Workflow
1. Preprocessing
1.1 Raw Data Format
The first step is to load in the raw dataset for both the PTM and Protein datasets. This data can then be converted into MSstatsLiP format with one of the built in converters, or manually converted. In this case we use the converter for Spectronaut.
## Read in raw data files
head(LiPRawData)
#> R.Condition R.FileName R.Replicate PG.ProteinAccessions PG.ProteinGroups
#> 1: Osmo 180423_IP1742 Osmo_1 P14164 P14164
#> 2: Osmo 180423_IP1742 Osmo_1 P14164 P14164
#> 3: Osmo 180423_IP1742 Osmo_1 P14164 P14164
#> 4: Osmo 180423_IP1742 Osmo_1 P14164 P14164
#> 5: Osmo 180423_IP1742 Osmo_1 P14164 P14164
#> 6: Osmo 180423_IP1742 Osmo_1 P14164 P14164
#> PG.Quantity PEP.GroupingKey PEP.StrippedSequence PEP.Quantity
#> 1: 105910.6 ILQNDLK ILQNDLK 65116.91
#> 2: 105910.6 ILQNDLK ILQNDLK 65116.91
#> 3: 105910.6 ILQNDLK ILQNDLK 65116.91
#> 4: 105910.6 ILQNDLK ILQNDLK 65116.91
#> 5: 105910.6 ILQNDLK ILQNDLK 65116.91
#> 6: 105910.6 ILQNDLK ILQNDLK 65116.91
#> EG.iRTPredicted EG.Library EG.ModifiedSequence EG.PrecursorId
#> 1: -9.285238 180517_Sc_Sent_O_all _ILQNDLK_ _ILQNDLK_.2
#> 2: -9.285238 180517_Sc_Sent_O_all _ILQNDLK_ _ILQNDLK_.2
#> 3: -9.285238 180517_Sc_Sent_O_all _ILQNDLK_ _ILQNDLK_.2
#> 4: -9.285238 180517_Sc_Sent_O_all _ILQNDLK_ _ILQNDLK_.2
#> 5: -9.285238 180517_Sc_Sent_O_all _ILQNDLK_ _ILQNDLK_.2
#> 6: -9.285238 180517_Sc_Sent_O_all _ILQNDLK_ _ILQNDLK_.2
#> EG.Qvalue FG.Charge FG.Id FG.PrecMz FG.Quantity F.Charge F.FrgIon
#> 1: 0.0001794077 2 _ILQNDLK_.2 422.2504 65116.91 1 y5
#> 2: 0.0001794077 2 _ILQNDLK_.2 422.2504 65116.91 1 y6
#> 3: 0.0001794077 2 _ILQNDLK_.2 422.2504 65116.91 1 y5
#> 4: 0.0001794077 2 _ILQNDLK_.2 422.2504 65116.91 1 y4
#> 5: 0.0001794077 2 _ILQNDLK_.2 422.2504 65116.91 1 y5
#> 6: 0.0001794077 2 _ILQNDLK_.2 422.2504 65116.91 1 y3
#> F.FrgLossType F.FrgMz F.FrgNum F.FrgType F.ExcludedFromQuantification
#> 1: noloss 617.3253 5 y TRUE
#> 2: noloss 730.4094 6 y FALSE
#> 3: NH3 600.2988 5 y FALSE
#> 4: noloss 489.2667 4 y FALSE
#> 5: H2O 599.3148 5 y TRUE
#> 6: noloss 375.2238 3 y TRUE
#> F.NormalizedPeakArea F.NormalizedPeakHeight F.PeakArea F.PeakHeight
#> 1: 40994.32 275561.99 39189.54 263430.31
#> 2: 32552.17 267988.39 31119.05 256190.14
#> 3: 17712.62 124252.06 16932.82 118781.84
#> 4: 14852.12 101414.80 14198.25 96949.99
#> 5: 30117.24 257451.20 28791.32 246116.86
#> 6: 28752.17 69229.75 27486.35 66181.90
head(TrPRawData)
#> R.Condition R.FileName R.Replicate PG.ProteinAccessions PG.ProteinGroups
#> 1: OsmoT 180423_IP1739 OsmoT_1 P14164 P14164
#> 2: OsmoT 180423_IP1739 OsmoT_1 P14164 P14164
#> 3: OsmoT 180423_IP1739 OsmoT_1 P14164 P14164
#> 4: OsmoT 180423_IP1739 OsmoT_1 P14164 P14164
#> 5: OsmoT 180423_IP1739 OsmoT_1 P14164 P14164
#> 6: OsmoT 180423_IP1739 OsmoT_1 P14164 P14164
#> PG.Quantity PEP.GroupingKey PEP.StrippedSequence PEP.Quantity
#> 1: 159438.5 ILQNDLK ILQNDLK 125562.7
#> 2: 159438.5 ILQNDLK ILQNDLK 125562.7
