insensitive.
#> ProteinName PeptideSequence Charge PSM Mixture
#> 1 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 2 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 3 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 4 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 5 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 6 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> TechRepMixture Run Channel
#> 1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 126
#> 2 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 3 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127N
#> 4 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 128C
#> 5 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 128N
#> 6 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 129C
#> BioReplicate Condition Intensity
#> 1 Norm Norm 23398.14
#> 2 0.125 0.125 22387.63
#> 3 0.667 0.667 17754.91
#> 4 1 1 19640.59
#> 5 0.5 0.5 20048.57
#> 6 0.5 0.5 19188.13Preprocess PSM data from Proteome Discoverer and convert into the required input format for MSstatsTMT.
input : data name of Proteome discover PSM output. Read PSM sheet.annotation : data frame which contains column Run, Fraction, TechRepMixture, Channel, Condition, BioReplicate, Mixture.which.proteinid : Use Protein.Accessions(default) column for protein name. Master.Protein.Accessions can be used instead.useNumProteinsColumn : TURE(default) remove shared peptides by information of # Proteins column in PSM sheet.useUniquePeptide : TRUE(default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.rmPSM_withfewMea_withinRun : only for rmPSM_withMissing_withinRun = FALSE. TRUE(default) will remove the features that have 1 or 2 measurements within each Run.removeProtein_with1Peptide : TRUE will remove the proteins which have only 1 peptide and charge. Default is FALSE.summaryforMultipleRows : sum(default) or max - when there are multiple measurements for certain PSM in certain run, select the PSM with the largest summation or maximal value.# read in PD PSM sheet
# raw.pd <- read.delim("161117_SILAC_HeLa_UPS1_TMT10_5Mixtures_3TechRep_UPSdB_Multiconsensus_PD22_Intensity_PSMs.txt")
head(raw.pd)
#> Checked Confidence Identifying.Node PSM.Ambiguity
#> 1: FALSE High Mascot (O4) Unambiguous
#> 2: FALSE High Mascot (K2) Unambiguous
#> 3: FALSE High Mascot (K2) Unambiguous
#> 4: FALSE High Mascot (F2) Selected
#> 5: FALSE High Mascot (K2) Unambiguous
#> 6: FALSE High Mascot (K2) Unambiguous
#> Annotated.Sequence
#> 1: [K].gFQQILAGEYDHLPEQAFYMVGPIEEAVAk.[A]
#> 2: [R].qYPWGVAEVENGEHcDFTILr.[N]
#> 3: [R].dkPSVEPVEEYDYEDLk.[E]
#> 4: [R].hEHQVMLmr.[Q]
#> 5: [R].dNLTLWTADNAGEEGGEAPQEPQS.[-]
#> 6: [R].aLVAIGTHDLDTLSGPFTYTAk.[R]
#> Modifications Marked.as
#> 1: N-Term(TMT6plex); K30(TMT6plex) NA
#> 2: N-Term(TMT6plex); C15(Carbamidomethyl); R21(Label:13C(6)15N(4)) NA
#> 3: N-Term(TMT6plex); K2(Label); K17(Label) NA
#> 4: N-Term(TMT6plex); M8(Oxidation); R9(Label:13C(6)15N(4)) NA
#> 5: N-Term(TMT6plex) NA
#> 6: N-Term(TMT6plex); K22(Label) NA
#> X..Protein.Groups X..Proteins Master.Protein.Accessions
#> 1: 1 1 P06576
#> 2: 1 1 Q16181
#> 3: 1 1 Q9Y450
#> 4: 1 1 Q15233
#> 5: 1 1 P31947
#> 6: 1 1 Q9NSD9
#> Master.Protein.Descriptions
#> 1: ATP synthase subunit beta, mitochondrial OS=Homo sapiens GN=ATP5B PE=1 SV=3
#> 2: Septin-7 OS=Homo sapiens GN=SEPT7 PE=1 SV=2
#> 3: HBS1-like protein OS=Homo sapiens GN=HBS1L PE=1 SV=1
#> 4: Non-POU domain-containing octamer-binding protein OS=Homo sapiens GN=NONO PE=1 SV=4
#> 5: 14-3-3 protein sigma OS=Homo sapiens GN=SFN PE=1 SV=1
#> 6: Phenylalanine--tRNA ligase beta subunit OS=Homo sapiens GN=FARSB PE=1 SV=3
#> Protein.Accessions
#> 1: P06576
#> 2: Q16181
#> 3: Q9Y450
#> 4: Q15233
#> 5: P31947
#> 6: Q9NSD9
#> Protein.Descriptions
#> 1: ATP synthase subunit beta, mitochondrial OS=Homo sapiens GN=ATP5B PE=1 SV=3
#> 2: Septin-7 OS=Homo sapiens GN=SEPT7 PE=1 SV=2
#> 3: HBS1-like protein OS=Homo sapiens GN=HBS1L PE=1 SV=1
#> 4: Non-POU domain-containing octamer-binding protein OS=Homo sapiens GN=NONO PE=1 SV=4
#> 5: 14-3-3 protein sigma OS=Homo sapiens GN=SFN PE=1 SV=1
