| Title: | Statistical Characterization of Post-translational Modifications |
|---|---|
| Description: | MSstatsPTM provides general statistical methods for quantitative characterization of post-translational modifications (PTMs). Supports DDA, DIA, SRM, and tandem mass tag (TMT) labeling. Typically, the analysis involves the quantification of PTM sites (i.e., modified residues) and their corresponding proteins, as well as the integration of the quantification results. MSstatsPTM provides functions for summarization, estimation of PTM site abundance, and detection of changes in PTMs across experimental conditions. |
| Authors: | Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Olga Vitek [aut] |
| Maintainer: | Devon Kohler <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 2.7.1 |
| Built: | 2024-09-25 03:24:25 UTC |
| Source: | https://github.com/bioc/MSstatsPTM |
annotSite annotates modified sites as their residues and locations.
annotSite(aaIndex, residue, lenIndex = NULL)annotSite(aaIndex, residue, lenIndex = NULL)
aaIndex |
An integer vector. Location of the sites. |
residue |
A string vector. Amino acid residue. |
lenIndex |
An integer. Default is |
A string.
annotSite(10, "K") annotSite(10, "K", 3L)annotSite(10, "K") annotSite(10, "K", 3L)
To illustrate the quantitative data and quality control of MS runs, dataProcessPlotsPTM takes the quantitative data from dataSummarizationPTM or dataSummarizationPTM_TMT to plot the following : (1) profile plot (specify "ProfilePlot" in option type), to identify the potential sources of variation for each protein; (2) quality control plot (specify "QCPlot" in option type), to evaluate the systematic bias between MS runs.
dataProcessPlotsPTM( data, type = "PROFILEPLOT", ylimUp = FALSE, ylimDown = FALSE, x.axis.size = 10, y.axis.size = 10, text.size = 4, text.angle = 90, legend.size = 7, dot.size.profile = 2, ncol.guide = 5, width = 10, height = 12, ptm.title = "All PTMs", protein.title = "All Proteins", which.PTM = "all", which.Protein = NULL, originalPlot = TRUE, summaryPlot = TRUE, address = "" )dataProcessPlotsPTM( data, type = "PROFILEPLOT", ylimUp = FALSE, ylimDown = FALSE, x.axis.size = 10, y.axis.size = 10, text.size = 4, text.angle = 90, legend.size = 7, dot.size.profile = 2, ncol.guide = 5, width = 10, height = 12, ptm.title = "All PTMs", protein.title = "All Proteins", which.PTM = "all", which.Protein = NULL, originalPlot = TRUE, summaryPlot = TRUE, address = "" )
data |
name of the list with PTM and (optionally) Protein data, which
can be the output of the MSstatsPTM
|
type |
choice of visualization. "ProfilePlot" represents profile plot of log intensities across MS runs. "QCPlot" represents box plots of log intensities across channels and MS runs. |
ylimUp |
upper limit for y-axis in the log scale. FALSE(Default) for Profile Plot and QC Plot uses 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 uses 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 Profile plot and QC plot. Default is 4. |
text.angle |
angle of labels represented each condition at the top of Profile plot and QC plot. Default is 0. |
legend.size |
size of legend above 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 pdf file. Default is 10. |
height |
height of the saved pdf file. Default is 10. |
ptm.title |
title of overall PTM QC plot |
protein.title |
title of overall Protein QC plot |
which.PTM |
PTM list to draw plots. List can be names of PTMs or order numbers of PTMs. Default is "all", which generates all plots for each protein. For QC plot, "allonly" will generate one QC plot with all proteins. |
which.Protein |
List of proteins to plot. Will plot all PTMs associated with listed Proteins. Default is NULL which will default to which.PTM. |
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. |
plot or pdf
# QCPlot dataProcessPlotsPTM(summary.data, type = 'QCPLOT', which.PTM = "allonly", address = FALSE) #ProfilePlot dataProcessPlotsPTM(summary.data, type = 'PROFILEPLOT', which.PTM = "Q9UQ80_K376", address = FALSE)# QCPlot dataProcessPlotsPTM(summary.data, type = 'QCPLOT', which.PTM = "allonly", address = FALSE) #ProfilePlot dataProcessPlotsPTM(summary.data, type = 'PROFILEPLOT', which.PTM = "Q9UQ80_K376", address = FALSE)
Utilizes functionality from MSstats to clean, summarize, and
normalize PTM and protein level data. Imputes missing values, performs
normalization, and summarizes data. PTM data is summarized up to the
modification and protein data is summarized up to the protein level. Takes
as input the output of the included converters (see included raw.input
data object for required input format).