#> 3: 159438.5 ILQNDLK ILQNDLK 125562.7
#> 4: 159438.5 ILQNDLK ILQNDLK 125562.7
#> 5: 159438.5 ILQNDLK ILQNDLK 125562.7
#> 6: 159438.5 HQEISSAGTSSNTTK HQEISSAGTSSNTTK 56869.4
#> EG.iRTPredicted EG.Library EG.ModifiedSequence
#> 1: -8.957758 180516_Sc_Sent_O_mixed _ILQNDLK_
#> 2: -8.957758 180516_Sc_Sent_O_mixed _ILQNDLK_
#> 3: -8.957758 180516_Sc_Sent_O_mixed _ILQNDLK_
#> 4: -8.957758 180516_Sc_Sent_O_mixed _ILQNDLK_
#> 5: -8.957758 180516_Sc_Sent_O_mixed _ILQNDLK_
#> 6: -46.080009 180516_Sc_Sent_O_mixed _HQEISSAGTSSNTTK_
#> EG.PrecursorId EG.Qvalue FG.Charge FG.Id
#> 1: _ILQNDLK_.2 1.87107676352627E-08 2 _ILQNDLK_.2
#> 2: _ILQNDLK_.2 1.87107676352627E-08 2 _ILQNDLK_.2
#> 3: _ILQNDLK_.2 1.87107676352627E-08 2 _ILQNDLK_.2
#> 4: _ILQNDLK_.2 1.87107676352627E-08 2 _ILQNDLK_.2
#> 5: _ILQNDLK_.2 1.87107676352627E-08 2 _ILQNDLK_.2
#> 6: _HQEISSAGTSSNTTK_.3 5.94616028780965E-24 3 _HQEISSAGTSSNTTK_.3
#> FG.PrecMz FG.Quantity F.Charge F.FrgIon F.FrgLossType F.FrgMz F.FrgNum
#> 1: 422.2504 125562.66 1 y5 noloss 617.3253 5
#> 2: 422.2504 125562.66 1 y6 noloss 730.4094 6
#> 3: 422.2504 125562.66 1 y5 NH3 600.2988 5
#> 4: 422.2504 125562.66 1 y4 noloss 489.2667 4
#> 5: 422.2504 125562.66 1 y3 noloss 375.2238 3
#> 6: 516.5814 38460.38 1 y6 noloss 637.3151 6
#> F.FrgType F.ExcludedFromQuantification F.NormalizedPeakArea
#> 1: y FALSE 43032.81
#> 2: y FALSE 45005.01
#> 3: y FALSE 37524.85
#> 4: y TRUE 19428.34
#> 5: y TRUE 50290.83
#> 6: y TRUE 44622.36
#> F.NormalizedPeakHeight F.PeakArea F.PeakHeight
#> 1: 196684.09 39642.51 181188.52
#> 2: 216104.63 41459.34 199079.03
#> 3: 153040.68 34568.49 140983.52
#> 4: 70077.31 17897.70 64556.34
#> 5: 220417.00 46328.71 203051.66
#> 6: 241914.85 41287.37 223834.581.2 Converter
## Run converter
MSstatsLiP_data <- SpectronauttoMSstatsLiPFormat(LiPRawData,
"../inst/extdata/ExampleFastaFile.fasta",
TrPRawData, use_log_file = FALSE,
append = FALSE)
#> INFO [2022-04-28 17:05:14] ** Raw data from Spectronaut imported successfully.
#> INFO [2022-04-28 17:05:14] ** Raw data from Spectronaut cleaned successfully.
#> INFO [2022-04-28 17:05:14] ** Using annotation extracted from quantification data.
#> INFO [2022-04-28 17:05:14] ** Run labels were standardized to remove symbols such as '.' or '%'.
#> INFO [2022-04-28 17:05:14] ** The following options are used:
#> - Features will be defined by the columns: PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge
#> - Shared peptides will be removed.
#> - Proteins with single feature will not be removed.
#> - Features with less than 3 measurements across runs will be removed.
#> WARN [2022-04-28 17:05:14] ** PGQvalue not found in input columns.
#> INFO [2022-04-28 17:05:14] ** Intensities with values not smaller than 0.01 in EGQvalue are replaced with 0
#> INFO [2022-04-28 17:05:14] ** Features with all missing measurements across runs are removed.
#> INFO [2022-04-28 17:05:14] ** Shared peptides are removed.