#> 6: Phenylalanine--tRNA ligase beta subunit OS=Homo sapiens GN=FARSB PE=1 SV=3
#> X..Missed.Cleavages Charge DeltaScore DeltaCn Rank Search.Engine.Rank
#> 1: 0 3 1.0000 0 1 1
#> 2: 0 3 1.0000 0 1 1
#> 3: 1 3 0.9730 0 1 1
#> 4: 0 4 0.5250 0 1 1
#> 5: 0 3 1.0000 0 1 1
#> 6: 0 3 0.9783 0 1 1
#> m.z..Da. MH...Da. Theo..MH...Da. DeltaM..ppm. Deltam.z..Da. Activation.Type
#> 1: 1270.3249 3808.960 3808.966 -1.51 -0.00192 CID
#> 2: 920.4493 2759.333 2759.332 0.31 0.00028 CID
#> 3: 920.1605 2758.467 2758.461 2.08 0.00192 CID
#> 4: 359.6898 1435.737 1435.738 -0.04 -0.00002 CID
#> 5: 920.0943 2758.268 2758.264 1.53 0.00141 CID
#> 6: 919.8502 2757.536 2757.532 1.48 0.00136 CID
#> MS.Order Isolation.Interference.... Average.Reporter.S.N
#> 1: MS2 47.955590 8.7
#> 2: MS2 9.377507 8.1
#> 3: MS2 38.317050 17.8
#> 4: MS2 21.390040 36.5
#> 5: MS2 0.000000 16.7
#> 6: MS2 30.619960 26.7
#> Ion.Inject.Time..ms. RT..min. First.Scan
#> 1: 50.000 212.2487 112815
#> 2: 3.242 164.7507 87392
#> 3: 13.596 143.4534 74786
#> 4: 50.000 21.6426 6458
#> 5: 6.723 174.1863 92950
#> 6: 8.958 176.4863 94294
#> Spectrum.File File.ID Abundance..126
#> 1: 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_03.raw F1 2548.326
#> 2: 161117_SILAC_HeLa_UPS1_TMT10_Mixture3_03.raw F5 22861.765
#> 3: 161117_SILAC_HeLa_UPS1_TMT10_Mixture3_03.raw F5 25504.083
#> 4: 161117_SILAC_HeLa_UPS1_TMT10_Mixture4_02.raw F10 13493.228
#> 5: 161117_SILAC_HeLa_UPS1_TMT10_Mixture3_03.raw F5 64582.786
#> 6: 161117_SILAC_HeLa_UPS1_TMT10_Mixture3_03.raw F5 35404.709
#> Abundance..127N Abundance..127C Abundance..128N Abundance..128C
#> 1: 3231.929 2760.839 4111.639 3127.254
#> 2: 25817.946 23349.498 29449.609 25995.929
#> 3: 27740.450 25144.974 25754.579 29923.176
#> 4: 14674.490 11187.900 12831.495 13839.426
#> 5: 50576.417 47126.037 56285.129 46257.310
#> 6: 31905.852 30993.941 36854.351 37506.001
#> Abundance..129N Abundance..129C Abundance..130N Abundance..130C
#> 1: 1874.163 2831.423 2298.401 3798.876
#> 2: 22955.769 30578.971 30660.488 38728.853
#> 3: 34097.637 31650.255 27632.692 23886.881
#> 4: 12441.353 13450.885 14777.844 13039.995
#> 5: 52634.885 49716.850 60660.574 55830.488
#> 6: 25703.444 38626.598 35447.942 33788.409
#> Abundance..131 Quan.Info Ions.Score Identity.Strict Identity.Relaxed
#> 1: 3739.067 NA 90 28 21
#> 2: 25047.280 NA 76 24 17
#> 3: 35331.092 NA 74 30 23
#> 4: 12057.121 NA 40 25 18
#> 5: 40280.577 NA 38 21 14
#> 6: 32031.516 NA 46 29 22
#> Expectation.Value Percolator.q.Value Percolator.PEP
#> 1: 7.038672e-09 0 1.396e-05
#> 2: 6.298627e-08 0 3.349e-07
#> 3: 4.318385e-07 0 9.922e-07
#> 4: 3.351211e-04 0 1.175e-04
#> 5: 2.152501e-04 0 1.383e-05
#> 6: 2.060469e-04 0 7.198e-05
# Read in annotation including condition and biological replicates per run and channel.
# Users should make this annotation file. It is not the output from Proteome Discoverer.
# annotation.pd <- read.csv(file="PD_Annotation.csv", header=TRUE)
head(annotation.pd)
#> Run Fraction TechRepMixture Channel
#> 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01.raw 1 1 126
#> 2 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01.raw 1 1 127N
#> 3 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01.raw 1 1 127C
#> 4 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01.raw 1 1 128N
#> 5 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01.raw 1 1 128C
#> 6 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01.raw 1 1 129N
#> Condition Mixture BioReplicate
#> 1 Norm Mixture1 Mixture1_Norm
#> 2 0.667 Mixture1 Mixture1_0.667
#> 3 0.125 Mixture1 Mixture1_0.125
#> 4 0.5 Mixture1 Mixture1_0.5
#> 5 1 Mixture1 Mixture1_1
#> 6 0.125 Mixture1 Mixture1_0.125
# use Protein.Accessions as protein name
input.pd <- PDtoMSstatsTMTFormat(raw.pd, annotation.pd,
which.proteinid = "Protein.Accessions")
#> INFO [2023-10-24 18:03:59] ** Raw data from ProteomeDiscoverer imported successfully.
#> INFO [2023-10-24 18:03:59] ** Raw data from ProteomeDiscoverer cleaned successfully.
#> INFO [2023-10-24 18:03:59] ** Using provided annotation.
#> INFO [2023-10-24 18:03:59] ** Run and Channel labels were standardized to remove symbols such as '.' or '%'.