dataSummarizationPTM( data, logTrans = 2, normalization = "equalizeMedians", normalization.PTM = "equalizeMedians", nameStandards = NULL, nameStandards.PTM = NULL, featureSubset = "all", featureSubset.PTM = "all", remove_uninformative_feature_outlier = FALSE, remove_uninformative_feature_outlier.PTM = FALSE, min_feature_count = 2, min_feature_count.PTM = 1, n_top_feature = 3, n_top_feature.PTM = 3, summaryMethod = "TMP", equalFeatureVar = TRUE, censoredInt = "NA", MBimpute = TRUE, MBimpute.PTM = TRUE, remove50missing = FALSE, fix_missing = NULL, maxQuantileforCensored = 0.999, use_log_file = TRUE, append = TRUE, verbose = TRUE, log_file_path = NULL, base = "MSstatsPTM_log_" )dataSummarizationPTM( data, logTrans = 2, normalization = "equalizeMedians", normalization.PTM = "equalizeMedians", nameStandards = NULL, nameStandards.PTM = NULL, featureSubset = "all", featureSubset.PTM = "all", remove_uninformative_feature_outlier = FALSE, remove_uninformative_feature_outlier.PTM = FALSE, min_feature_count = 2, min_feature_count.PTM = 1, n_top_feature = 3, n_top_feature.PTM = 3, summaryMethod = "TMP", equalFeatureVar = TRUE, censoredInt = "NA", MBimpute = TRUE, MBimpute.PTM = TRUE, remove50missing = FALSE, fix_missing = NULL, maxQuantileforCensored = 0.999, use_log_file = TRUE, append = TRUE, verbose = TRUE, log_file_path = NULL, base = "MSstatsPTM_log_" )
data |
name of the list with PTM and (optionally) unmodified protein data.tables, which can be the output of the MSstatsPTM converter functions |
logTrans |
logarithm transformation with base 2(default) or 10 |
normalization |
normalization for the protein level dataset, to remove systematic bias between MS runs. There are three different normalizations supported. 'equalizeMedians'(default) represents constant normalization (equalizing the medians) based on reference signals is performed. 'quantile' represents quantile normalization based on reference signals is performed. 'globalStandards' represents normalization with global standards proteins. FALSE represents no normalization is performed |
normalization.PTM |
normalization for PTM level dataset. Default is "equalizeMedians" Can be adjusted to any of the options described above. |
nameStandards |
vector of global standard peptide names for protein dataset. only for normalization with global standard peptides. |
nameStandards.PTM |
Same as above for PTM dataset. |
featureSubset |
"all" (default) uses all features that the data set has. "top3" uses top 3 features which have highest average of log-intensity across runs. "topN" uses top N features which has highest average of log-intensity across runs. It needs the input for n_top_feature option. "highQuality" flags uninformative feature and outliers. |
featureSubset.PTM |
For PTM dataset only. Options same as above. |
remove_uninformative_feature_outlier |
For protein dataset only. It only works after users used featureSubset="highQuality" in dataProcess. TRUE allows to remove 1) the features are flagged in the column, feature_quality="Uninformative" which are features with bad quality, 2) outliers that are flagged in the column, is_outlier=TRUE, for run-level summarization. FALSE (default) uses all features and intensities for run-level summarization. |
remove_uninformative_feature_outlier.PTM |
For PTM dataset only. Options same as above. |
min_feature_count |
optional. Only required if featureSubset = "highQuality". Defines a minimum number of informative features a protein needs to be considered in the feature selection algorithm. |
min_feature_count.PTM |
For PTM dataset only. Options the same as above. Default is 1 due to low average feature count for PTMs. |
n_top_feature |
For protein dataset only. The number of top features for featureSubset='topN'. Default is 3, which means to use top 3 features. |
n_top_feature.PTM |
For PTM dataset only. Options same as above. |
summaryMethod |
"TMP"(default) means Tukey's median polish, which is robust estimation method. "linear" uses linear mixed model. |
equalFeatureVar |
only for summaryMethod="linear". default is TRUE. Logical variable for whether the model should account for heterogeneous variation among intensities from different features. Default is TRUE, which assume equal variance among intensities from features. FALSE means that we cannot assume equal variance among intensities from features, then we will account for heterogeneous variation from different features. |