#> INFO [2022-04-28 17:05:14] ** Multiple measurements in a feature and a run are summarized by summaryforMultipleRows: max
#> INFO [2022-04-28 17:05:14] ** Features with one or two measurements across runs are removed.
#> INFO [2022-04-28 17:05:14] ** Run annotation merged with quantification data.
#> INFO [2022-04-28 17:05:14] ** Features with one or two measurements across runs are removed.
#> INFO [2022-04-28 17:05:14] ** Fractionation handled.
#> INFO [2022-04-28 17:05:14] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO [2022-04-28 17:05:14] ** Finished preprocessing. The dataset is ready to be processed by the dataProcess function.
#> INFO [2022-04-28 17:05:14] ** Raw data from Spectronaut imported successfully.
#> INFO [2022-04-28 17:05:14] ** Raw data from Spectronaut cleaned successfully.
#> INFO [2022-04-28 17:05:14] ** Using annotation extracted from quantification data.
#> INFO [2022-04-28 17:05:14] ** Run labels were standardized to remove symbols such as '.' or '%'.
#> INFO [2022-04-28 17:05:14] ** The following options are used:
#> - Features will be defined by the columns: PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge
#> - Shared peptides will be removed.
#> - Proteins with single feature will not be removed.
#> - Features with less than 3 measurements across runs will be removed.
#> WARN [2022-04-28 17:05:14] ** PGQvalue not found in input columns.
#> INFO [2022-04-28 17:05:14] ** Intensities with values not smaller than 0.01 in EGQvalue are replaced with 0
#> INFO [2022-04-28 17:05:14] ** Features with all missing measurements across runs are removed.
#> INFO [2022-04-28 17:05:14] ** Shared peptides are removed.
#> INFO [2022-04-28 17:05:14] ** Multiple measurements in a feature and a run are summarized by summaryforMultipleRows: max
#> INFO [2022-04-28 17:05:14] ** Features with one or two measurements across runs are removed.
#> INFO [2022-04-28 17:05:14] ** Run annotation merged with quantification data.
#> INFO [2022-04-28 17:05:14] ** Features with one or two measurements across runs are removed.
#> INFO [2022-04-28 17:05:14] ** Fractionation handled.
#> INFO [2022-04-28 17:05:14] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO [2022-04-28 17:05:14] ** Finished preprocessing. The dataset is ready to be processed by the dataProcess function.
head(MSstatsLiP_data[["LiP"]])
#> ProteinName PeptideSequence PrecursorCharge FragmentIon ProductCharge
#> 1: P14164 ILQNDLK 2 y4 1
#> 2: P14164 ILQNDLK 2 y4 1
#> 3: P14164 ILQNDLK 2 y4 1
#> 4: P14164 ILQNDLK 2 y4 1
#> 5: P14164 ILQNDLK 2 y4 1
#> 6: P14164 ILQNDLK 2 y4 1
#> IsotopeLabelType Condition BioReplicate Run Fraction Intensity
#> 1: L Osmo Osmo_1 180423_IP1742 1 1.419825e+04
#> 2: L Osmo Osmo_2 180423_IP1743 1 1.053235e+04
#> 3: L Osmo Osmo_3 180423_IP1744 1 1.232257e+04
#> 4: L Control Control_1 180423_IP1748 1 1.034930e+04
#> 5: L Control Control_2 180423_IP1749 1 2.396684e+04
#> 6: L Control Control_3 180423_IP1750 1 9.265785e+03
#> FULL_PEPTIDE
#> 1: P14164_ILQNDLK
#> 2: P14164_ILQNDLK
#> 3: P14164_ILQNDLK
#> 4: P14164_ILQNDLK
#> 5: P14164_ILQNDLK
#> 6: P14164_ILQNDLK
head(MSstatsLiP_data[["TrP"]])
#> ProteinName PeptideSequence PrecursorCharge FragmentIon ProductCharge
#> 1: P14164 GLDDESGPTHGNDSGNHR 4 y12 2
#> 2: P14164 GLDDESGPTHGNDSGNHR 4 y12 2
#> 3: P14164 GLDDESGPTHGNDSGNHR 4 y12 2
#> 4: P14164 GLDDESGPTHGNDSGNHR 4 y12 2
#> 5: P14164 GLDDESGPTHGNDSGNHR 4 y12 2
#> 6: P14164 GLDDESGPTHGNDSGNHR 4 y12 2
#> IsotopeLabelType Condition BioReplicate Run Fraction
#> 1: L OsmoT OsmoT_1 180423_IP1739 1
#> 2: L OsmoT OsmoT_2 180423_IP1740_2 1
#> 3: L OsmoT OsmoT_3 180423_IP1741_2 1
#> 4: L CtrlT CtrlT_1 180423_IP1745 1
#> 5: L CtrlT CtrlT_2 180423_IP1746 1
#> 6: L CtrlT CtrlT_3 180423_IP1747 1
#> Intensity
#> 1: 7.497252e+02
#> 2: <NA>
#> 3: 3.105397e+03
#> 4: 3.712693e+03
#> 5: 4.353941e+03
#> 6: 4.617424e+03
## Make conditions match
MSstatsLiP_data[["LiP"]][MSstatsLiP_data[["LiP"]]$Condition == "Control",
"Condition"] = "Ctrl"
MSstatsLiP_data[["TrP"]]$Condition = substr(MSstatsLiP_data[["TrP"]]$Condition,
1, nchar(MSstatsLiP_data[["TrP"]]$Condition)-1)In the case above the SpectronauttoMSstatsLiPFormat was ran to convert the data into the format required for MSstatsLiP. Note that the condition names did not match between the LiP and TrP datasets. Here we edit the conditions in each dataset to match.