#> INFO [2023-10-24 18:03:59] ** The following options are used:
#> - Features will be defined by the columns: PeptideSequence, PrecursorCharge
#> - Shared peptides will be removed.
#> - Proteins with single feature will not be removed.
#> - Features with less than 3 measurements within each run will be removed.
#> INFO [2023-10-24 18:03:59] ** Features with all missing measurements across channels within each run are removed.
#> INFO [2023-10-24 18:03:59] ** Shared peptides are removed.
#> INFO [2023-10-24 18:03:59] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:00] ** PSMs have been aggregated to peptide ions.
#> INFO [2023-10-24 18:04:00] ** Run annotation merged with quantification data.
#> INFO [2023-10-24 18:04:00] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:00] ** Fractionation handled.
#> INFO [2023-10-24 18:04:00] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO [2023-10-24 18:04:00] ** Finished preprocessing. The dataset is ready to be processed by the proteinSummarization function.
head(input.pd)
#> ProteinName PeptideSequence Charge PSM Mixture
#> 1 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 2 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 3 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 4 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 5 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 6 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> TechRepMixture Run Channel
#> 1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 126
#> 2 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 3 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127N
#> 4 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 128C
#> 5 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 128N
#> 6 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 129C
#> BioReplicate Condition Intensity
#> 1 Mixture1_Norm Norm 23398.14
#> 2 Mixture1_0.125 0.125 22387.63
#> 3 Mixture1_0.667 0.667 17754.91
#> 4 Mixture1_1 1 19640.59
#> 5 Mixture1_0.5 0.5 20048.57
#> 6 Mixture1_0.5 0.5 19188.13
# use Master.Protein.Accessions as protein name
input.pd.master <- PDtoMSstatsTMTFormat(raw.pd, annotation.pd,
which.proteinid = "Master.Protein.Accessions")
#> INFO [2023-10-24 18:04:00] ** Raw data from ProteomeDiscoverer imported successfully.
#> INFO [2023-10-24 18:04:00] ** Raw data from ProteomeDiscoverer cleaned successfully.
#> INFO [2023-10-24 18:04:00] ** Using provided annotation.
#> INFO [2023-10-24 18:04:00] ** Run and Channel labels were standardized to remove symbols such as '.' or '%'.
#> INFO [2023-10-24 18:04:00] ** The following options are used:
#> - Features will be defined by the columns: PeptideSequence, PrecursorCharge
#> - Shared peptides will be removed.
#> - Proteins with single feature will not be removed.
#> - Features with less than 3 measurements within each run will be removed.
#> INFO [2023-10-24 18:04:00] ** Features with all missing measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:00] ** Shared peptides are removed.
#> INFO [2023-10-24 18:04:00] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:01] ** PSMs have been aggregated to peptide ions.
#> INFO [2023-10-24 18:04:02] ** Run annotation merged with quantification data.
#> INFO [2023-10-24 18:04:02] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** Fractionation handled.
#> INFO [2023-10-24 18:04:02] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO [2023-10-24 18:04:02] ** Finished preprocessing. The dataset is ready to be processed by the proteinSummarization function.
head(input.pd.master)
#> ProteinName PeptideSequence Charge PSM Mixture
#> 1 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 2 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 3 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 4 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 5 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> 6 Q9NSD9 [K].aAGASDVVLYk.[I] 2 [K].aAGASDVVLYk.[I]_2 Mixture1
#> TechRepMixture Run Channel
#> 1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 126
#> 2 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 3 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127N
#> 4 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 128C
#> 5 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 128N
#> 6 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 129C
#> BioReplicate Condition Intensity
#> 1 Mixture1_Norm Norm 23398.14
#> 2 Mixture1_0.125 0.125 22387.63
#> 3 Mixture1_0.667 0.667 17754.91
#> 4 Mixture1_1 1 19640.59
#> 5 Mixture1_0.5 0.5 20048.57
#> 6 Mixture1_0.5 0.5 19188.13Here is the summary of pre-processing steps in PDtoMSstatsTMTFormat function.
Preprocess PSM-level data from MaxQuant and convert into the required input format for MSstatsTMT.
evidence : name of evidence.txt data, which includes PSM-level data.proteinGroups : name of proteinGroups.txt data, which contains the detailed information of protein identifications.annotation : data frame which contains column Run, Fraction, TechRepMixture, Channel, Condition, BioReplicate, Mixture.which.proteinid : Use Proteins(default) column for protein name. Leading.proteins or Leading.razor.proteins can be used instead. However, those can potentially have the shared peptides.rmProt_Only.identified.by.site : TRUE will remove proteins with ‘+’ in ‘Only.identified.by.site’ column from proteinGroups.txt, which was identified only by a modification site. FALSE is the default.useUniquePeptide : TRUE(default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.rmPSM_withfewMea_withinRun : only for rmPSM_withMissing_withinRun = FALSE. TRUE(default) will remove the features that have 1 or 2 measurements within each Run.removeProtein_with1Peptide : TRUE will remove the proteins which have only 1 peptide and charge. Default is FALSE.summaryforMultipleRows : sum(default) or max - when there are multiple measurements for certain PSM in certain run, select the PSM with the largest summation or maximal value.# Read in MaxQuant files
# proteinGroups <- read.table("proteinGroups.txt", sep="\t", header=TRUE)
# evidence <- read.table("evidence.txt", sep="\t", header=TRUE)
# Users should make this annotation file. It is not the output from MaxQuant.
# annotation.mq <- read.csv(file="MQ_Annotation.csv", header=TRUE)
input.mq <- MaxQtoMSstatsTMTFormat(evidence, proteinGroups, annotation.mq)
#> INFO [2023-10-24 18:04:02] ** Raw data from MaxQuant imported successfully.