censoredInt |
Missing values are censored or at random. 'NA' (default) assumes that all 'NA's in 'Intensity' column are censored. '0' uses zero intensities as censored intensity. In this case, NA intensities are missing at random. The output from Skyline should use '0'. Null assumes that all NA intensites are randomly missing. |
MBimpute |
For protein dataset only. only for summaryMethod="TMP" and censoredInt='NA' or '0'. TRUE (default) imputes 'NA' or '0' (depending on censoredInt option) by Accelated failure model. FALSE uses the values assigned by cutoffCensored. |
MBimpute.PTM |
For PTM dataset only. Options same as above. |
remove50missing |
only for summaryMethod="TMP". TRUE removes the runs which have more than 50% missing values. FALSE is default. |
fix_missing |
Default is Null. Optional, same as the 'fix_missing' parameter in MSstatsConvert::MSstatsBalancedDesign function |
maxQuantileforCensored |
Maximum quantile for deciding censored missing values. default is 0.999 |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing will be printed to the console. |
log_file_path |
character. Path to a file to which information about
data processing will be saved.
If not provided, such a file will be created automatically.
If |
base |
start of the file name. |
list of summarized PTM and Protein results. These results contain the reformatted input to the summarization function, as well as run-level summarization results.
head(raw.input$PTM) head(raw.input$PROTEIN) quant.lf.msstatsptm = dataSummarizationPTM(raw.input, verbose = FALSE) head(quant.lf.msstatsptm$PTM$ProteinLevelData)head(raw.input$PTM) head(raw.input$PROTEIN) quant.lf.msstatsptm = dataSummarizationPTM(raw.input, verbose = FALSE) head(quant.lf.msstatsptm$PTM$ProteinLevelData)
Utilizes functionality from MSstatsTMT to clean, summarize, and
normalize PTM and protein level data. Imputes missing values, performs
normalization, and summarizes data. PTM data is summarized up to the
modification and protein data is summarized up to the protein level. Takes
as input the output of the included converters (see included raw.input.tmt
data object for required input format).
dataSummarizationPTM_TMT( data, method = "msstats", global_norm = TRUE, global_norm.PTM = TRUE, reference_norm = TRUE, reference_norm.PTM = TRUE, remove_norm_channel = TRUE, remove_empty_channel = TRUE, MBimpute = TRUE, MBimpute.PTM = TRUE, maxQuantileforCensored = NULL, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )dataSummarizationPTM_TMT( data, method = "msstats", global_norm = TRUE, global_norm.PTM = TRUE, reference_norm = TRUE, reference_norm.PTM = TRUE, remove_norm_channel = TRUE, remove_empty_channel = TRUE, MBimpute = TRUE, MBimpute.PTM = TRUE, maxQuantileforCensored = NULL, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
data |
Name of the output of MSstatsPTM converter function or peptide-level quantified data from other tools. It should be a list containing one or two data tables, named PTM and PROTEIN for modified and unmodified datasets. The list must at least contain the PTM dataset. The data should have columns ProteinName, PeptideSequence, Charge, PSM, Mixture, TechRepMixture, 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 for unmodified peptide level data (equalizing the medians across all the channels and MS runs). Default is TRUE. It will be performed before protein-level summarization. |
global_norm.PTM |
Same as above for modified peptide level data. Default is TRUE. |
reference_norm |
Reference channel based normalization between MS runs on unmodified protein level data. 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. |
reference_norm.PTM |
Same as above for modified peptide level data. Default is TRUE. |
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. |
MBimpute.PTM |
Same as above for modified peptide level data. Default is TRUE |
maxQuantileforCensored |
We assume missing values are censored. maxQuantileforCensored is Maximum quantile for deciding censored missing value, for instance, 0.999. Default is Null. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing will be printed to the console. |
log_file_path |
character. Path to a file to which information about
data processing will be saved.