Additionally, it is important that the column “FULL_PEPTIDE” in the LiP dataset contains both the Protein and Peptide information, seperated by an underscore. This allows us to summarize up to the peptide level, while keeping important information about which protein the peptide corresponds to.
2. Summarization
The next step in the MSstatsLiP workflow is to summarize feature intensities per run into one value using the dataSummarizationLiP function. This function takes as input the formatted data from the converter.
2.1 Summarization Function
MSstatsLiP_Summarized <- dataSummarizationLiP(MSstatsLiP_data,
MBimpute = FALSE,
use_log_file = FALSE,
append = FALSE)
#> Starting PTM summarization...
#> INFO [2022-04-28 17:05:15] ** Features with one or two measurements across runs are removed.
#> INFO [2022-04-28 17:05:15] ** Fractionation handled.
#> INFO [2022-04-28 17:05:15] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO [2022-04-28 17:05:15] ** Use all features that the dataset originally has.
#> INFO [2022-04-28 17:05:15]
#> # proteins: 14
#> # peptides per protein: 1-2
#> # features per peptide: 2-4
#> INFO [2022-04-28 17:05:15]
#> Ctrl Osmo
#> # runs 3 3
#> # bioreplicates 3 3
#> # tech. replicates 1 1
#> INFO [2022-04-28 17:05:15] == Start the summarization per subplot...
#>
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#> INFO [2022-04-28 17:05:15] == Summarization is done.
#> Starting Protein summarization...
#> INFO [2022-04-28 17:05:15] ** Features with one or two measurements across runs are removed.
#> INFO [2022-04-28 17:05:15] ** Fractionation handled.
#> INFO [2022-04-28 17:05:15] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO [2022-04-28 17:05:15] ** Use all features that the dataset originally has.
#> INFO [2022-04-28 17:05:15]
#> # proteins: 13
#> # peptides per protein: 1-4
#> # features per peptide: 2-4
#> INFO [2022-04-28 17:05:15]
#> Ctrl Osmo
#> # runs 3 3
#> # bioreplicates 3 3
#> # tech. replicates 1 1
#> INFO [2022-04-28 17:05:15] Some features are completely missing in at least one condition:
#> SNEVNGNNDDDADANNIFK_2_b3_1,
#> SNEVNGNNDDDADANNIFK_2_y6_1,
#> KDDDTDFLK_3_y3_1,
#> KDDDTDFLK_3_y4_1,
#> SNDLLSGLTGSSQTR_2_b3_1 ...
#> INFO [2022-04-28 17:05:15] == Start the summarization per subplot...
#>
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#> INFO [2022-04-28 17:05:15] == Summarization is done.