#> INFO [2023-10-24 18:04:02] ** Rows with values of Potentialcontaminant equal to + are removed
#> INFO [2023-10-24 18:04:02] ** Rows with values of Reverse equal to + are removed
#> INFO [2023-10-24 18:04:02] ** Rows with values of Potentialcontaminant equal to + are removed
#> INFO [2023-10-24 18:04:02] ** Rows with values of Reverse equal to + are removed
#> INFO [2023-10-24 18:04:02] ** + Contaminant, + Reverse, + Potential.contaminant proteins are removed.
#> INFO [2023-10-24 18:04:02] ** Features with all missing measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** Raw data from MaxQuant cleaned successfully.
#> INFO [2023-10-24 18:04:02] ** Using provided annotation.
#> INFO [2023-10-24 18:04:02] ** Run and Channel labels were standardized to remove symbols such as '.' or '%'.
#> INFO [2023-10-24 18:04:02] ** The following options are used:
#> - Features will be defined by the columns: PeptideSequence, PrecursorCharge
#> - Shared peptides will be removed.
#> - Proteins with single feature will not be removed.
#> - Features with less than 3 measurements within each run will be removed.
#> INFO [2023-10-24 18:04:02] ** Features with all missing measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** Shared peptides are removed.
#> INFO [2023-10-24 18:04:02] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** PSMs have been aggregated to peptide ions.
#> INFO [2023-10-24 18:04:02] ** Run annotation merged with quantification data.
#> INFO [2023-10-24 18:04:02] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** Fractionation handled.
#> INFO [2023-10-24 18:04:02] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO [2023-10-24 18:04:02] ** Finished preprocessing. The dataset is ready to be processed by the proteinSummarization function.
head(input.mq)
#> ProteinName PeptideSequence Charge
#> 1 P37108 AAAAAAAAAPAAAATAPTTAATTAATAAQ 3
#> 2 P37108 AAAAAAAAAPAAAATAPTTAATTAATAAQ 3
#> 3 P37108 AAAAAAAAAPAAAATAPTTAATTAATAAQ 3
#> 4 P37108 AAAAAAAAAPAAAATAPTTAATTAATAAQ 3
#> 5 P37108 AAAAAAAAAPAAAATAPTTAATTAATAAQ 3
#> 6 P37108 AAAAAAAAAPAAAATAPTTAATTAATAAQ 3
#> PSM Mixture TechRepMixture
#> 1 AAAAAAAAAPAAAATAPTTAATTAATAAQ_3 Mixture1 1
#> 2 AAAAAAAAAPAAAATAPTTAATTAATAAQ_3 Mixture1 1
#> 3 AAAAAAAAAPAAAATAPTTAATTAATAAQ_3 Mixture1 1
#> 4 AAAAAAAAAPAAAATAPTTAATTAATAAQ_3 Mixture1 1
#> 5 AAAAAAAAAPAAAATAPTTAATTAATAAQ_3 Mixture1 1
#> 6 AAAAAAAAAPAAAATAPTTAATTAATAAQ_3 Mixture1 1
#> Run Channel BioReplicate Condition
#> 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01 channel0 Mixture1_Norm Norm
#> 2 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01 channel1 Mixture1_0.667 0.667
#> 3 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01 channel2 Mixture1_0.125 0.125
#> 4 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01 channel3 Mixture1_0.5 0.5
#> 5 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01 channel4 Mixture1_1 1
#> 6 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01 channel5 Mixture1_0.125 0.125
#> Intensity
#> 1 883.78
#> 2 715.37
#> 3 1090.60
#> 4 1080.10
#> 5 1006.40
#> 6 1137.90Preprocess PSM data from SpectroMine and convert into the required input format for MSstatsTMT.
input : data name of SpectroMine PSM output. Read PSM sheet.annotation : data frame which contains column Run, Fraction, TechRepMixture, Channel, Condition, BioReplicate, Mixture.filter_with_Qvalue : TRUE(default) will filter out the intensities that have greater than qvalue_cutoff in EG.Qvalue column. Those intensities will be replaced with NA and will be considered as censored missing values for imputation purpose.qvalue_cutoff : Cutoff for EG.Qvalue. default is 0.01.useUniquePeptide : TRUE(default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.rmPSM_withfewMea_withinRun : only for rmPSM_withMissing_withinRun = FALSE. TRUE(default) will remove the features that have 1 or 2 measurements within each Run.removeProtein_with1Peptide : TRUE will remove the proteins which have only 1 peptide and charge. Default is FALSE.summaryforMultipleRows : sum(default) or max - when there are multiple measurements for certain PSM in certain run, select the PSM with the largest summation or maximal value.# Read in SpectroMine PSM report
# raw.mine <- read.csv('20180831_095547_CID-OT-MS3-Short_PSM Report_20180831_103118.xls', sep="\t")
# Users should make this annotation file. It is not the output from SpectroMine
# annotation.mine <- read.csv(file="Mine_Annotation.csv", header=TRUE)
input.mine <- SpectroMinetoMSstatsTMTFormat(raw.mine, annotation.mine)
#> INFO [2023-10-24 18:04:02] ** Raw data from SpectroMine imported successfully.