If not provided, such a file will be created automatically.
If |
list of two data.tables
head(raw.input.tmt$PTM) head(raw.input.tmt$PROTEIN) quant.tmt.msstatsptm = dataSummarizationPTM_TMT(raw.input.tmt, method = "msstats", verbose = FALSE) head(quant.tmt.msstatsptm$PTM$ProteinLevelData)head(raw.input.tmt$PTM) head(raw.input.tmt$PROTEIN) quant.tmt.msstatsptm = dataSummarizationPTM_TMT(raw.input.tmt, method = "msstats", verbose = FALSE) head(quant.tmt.msstatsptm$PTM$ProteinLevelData)
Calculate sample size for future experiments of a PTM experiment based on intensity-based linear model. Calculation is only available for group comparison experimental designs (not including time series). Two options of the calculation: (1) number of biological replicates per condition, (2) power.
designSampleSizePTM( data, desiredFC, FDR = 0.05, numSample = TRUE, power = 0.8, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL, base = "MSstatsPTM_log_" )designSampleSizePTM( data, desiredFC, FDR = 0.05, numSample = TRUE, power = 0.8, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL, base = "MSstatsPTM_log_" )
data |
output of the groupComparisonPTM function. |
desiredFC |
the range of a desired fold change which includes the lower and upper values of the desired fold change. |
FDR |
a pre-specified false discovery ratio (FDR) to control the overall false positive rate. Default is 0.05 |
numSample |
minimal number of biological replicates per condition. TRUE represents you require to calculate the sample size for this category, else you should input the exact number of biological replicates. |
power |
a pre-specified statistical power which defined as the probability of detecting a true fold change. TRUE represent you require to calculate the power for this category, else you should input the average of power you expect. Default is 0.9 |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing will be printed to the console. |
log_file_path |
character. Path to a file to which information about
data processing will be saved.
If not provided, such a file will be created automatically.
If |
base |
start of the file name. |
The function fits the model and uses variance components to calculate sample size. The underlying model fitting with intensity-based linear model with technical MS run replication. Estimated sample size is rounded to 0 decimal. The function can only obtain either one of the categories of the sample size calculation (numSample, numPep, numTran, power) at the same time.
data.frame - sample size calculation results including varibles: desiredFC, numSample, FDR, and power.
model.lf.msstatsptm = groupComparisonPTM(summary.data, data.type = "LabelFree", verbose = FALSE) #(1) Minimal number of biological replicates per condition designSampleSizePTM(data=model.lf.msstatsptm, numSample=TRUE, desiredFC=c(2.0,2.75), FDR=0.05, power=0.8) #(2) Power calculation designSampleSizePTM(data=model.lf.msstatsptm, numSample=5, desiredFC=c(2.0,2.75), FDR=0.05, power=TRUE)model.lf.msstatsptm = groupComparisonPTM(summary.data, data.type = "LabelFree", verbose = FALSE) #(1) Minimal number of biological replicates per condition designSampleSizePTM(data=model.lf.msstatsptm, numSample=TRUE, desiredFC=c(2.0,2.75), FDR=0.05, power=0.8) #(2) Power calculation designSampleSizePTM(data=model.lf.msstatsptm, numSample=5, desiredFC=c(2.0,2.75), FDR=0.05, power=TRUE)
Takes as input the report.tsv file from DIA-NN and converts it into
MSstatsPTM format. Requires PSM and an annotation file. Optionally an
additional report.tsv file for a corresponding global profiling run can
be included.