names(MSstatsLiP_Summarized[["LiP"]])
#> [1] "FeatureLevelData" "ProteinLevelData" "SummaryMethod"
#> [4] "ModelQC" "PredictBySurvival"
lip_protein_data <- MSstatsLiP_Summarized[["LiP"]]$ProteinLevelData
trp_protein_data <- MSstatsLiP_Summarized[["TrP"]]$ProteinLevelData
head(lip_protein_data)
#> FULL_PEPTIDE RUN LogIntensities originalRUN GROUP SUBJECT
#> 1: P14164_ILQNDLK 1 15.67592 180423_IP1748 Ctrl Control_1
#> 2: P14164_ILQNDLK 2 15.71415 180423_IP1749 Ctrl Control_2
#> 3: P14164_ILQNDLK 3 15.50261 180423_IP1750 Ctrl Control_3
#> 4: P14164_ILQNDLK 4 14.35382 180423_IP1742 Osmo Osmo_1
#> 5: P14164_ILQNDLK 5 14.98971 180423_IP1743 Osmo Osmo_2
#> 6: P14164_ILQNDLK 6 14.36873 180423_IP1744 Osmo Osmo_3
#> TotalGroupMeasurements NumMeasuredFeature MissingPercentage more50missing
#> 1: 6 2 0 FALSE
#> 2: 6 2 0 FALSE
#> 3: 6 2 0 FALSE
#> 4: 6 2 0 FALSE
#> 5: 6 2 0 FALSE
#> 6: 6 2 0 FALSE
#> NumImputedFeature Protein
#> 1: 0 P14164
#> 2: 0 P14164
#> 3: 0 P14164
#> 4: 0 P14164
#> 5: 0 P14164
#> 6: 0 P14164
head(trp_protein_data)
#> RUN Protein LogIntensities originalRUN GROUP SUBJECT
#> 1 1 P14164 12.20158 180423_IP1745 Ctrl CtrlT_1
#> 2 2 P14164 12.00412 180423_IP1746 Ctrl CtrlT_2
#> 3 3 P14164 12.34676 180423_IP1747 Ctrl CtrlT_3
#> 4 4 P14164 11.58219 180423_IP1739 Osmo OsmoT_1
#> 5 6 P14164 11.78048 180423_IP1741_2 Osmo OsmoT_3
#> 6 3 P16622 12.85950 180423_IP1747 Ctrl CtrlT_3
#> TotalGroupMeasurements NumMeasuredFeature MissingPercentage more50missing
#> 1 15 5 0.0 FALSE
#> 2 15 5 0.0 FALSE
#> 3 15 5 0.0 FALSE
#> 4 15 3 0.4 FALSE
#> 5 15 3 0.4 FALSE
#> 6 9 3 0.0 FALSE
#> NumImputedFeature
#> 1 0
#> 2 0
#> 3 0
#> 4 0
#> 5 0
#> 6 0Again the summarization function returns a list of two dataframes one each for LiP and TrP. Each LiP and TrP is also a list with additional summary information. This summarized data can be used as input into some of the plotting functions included in the package.
2.2 Tryptic barplot
MSstatsLiP has a wide variety of plotting functionality to analysis and assess the results of experiments. Here we plot the number of half vs fully tryptic peptides per replicate.
trypticHistogramLiP(MSstatsLiP_Summarized,
"../inst/extdata/ExampleFastaFile.fasta",
color_scale = "bright",
address = FALSE)
2.3 Run Correlation Plot
MSstatsLiP also provides a function to plot run correlation.
2.4 Coefficient of Variation
Here we provide a simple script to examine the coefficient of variance between conditions
MSstatsLiP_Summarized[["LiP"]]$FeatureLevelData %>%
group_by(PEPTIDE, GROUP) %>%
summarize(cv = sd(INTENSITY) / mean(INTENSITY)) %>%
ggplot() + geom_violin(aes(x = GROUP, y = cv, fill = GROUP)) +
labs(title = "Coefficient of Variation between Condtions",
y = "Coefficient of Variation", x = "Conditon")
#> `summarise()` has grouped output by 'PEPTIDE'. You can override using the
#> `.groups` argument.
#> Warning: Removed 12 rows containing non-finite values (stat_ydensity).
2.5 QCPlot
The following plots are used to view the summarized data and check for potential systemic issues.
## Quality Control Plot
dataProcessPlotsLiP(MSstatsLiP_Summarized,
type = 'QCPLOT',
which.Peptide = "allonly",
address = FALSE)
#> Drew the Quality Contol plot(boxplot) for all ptms/proteins.
2.6 Profile Plot
dataProcessPlotsLiP(MSstatsLiP_Summarized,
type = 'ProfilePlot',
which.Peptide = c("P14164_ILQNDLK"),
address = FALSE)
#> Drew the Profile plot for P14164_ILQNDLK (1 of 1)
#> Drew the Profile plot for P14164_ILQNDLK ( 1 of 1 )
2.7 PCA Plot
In addition, Priciple Component Analysis can also be done on the summarized dataset. Three different PCA plots can be created one each for: Percent of explained variance per component, PC1 vs PC2 for peptides, and PC1 vs PC2 for conditions.