#> INFO [2023-10-24 18:04:02] ** Raw data from SpectroMine cleaned successfully.
#> INFO [2023-10-24 18:04:02] ** Using provided annotation.
#> INFO [2023-10-24 18:04:02] ** Run and Channel labels were standardized to remove symbols such as '.' or '%'.
#> INFO [2023-10-24 18:04:02] ** The following options are used:
#> - Features will be defined by the columns: PeptideSequence, PrecursorCharge
#> - Shared peptides will be removed.
#> - Proteins with single feature will not be removed.
#> - Features with less than 3 measurements within each run will be removed.
#> INFO [2023-10-24 18:04:02] ** Intensities with values not smaller than 0.01 in PGQValue are replaced with NA
#> INFO [2023-10-24 18:04:02] ** Intensities with values not smaller than 0.01 in Qvalue are replaced with NA
#> INFO [2023-10-24 18:04:02] ** Features with all missing measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** Shared peptides are removed.
#> INFO [2023-10-24 18:04:02] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** PSMs have been aggregated to peptide ions.
#> INFO [2023-10-24 18:04:02] ** Run annotation merged with quantification data.
#> INFO [2023-10-24 18:04:02] ** For peptides overlapped between fractions of 1_1 use the fraction with maximal average abundance.
#> INFO [2023-10-24 18:04:02] ** Fractions belonging to same mixture have been combined.
#> INFO [2023-10-24 18:04:02] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** Fractionation handled.
#> INFO [2023-10-24 18:04:02] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO [2023-10-24 18:04:02] ** Finished preprocessing. The dataset is ready to be processed by the proteinSummarization function.
head(input.mine)
#> ProteinName PeptideSequence Charge
#> 1 P07954 [TMTNter]AAAEVNQDYGLDPK[TMTLys] 2
#> 2 P07954 [TMTNter]AAAEVNQDYGLDPK[TMTLys] 2
#> 3 P07954 [TMTNter]AAAEVNQDYGLDPK[TMTLys] 2
#> 4 P07954 [TMTNter]AAAEVNQDYGLDPK[TMTLys] 2
#> 5 P07954 [TMTNter]AAAEVNQDYGLDPK[TMTLys] 2
#> 6 P07954 [TMTNter]AAAEVNQDYGLDPK[TMTLys] 2
#> PSM Mixture TechRepMixture Run Channel
#> 1 [TMTNter]AAAEVNQDYGLDPK[TMTLys]_2 1 1 1_1 TMT6_126
#> 2 [TMTNter]AAAEVNQDYGLDPK[TMTLys]_2 1 1 1_1 TMT6_127
#> 3 [TMTNter]AAAEVNQDYGLDPK[TMTLys]_2 1 1 1_1 TMT6_128
#> 4 [TMTNter]AAAEVNQDYGLDPK[TMTLys]_2 1 1 1_1 TMT6_129
#> 5 [TMTNter]AAAEVNQDYGLDPK[TMTLys]_2 1 1 1_1 TMT6_130
#> 6 [TMTNter]AAAEVNQDYGLDPK[TMTLys]_2 1 1 1_1 TMT6_131
#> BioReplicate Condition Intensity
#> 1 1 3 6393.694
#> 2 2 3 7887.951
#> 3 3 3 9917.544
#> 4 1 1 11282.770
#> 5 2 1 8544.471
#> 6 3 1 4893.753Preprocess MSstatsTMT report from OpenMS and convert into the required input format for MSstatsTMT.
input : data name of MSstatsTMT report from OpenMS. Read csv file.useUniquePeptide : TRUE(default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.rmPSM_withfewMea_withinRun : only for rmPSM_withMissing_withinRun = FALSE. TRUE(default) will remove the features that have 1 or 2 measurements within each Run.removeProtein_with1Peptide : TRUE will remove the proteins which have only 1 peptide and charge. Default is FALSE.summaryforMultipleRows : sum(default) or max - when there are multiple measurements for certain PSM in certain run, select the PSM with the largest summation or maximal value.# read in MSstatsTMT report from OpenMS
# raw.om <- read.csv("OpenMS_20200222/20200225_MSstatsTMT_OpenMS_Export.csv")
head(raw.om)
#> RetentionTime ProteinName PeptideSequence Charge
#> 1 2924.491 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 2 2924.491 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 3 2924.491 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 4 2924.491 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 5 2924.491 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 6 2924.491 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> Channel Condition BioReplicate Run Mixture TechRepMixture Fraction
#> 1 1 Long_LF 1 1_1_3 1 1_1 3
#> 2 2 Long_LF 2 1_1_3 1 1_1 3
#> 3 3 Long_M 3 1_1_3 1 1_1 3
#> 4 6 Long_M 6 1_1_3 1 1_1 3
#> 5 5 Norm 5 1_1_3 1 1_1 3
#> 6 9 Norm 9 1_1_3 1 1_1 3
#> Intensity
#> 1 5727.319
#> 2 6985.365
#> 3 4553.897
#> 4 5937.782
#> 5 5151.292
#> 6 6800.128
#> Reference
#> 1 PAMI-176_Mouse_A-J_TMT_40ug_22pctACN_25cm_120min_20160223_OT.mzML_controllerType=0 controllerNumber=1 scan=11324
#> 2 PAMI-176_Mouse_A-J_TMT_40ug_22pctACN_25cm_120min_20160223_OT.mzML_controllerType=0 controllerNumber=1 scan=11324
#> 3 PAMI-176_Mouse_A-J_TMT_40ug_22pctACN_25cm_120min_20160223_OT.mzML_controllerType=0 controllerNumber=1 scan=11324
#> 4 PAMI-176_Mouse_A-J_TMT_40ug_22pctACN_25cm_120min_20160223_OT.mzML_controllerType=0 controllerNumber=1 scan=11324
#> 5 PAMI-176_Mouse_A-J_TMT_40ug_22pctACN_25cm_120min_20160223_OT.mzML_controllerType=0 controllerNumber=1 scan=11324
#> 6 PAMI-176_Mouse_A-J_TMT_40ug_22pctACN_25cm_120min_20160223_OT.mzML_controllerType=0 controllerNumber=1 scan=11324
# the function only requries one input file
input.om <- OpenMStoMSstatsTMTFormat(raw.om)
#> INFO [2023-10-24 18:04:02] ** Raw data from OpenMS imported successfully.
#> INFO [2023-10-24 18:04:02] ** Raw data from OpenMS cleaned successfully.
#> INFO [2023-10-24 18:04:02] ** The following options are used:
#> - Features will be defined by the columns: PeptideSequence, PrecursorCharge
#> - Shared peptides will be removed.
#> - Proteins with single feature will not be removed.
#> - Features with less than 3 measurements within each run will be removed.
#> INFO [2023-10-24 18:04:02] ** Features with all missing measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** Shared peptides are removed.
#> INFO [2023-10-24 18:04:02] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** PSMs have been aggregated to peptide ions.
#> INFO [2023-10-24 18:04:02] ** For peptides overlapped between fractions of 2_2_2 use the fraction with maximal average abundance.
#> INFO [2023-10-24 18:04:02] ** For peptides overlapped between fractions of 3_3_3 use the fraction with maximal average abundance.
#> INFO [2023-10-24 18:04:02] ** Fractions belonging to same mixture have been combined.
#> INFO [2023-10-24 18:04:02] ** Features with one or two measurements across channels within each run are removed.
#> INFO [2023-10-24 18:04:02] ** Fractionation handled.
#> INFO [2023-10-24 18:04:02] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO [2023-10-24 18:04:02] ** Finished preprocessing. The dataset is ready to be processed by the proteinSummarization function.
head(input.om)
#> ProteinName PeptideSequence Charge
#> 1 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 2 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 3 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 4 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 5 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> 6 sp|P11679|K2C8_MOUSE .(TMT6plex)AEAETMYQIK(TMT6plex) 2
#> PSM Mixture TechRepMixture Run Channel
#> 1 .(TMT6plex)AEAETMYQIK(TMT6plex)_2 1 1_1 1_1_1 1
#> 2 .(TMT6plex)AEAETMYQIK(TMT6plex)_2 1 1_1 1_1_1 2
#> 3 .(TMT6plex)AEAETMYQIK(TMT6plex)_2 1 1_1 1_1_1 3
#> 4 .(TMT6plex)AEAETMYQIK(TMT6plex)_2 1 1_1 1_1_1 4
#> 5 .(TMT6plex)AEAETMYQIK(TMT6plex)_2 1 1_1 1_1_1 5
#> 6 .(TMT6plex)AEAETMYQIK(TMT6plex)_2 1 1_1 1_1_1 6
#> BioReplicate Condition Intensity
#> 1 1 Long_LF 5727.319
#> 2 2 Long_LF 6985.365
#> 3 3 Long_M 4553.897
#> 4 4 Short_LF 6277.917
#> 5 5 Norm 5151.292
#> 6 6 Long_M 5937.782Preprocess MSstats report from Philosopher of Fragpipe and convert into the required input format for MSstatsTMT.
input : list of tables exported by Philosopher. Fragpipe produces a csv file for each TMT mixture.path : a path to the folder with all the Philosopher msstats csv files. Fragpipe produces a msstats.csv file for each TMT mixture.folder : logical, if TRUE, path parameter will be treated as folder path and all msstats*.csv files will be imported. If FALSE, path parameter will be treated as a vector of fixed file paths.annotation : annotation with Run, Fraction, TechRepMixture, Mixture, Channel, BioReplicate, Condition columns or a path to file. Refer to the example ‘annotation’ for the meaning of each column. Channel column should be consistent with the channel columns (Ignore the prefix “Channel”) in msstats.csv file. Run column should be consistent with the Spectrum.File columns in msstats.csv file.protein_id_col : Use ‘Protein.Accessions’(default) column for protein name. ‘Master.Protein.Accessions’ can be used instead to get the protein ID with single protein.peptide_id_col : Use ‘Peptide.Sequence’(default) column for peptide sequence. ‘Modified.Peptide.Sequence’ can be used instead to get the modified peptide sequence.Purity_cutoff : Cutoff for purity. Default is 0.6PeptideProphet_prob_cutoff : Cutoff for the peptide identification probability. Default is 0.7.useUniquePeptide : logical, if TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.rmPSM_withfewMea_withinRun : TRUE(default) will remove the features that have 1 or 2 measurements within each Run.rmPeptide_OxidationM : TRUE (default) will remove the peptides including oxidation (M) sequence.removeProtein_with1Peptide : TRUE will remove the proteins which have only 1 peptide and charge. Default is FALSE.summaryforMultipleRows : sum(default) or max - when there are multiple measurements for certain PSM in certain run, select the PSM with the largest summation or maximal value.After reading the input files and get the data with required format, MSstatsTMT performs
Intensity columnGlobal median normalization is first applied to peptide level quantification data (equalizing the medians across all the channels and MS runs). Protein summarization from peptide level quantification should be performed before testing differentially abundant proteins. Then, normalization between MS runs using reference channels will be implemented. In particular, protein summarization method MSstats assumes missing values are censored and then imputes the missing values before summarizing peptide level data into protein level data. Other methods, including MedianPolish, Median and LogSum, do not impute missing values.