PCAPlotLiP(MSstatsLiP_Summarized,
bar.plot = FALSE,
protein.pca = FALSE,
comparison.pca = TRUE,
which.comparison = c("Ctrl", "Osmo"),
address=FALSE)
PCAPlotLiP(MSstatsLiP_Summarized,
bar.plot = FALSE,
protein.pca = TRUE,
comparison.pca = FALSE,
which.pep = c("P14164_ILQNDLK", "P17891_ALQLINQDDADIIGGRDR"),
address=FALSE)
2.8 Calculate Trypticity
Finally, the trypticity of a peptide can also be calculated and added to any dataframe with the ProteinName and PeptideSequence column.
feature_data <- data.table::copy(MSstatsLiP_Summarized$LiP$FeatureLevelData)
data.table::setnames(feature_data, c("PEPTIDE", "PROTEIN"),
c("PeptideSequence", "ProteinName"))
feature_data$PeptideSequence <- substr(feature_data$PeptideSequence, 1,
nchar(as.character(
feature_data$PeptideSequence)) - 2)
calculateTrypticity(feature_data, "../inst/extdata/ExampleFastaFile.fasta")
#> ProteinName PeptideSequence fully_TRI NSEMI_TRI CSEMI_TRI CTERMINUS
#> 1: P14164 ILQNDLK TRUE FALSE FALSE FALSE
#> 2: P16622 SHLQSNQLYSNQLPLDFALGK TRUE FALSE FALSE FALSE
#> 3: P17891 ALQLINQDDADIIGGRDR TRUE FALSE FALSE FALSE
#> 4: P17891 DDDTDFLK TRUE FALSE FALSE FALSE
#> 5: P24004 FIGASEQNIR TRUE FALSE FALSE FALSE
#> 6: P36112 SNDLLSGLTGSSQTR TRUE FALSE FALSE FALSE
#> 7: P38805 LGQTVGR TRUE FALSE FALSE FALSE
#> 8: P46959 DIIGKPYGSQIAIR TRUE FALSE FALSE FALSE
#> 9: P52893 SSSQGVEGIRK FALSE FALSE TRUE FALSE
#> 10: P52911 TWITEDDFEQIK TRUE FALSE FALSE FALSE
#> 11: P53235 ERQAVGDKLEDTQVLK TRUE FALSE FALSE FALSE
#> 12: P53858 FLDNHEVDSIVSLER TRUE FALSE FALSE FALSE
#> 13: Q02908 ISVISGVGVR TRUE FALSE FALSE FALSE
#> 14: Q12248 EFQSVSDLWK TRUE FALSE FALSE FALSE
#> NTERMINUS MISSED StartPos EndPos
#> 1: FALSE FALSE 358 365
#> 2: FALSE FALSE 354 375
#> 3: FALSE TRUE 196 214
#> 4: FALSE FALSE 21 29
#> 5: FALSE FALSE 770 780
#> 6: FALSE FALSE 102 117
#> 7: FALSE FALSE 205 212
#> 8: FALSE FALSE 49 63
#> 9: FALSE TRUE 221 232
#> 10: FALSE FALSE 117 129
#> 11: FALSE TRUE 607 623
#> 12: FALSE FALSE 491 506
#> 13: FALSE FALSE 528 538
#> 14: FALSE FALSE 57 67
MSstatsLiP_Summarized$LiP$FeatureLevelData%>%
rename(PeptideSequence=PEPTIDE, ProteinName=PROTEIN)%>%
mutate(PeptideSequence=substr(PeptideSequence, 1,
nchar(as.character(PeptideSequence))-2)
) %>% calculateTrypticity("../inst/extdata/ExampleFastaFile.fasta")
#> ProteinName PeptideSequence fully_TRI NSEMI_TRI CSEMI_TRI CTERMINUS
#> 1: P14164 ILQNDLK TRUE FALSE FALSE FALSE
#> 2: P16622 SHLQSNQLYSNQLPLDFALGK TRUE FALSE FALSE FALSE
#> 3: P17891 ALQLINQDDADIIGGRDR TRUE FALSE FALSE FALSE
#> 4: P17891 DDDTDFLK TRUE FALSE FALSE FALSE
#> 5: P24004 FIGASEQNIR TRUE FALSE FALSE FALSE
#> 6: P36112 SNDLLSGLTGSSQTR TRUE FALSE FALSE FALSE
#> 7: P38805 LGQTVGR TRUE FALSE FALSE FALSE
#> 8: P46959 DIIGKPYGSQIAIR TRUE FALSE FALSE FALSE
#> 9: P52893 SSSQGVEGIRK FALSE FALSE TRUE FALSE
#> 10: P52911 TWITEDDFEQIK TRUE FALSE FALSE FALSE
#> 11: P53235 ERQAVGDKLEDTQVLK TRUE FALSE FALSE FALSE
#> 12: P53858 FLDNHEVDSIVSLER TRUE FALSE FALSE FALSE
#> 13: Q02908 ISVISGVGVR TRUE FALSE FALSE FALSE
#> 14: Q12248 EFQSVSDLWK TRUE FALSE FALSE FALSE
#> NTERMINUS MISSED StartPos EndPos
#> 1: FALSE FALSE 358 365
#> 2: FALSE FALSE 354 375
#> 3: FALSE TRUE 196 214
#> 4: FALSE FALSE 21 29
#> 5: FALSE FALSE 770 780
#> 6: FALSE FALSE 102 117
#> 7: FALSE FALSE 205 212
#> 8: FALSE FALSE 49 63
#> 9: FALSE TRUE 221 232
#> 10: FALSE FALSE 117 129
#> 11: FALSE TRUE 607 623
#> 12: FALSE FALSE 491 506
#> 13: FALSE FALSE 528 538
#> 14: FALSE FALSE 57 673. Modeling
The modeling function groupComparisonLiP takes as input the output of the summarization function dataSummarizationLiP.
3.1 Function
MSstatsLiP_model <- groupComparisonLiP(MSstatsLiP_Summarized,
fasta = "../inst/extdata/ExampleFastaFile.fasta",
use_log_file = FALSE,
append = FALSE)
#> Starting PTM modeling...
#> INFO [2022-04-28 17:05:21] == Start to test and get inference in whole plot ...
#>
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#> INFO [2022-04-28 17:05:21] == Comparisons for all proteins are done.
#> Starting Protein modeling...
#> INFO [2022-04-28 17:05:21] == Start to test and get inference in whole plot ...
#>
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#> INFO [2022-04-28 17:05:21] == Comparisons for all proteins are done.
#> Starting adjustment...
#> Warning in as.data.table.list(x, keep.rownames = keep.rownames, check.names =
#> check.names, : Item 1 has 0 rows but longest item has 1; filled with NA
#> Warning in as.data.table.list(x, keep.rownames = keep.rownames, check.names =
#> check.names, : Item 2 has 0 rows but longest item has 1; filled with NA
#> Warning in as.data.table.list(x, keep.rownames = keep.rownames, check.names =
#> check.names, : Item 3 has 0 rows but longest item has 1; filled with NA
#> Warning in as.data.table.list(x, keep.rownames = keep.rownames, check.names =
#> check.names, : Item 1 has 0 rows but longest item has 1; filled with NA
#> Warning in as.data.table.list(x, keep.rownames = keep.rownames, check.names =
#> check.names, : Item 2 has 0 rows but longest item has 1; filled with NA
#> Warning in as.data.table.list(x, keep.rownames = keep.rownames, check.names =
#> check.names, : Item 3 has 0 rows but longest item has 1; filled with NA
lip_model <- MSstatsLiP_model[["LiP.Model"]]
trp_model <- MSstatsLiP_model[["TrP.Model"]]
adj_lip_model <- MSstatsLiP_model[["Adjusted.LiP.Model"]]
head(lip_model)
#> ProteinName PeptideSequence FULL_PEPTIDE Label
#> 1: P14164 ILQNDLK P14164_ILQNDLK Ctrl vs Osmo
#> 2: P16622 SHLQSNQLYSNQLPLDFALGK P16622_SHLQSNQLYSNQLPLDFALGK Ctrl vs Osmo
#> 3: P17891 ALQLINQDDADIIGGRDR P17891_ALQLINQDDADIIGGRDR Ctrl vs Osmo
#> 4: P17891 DDDTDFLK P17891_DDDTDFLK Ctrl vs Osmo
#> 5: P24004 FIGASEQNIR P24004_FIGASEQNIR Ctrl vs Osmo
#> 6: P36112 SNDLLSGLTGSSQTR P36112_SNDLLSGLTGSSQTR Ctrl vs Osmo
#> log2FC SE Tvalue DF pvalue adj.pvalue issue
#> 1: 1.0601391 0.219397767 4.8320413 4 0.008448744 0.03942747 NA
#> 2: 0.1655540 0.277961791 0.5955998 3 0.593383886 0.71358286 NA
#> 3: 0.5436687 0.296272579 1.8350288 4 0.140405767 0.28081153 NA
#> 4: -0.3294106 0.173801944 -1.8953216 4 0.130943731 0.28081153 NA
#> 5: 1.7019345 0.006157026 276.4215332 1 0.002303066 0.03224292 NA
#> 6: -0.1584000 0.601777383 -0.2632203 1 0.836145500 0.85751951 NA
#> MissingPercentage ImputationPercentage fully_TRI NSEMI_TRI CSEMI_TRI
#> 1: 0.0000000 0 TRUE FALSE FALSE
#> 2: 0.1666667 0 TRUE FALSE FALSE
#> 3: 0.0000000 0 TRUE FALSE FALSE
#> 4: 0.0000000 0 TRUE FALSE FALSE
#> 5: 0.5000000 0 TRUE FALSE FALSE