data : Name of the output of PDtoMSstatsTMTFormat function or peptide-level quantified data from other tools. It should have columns named Protein, PSM, TechRepMixture, Mixture, Run, Channel, Condition, BioReplicate, Intensity.method : Four different summarization methods to protein-level can be performed : msstats(default), MedianPolish, Median, LogSum.global_norm : Global median normalization on peptide level data (equalizing the medians across all the channels and MS runs). Default is TRUE. It will be performed before protein-level summarization.reference_norm : Reference channel based normalization between MS runs. TRUE(default) needs at least one reference channel in each MS run, annotated by Norm in Condtion column. It will be performed after protein-level summarization. FALSE will not perform this normalization step. If data only has one run, then reference_norm=FALSE.remove_norm_channel : TRUE(default) removes Norm channels from protein level data.remove_empty_channel : TRUE(default) removes Empty channels from protein level data.MBimpute : only for method = "msstats". TRUE (default) imputes missing values by Accelated failure model. FALSE uses minimum value to impute the missing value for each peptide precursor ion.maxQuantileforCensored : We assume missing values are censored. maxQuantileforCensored is Maximum quantile for deciding censored missing value, for instance, 0.999. Default is Null.# use MSstats for protein summarization
quant.msstats <- proteinSummarization(input.pd,
method="msstats",
global_norm=TRUE,
reference_norm=TRUE,
remove_norm_channel = TRUE,
remove_empty_channel = TRUE)head(quant.pd.msstats$ProteinLevelData)
#> Mixture TechRepMixture Run Channel
#> 1 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 2 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 3 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 4 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 5 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 6 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> Protein Abundance BioReplicate Condition
#> 1 P04406 16.59812 0.125 0.125
#> 2 P06576 15.55891 0.125 0.125
#> 3 P12277 15.28471 0.125 0.125
#> 4 P23919 15.20871 0.125 0.125
#> 5 P31947 14.86975 0.125 0.125
#> 6 Q15233 14.57543 0.125 0.125# use Median for protein summarization
quant.median <- proteinSummarization(input.pd,
method="Median",
global_norm=TRUE,
reference_norm=TRUE,
remove_norm_channel = TRUE,
remove_empty_channel = TRUE)head(quant.median$ProteinLevelData)
#> Mixture TechRepMixture Run Channel
#> 1 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 2 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 3 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 4 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 5 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> 6 Mixture1 1 161117_SILAC_HeLa_UPS1_TMT10_Mixture1_01raw 127C
#> Protein Abundance BioReplicate Condition
#> 1 Q9NSD9 15.33397 Mixture1_0.125 0.125
#> 2 P04406 16.39393 Mixture1_0.125 0.125
#> 3 Q15233 14.69981 Mixture1_0.125 0.125
#> 4 Q16181 13.83691 Mixture1_0.125 0.125
#> 5 P12277 15.06154 Mixture1_0.125 0.125
#> 6 P23919 14.89041 Mixture1_0.125 0.125Visualization for explanatory data analysis. To illustrate the quantitative data after data-preprocessing and quality control of TMT runs, dataProcessPlotsTMT takes the quantitative data and summarized data from function proteinSummarization as input. It generates two types of figures in pdf files as output :
profile plot (specify “ProfilePlot” in option type), to identify the potential sources of variation for each protein;
quality control plot (specify “QCPlot” in option type), to evaluate the systematic bias between MS runs and channels.
data : the output of proteinSummarization function. It is a list with data frames FeatureLevelData and ProteinLevelDatatype : choice of visualization. “ProfilePlot” represents profile plot of log intensities across MS runs. “QCPlot” represents quality control plot of log intensities across MS runs.ylimUp : upper limit for y-axis in the log scale. FALSE(Default) for Profile Plot and QC Plot use the upper limit as rounded off maximum of log2(intensities) after normalization + 3.ylimDown : lower limit for y-axis in the log scale. FALSE(Default) for Profile Plot and QC Plot is 0.x.axis.size : size of x-axis labeling for “Run” and “channel” in Profile Plot and QC Plot.y.axis.size : size of y-axis labels. Default is 10.text.size : size of labels represented each condition at the top of graph in Profile Plot and QC plot. Default is 4.text.angle : angle of labels represented each condition at the top of graph in Profile Plot and QC plot. Default is 0.legend.size : size of legend above graph in Profile Plot. Default is 7.dot.size.profile : size of dots in profile plot. Default is 2.ncol.guide : number of columns for legends at the top of plot. Default is 5.width : width of the saved file. Default is 10.height : height of the saved file. Default is 10.which.Protein : Protein list to draw plots. List can be names of Proteins or order numbers of Proteins. Default is “all”, which generates all plots for each protein. For QC plot, “allonly” will generate one QC plot with all proteins.originalPlot : TRUE(default) draws original profile plots, without normalization.summaryPlot : TRUE(default) draws profile plots with protein summarization for each channel and MS run.address : the name of folder that will store the results. Default folder is the current working directory. The other assigned folder has to be existed under the current working directory. An output pdf file is automatically created with the default name of “ProfilePlot.pdf” or “QCplot.pdf”. The command address can help to specify where to store the file as well as how to modify the beginning of the file name. If address=FALSE, plot will be not saved as pdf file but showed in window.## Profile plot without norm channnels and empty channels
dataProcessPlotsTMT(data=quant.msstats,
type = 'ProfilePlot',
width = 21, # adjust the figure width since there are 15 TMT runs.