#> 6: 0.5000000 0 TRUE FALSE FALSE
#> CTERMINUS NTERMINUS MISSED StartPos EndPos
#> 1: FALSE FALSE FALSE 358 365
#> 2: FALSE FALSE FALSE 354 375
#> 3: FALSE FALSE TRUE 196 214
#> 4: FALSE FALSE FALSE 21 29
#> 5: FALSE FALSE FALSE 770 780
#> 6: FALSE FALSE FALSE 102 117
head(trp_model)
#> Protein Label log2FC SE Tvalue DF pvalue
#> 1: P14164 Ctrl vs Osmo 0.50281634 0.14796419 3.398230 3 0.04251661
#> 2: P16622 Ctrl vs Osmo -0.48689817 0.38376886 -1.268728 1 0.42494298
#> 3: P17891 Ctrl vs Osmo 1.15384384 0.52228878 2.209207 4 0.09170709
#> 4: P24004 Ctrl vs Osmo -0.09463078 0.03735157 -2.533516 1 0.23932853
#> 5: P36112 Ctrl vs Osmo -0.85301975 0.23381706 -3.648236 4 0.02180540
#> 6: P38805 Ctrl vs Osmo 0.40570919 0.25707606 1.578168 4 0.18966680
#> adj.pvalue issue MissingPercentage ImputationPercentage
#> 1: 0.09211932 NA 0.3000000 0
#> 2: 0.48236810 NA 0.5000000 0
#> 3: 0.17031317 NA 0.3194444 0
#> 4: 0.34569677 NA 0.5000000 0
#> 5: 0.06993334 NA 0.4393939 0
#> 6: 0.30820855 NA 0.2500000 0
head(adj_lip_model)
#> FULL_PEPTIDE Label ProteinName PeptideSequence
#> 1: P14164_ILQNDLK Ctrl vs Osmo P14164 ILQNDLK
#> 2: P16622_SHLQSNQLYSNQLPLDFALGK Ctrl vs Osmo P16622 SHLQSNQLYSNQLPLDFALGK
#> 3: P17891_ALQLINQDDADIIGGRDR Ctrl vs Osmo P17891 ALQLINQDDADIIGGRDR
#> 4: P17891_DDDTDFLK Ctrl vs Osmo P17891 DDDTDFLK
#> 5: P24004_FIGASEQNIR Ctrl vs Osmo P24004 FIGASEQNIR
#> 6: P36112_SNDLLSGLTGSSQTR Ctrl vs Osmo P36112 SNDLLSGLTGSSQTR
#> log2FC SE Tvalue DF pvalue adj.pvalue fully_TRI
#> 1: 0.5573227 0.26462952 2.106049 6.635790 0.07538247 0.15076494 TRUE
#> 2: 0.6524522 0.47385789 1.376894 2.129099 0.29543338 0.51700842 TRUE
#> 3: -0.6101751 0.60046899 -1.016164 6.332717 0.34679119 0.53945296 TRUE
#> 4: -1.4832544 0.55044772 -2.694633 4.875155 0.04419616 0.12374924 TRUE
#> 5: 1.7965653 0.03785563 47.458342 1.054304 0.01100135 0.04872612 TRUE
#> 6: 0.6946197 0.64560548 1.075920 1.317219 0.44032063 0.54772318 TRUE
#> NSEMI_TRI CSEMI_TRI CTERMINUS NTERMINUS MISSED StartPos EndPos issue
#> 1: FALSE FALSE FALSE FALSE FALSE 358 365 NA
#> 2: FALSE FALSE FALSE FALSE FALSE 354 375 NA
#> 3: FALSE FALSE FALSE FALSE TRUE 196 214 NA
#> 4: FALSE FALSE FALSE FALSE FALSE 21 29 NA
#> 5: FALSE FALSE FALSE FALSE FALSE 770 780 NA
#> 6: FALSE FALSE FALSE FALSE FALSE 102 117 NA
## Number of significant adjusted lip peptides
adj_lip_model %>% filter(adj.pvalue < .05) %>% nrow()
#> [1] 4The groupComparisonLiP function outputs a list with three separate models. These models are as follows: LiP model, TrP model, and adjusted LiP model.
3.3 Heatmap
3.4 Barcode
Here we show a barcode plot, showing the coverage of LiP and TrP peptides. This function requires the data in MSstatsLiP format and the path to a fasta file.
StructuralBarcodePlotLiP(MSstatsLiP_model,
"../inst/extdata/ExampleFastaFile.fasta",
model_type = "Adjusted", which.prot = c("P53858"),
address = FALSE)