height = 7)There are two pdfs with all the proteins, first is profile plot and second plot is profile plot with summarized and normalized data. XXX_ProfilePlot.pdf shows each peptide ions across runs and channels, grouped per condition. Each panel represents one MS run and each dot within one panel is one channel within one Run. Each peptide has a different colour/type layout. The dots are linked with line per peptide ion If line is disconnected, that means there is no value (missing value). Profile plot is good visualization to check individual measurements. XXX_ProfilePlot_wSummarization.pdf shows the same peptide ions in grey, with the values as summarized by the model overlayed in red.
Instead of making all profile plots for all proteins, we can make plot for individual protein. Here is the example of proteinP04406
Tests for significant changes in protein abundance across conditions based on a family of linear mixed-effects models in TMT experiment. Experimental design of case-control study (patients are not repeatedly measured) is automatically determined based on proper statistical model.
data : the output of proteinSummarization function. It is a list with data frames FeatureLevelData and ProteinLevelDatacontrast.matrix : Comparison between conditions of interests. 1) default is pairwise, which compare all possible pairs between two conditions. 2) Otherwise, users can specify the comparisons of interest. Based on the levels of conditions, specify 1 or -1 to the conditions of interests and 0 otherwise. The levels of conditions are sorted alphabetically.moderated : If moderated = TRUE, then moderated t statistic will be calculated; otherwise, ordinary t statistic will be used.adj.method : adjusted method for multiple comparison. ’BH` is default.save_fitted_models: logical, if TRUE, fitted models will be added toremove_norm_channel : TRUE(default) removes Norm channels from protein level data.remove_empty_channel : TRUE(default) removes Empty channels from protein level data.If you want to make all the pairwise comparison,MSstatsTMT has an easy option for it. Setting contrast.matrix = pairwise compares all the possible pairs between two conditions.
# test for all the possible pairs of conditions
test.pairwise <- groupComparisonTMT(quant.msstats, moderated = TRUE)# Show test result
# Label : which comparison is used
# log2FC : estimated log2 fold change between two conditions (the contrast)
# adj.pvalue : adjusted p value
head(test.pairwise$ComparisonResult)
#> Protein Label log2FC SE DF pvalue adj.pvalue
#> 1: P04406 0.125 vs 0.667 -0.010442843 0.02604088 43.37604 0.6903760 0.9855065
#> 2: P04406 0.125 vs 1 -0.005921016 0.02604088 43.37604 0.8212013 0.9656146
#> 3: P04406 0.125 vs 0.5 -0.031373953 0.02604088 43.37604 0.2348144 0.6884275
#> 4: P04406 0.667 vs 1 0.004521827 0.02604088 43.37604 0.8629535 0.9540600
#> 5: P04406 0.667 vs 0.5 -0.020931110 0.02604088 43.37604 0.4259078 0.7655582
#> 6: P04406 1 vs 0.5 -0.025452937 0.02604088 43.37604 0.3337801 0.8551737
#> issue
#> 1: NA
#> 2: NA
#> 3: NA
#> 4: NA
#> 5: NA
#> 6: NAIf you would like to compare some specific combination of conditions, you need to tell groupComparisonTMT the contrast of the conditions to compare. You can make your contrast.matrix in R in a text editor. We define our contrast matrix by adding a column for every condition. We add a row for every comparison we would like to make between groups of conditions.
0 is for conditions we would like to ignore. 1 is for conditions we would like to put in the numerator of the ratio or fold-change. -1 is for conditions we would like to put in the denumerator of the ratio or fold-change.
If you have multiple groups, you can assign any group comparisons you are interested in.
# Check the conditions in the protein level data
levels(quant.msstats$ProteinLevelData$Condition)
#> [1] "0.125" "0.5" "0.667" "1"
# Only compare condition 0.125 and 1
comparison<-matrix(c(-1,0,0,1),nrow=1)
# Set the names of each row
row.names(comparison)<-"1-0.125"
# Set the column names
colnames(comparison)<- c("0.125", "0.5", "0.667", "1")
comparison
#> 0.125 0.5 0.667 1
#> 1-0.125 -1 0 0 1test.contrast <- groupComparisonTMT(data = quant.msstats, contrast.matrix = comparison, moderated = TRUE)head(test.contrast$ComparisonResult)
#> Protein Label log2FC SE DF pvalue adj.pvalue issue
#> 1: P04406 1-0.125 0.005921016 0.02604088 43.37604 0.8212013 0.9656146 NA
#> 2: P06576 1-0.125 -0.001284321 0.02962229 43.74848 0.9656146 0.9656146 NA
#> 3: P12277 1-0.125 -0.013004897 0.03874807 40.31591 0.7388926 0.9656146 NA
#> 4: P23919 1-0.125 0.031852508 0.03691476 43.22630 0.3929732 0.7859463 NA
#> 5: P31947 1-0.125 0.034549433 0.03955270 42.00057 0.3873557 0.7859463 NA
#> 6: Q15233 1-0.125 0.010110290 0.03056908 43.19538 0.7424430 0.9656146 NA