DIANNtoMSstatsPTMFormat( input, annotation, input_protein = NULL, annotation_protein = NULL, fasta_path = NULL, use_unmod_peptides = FALSE, protein_id_col = "Protein.Group", fasta_protein_name = "uniprot_ac", global_qvalue_cutoff = 0.01, qvalue_cutoff = 0.01, pg_qvalue_cutoff = 0.01, useUniquePeptide = TRUE, removeFewMeasurements = TRUE, removeOxidationMpeptides = TRUE, removeProtein_with1Feature = FALSE, MBR = TRUE, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )DIANNtoMSstatsPTMFormat( input, annotation, input_protein = NULL, annotation_protein = NULL, fasta_path = NULL, use_unmod_peptides = FALSE, protein_id_col = "Protein.Group", fasta_protein_name = "uniprot_ac", global_qvalue_cutoff = 0.01, qvalue_cutoff = 0.01, pg_qvalue_cutoff = 0.01, useUniquePeptide = TRUE, removeFewMeasurements = TRUE, removeOxidationMpeptides = TRUE, removeProtein_with1Feature = FALSE, MBR = TRUE, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
input |
data.frame of |
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. |
input_protein |
same as |
annotation_protein |
same as |
fasta_path |
A string of path to a FASTA file, used to match PTM peptides. |
use_unmod_peptides |
Boolean if the unmodified peptides in the input
file should be used to construct the unmodified protein output. Only used if
|
protein_id_col |
Use 'Protein.Groups'(default) column for protein name. |
fasta_protein_name |
Name of column that matches with the protein names
in |
global_qvalue_cutoff |
The global qvalue cutoff. Default is 0.01. |
qvalue_cutoff |
local qvalue cutoff for library. Default is 0.01. |
pg_qvalue_cutoff |
local qvalue cutoff for protein groups Run should be the same as filename. Default is 0.01. |
useUniquePeptide |
logical, if TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein. |
removeFewMeasurements |
TRUE (default) will remove the features that have 1 or 2 measurements within each Run. |
removeOxidationMpeptides |
TRUE (default) will remove the peptides including oxidation (M) sequence. |
removeProtein_with1Feature |
TRUE will remove the proteins which have only 1 peptide and charge. Defaut is FALSE. |
MBR |
If analaysis was done with match between runs or not. Default is TRUE. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing wil be printed to the console. |
log_file_path |
character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If 'append = TRUE', has to be a valid path to a file. |
list of one or two data.frame of class MSstatsTMT, named PTM and PROTEIN
# ptm = read.csv("Phospho/report.tsv", sep="\t") # protein = read.csv("Protein/report.tsv", sep="\t") # annotation = read.csv("Phospho/annotation.csv") # annotation_protein = read.csv("Protein/annotation.csv") #DIANNtoMSstatsPTMFormat(ptm, annotation, # protein, annotation_protein, # fasta_path="fasta_file.fasta")# ptm = read.csv("Phospho/report.tsv", sep="\t") # protein = read.csv("Protein/report.tsv", sep="\t") # annotation = read.csv("Phospho/annotation.csv") # annotation_protein = read.csv("Protein/annotation.csv") #DIANNtoMSstatsPTMFormat(ptm, annotation, # protein, annotation_protein, # fasta_path="fasta_file.fasta")
Automatically created by FragPipe, manually checked by the user and input into the FragPipetoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.
fragpipe_annotationfragpipe_annotation
A data.table with 7 columns.
Run : Run name that matches exactly with FragPipe run. Used to join evidence and metadata in annotation file.
Fraction : If multiple fractions were used (i.e. the same mixture split into multiple fractions) enter that here. TechRepMixture : Multiple runs using the same bioreplicate
Channel : Mixture channel used
Condition : Name of condition that was used for each run.
Mixture : The unique mixture (plex) name
BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.
head(fragpipe_annotation)head(fragpipe_annotation)
Automatically created by FragPipe, manually checked by the user and input into the FragPipetoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.
fragpipe_annotation_proteinfragpipe_annotation_protein
A data.table with 7 columns.
Run : Run name that matches exactly with FragPipe run. Used to join evidence and metadata in annotation file.
Fraction : If multiple fractions were used (i.e. the same mixture split into multiple fractions) enter that here. TechRepMixture : Multiple runs using the same bioreplicate
Channel : Mixture channel used
Condition : Name of condition that was used for each run.
Mixture : The unique mixture (plex) name
BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.
head(fragpipe_annotation_protein)head(fragpipe_annotation_protein)
This dataset was provided by the FragPipe team at the Nesvilab. It was processed using Philosopher and targeted Phosphorylation.
fragpipe_inputfragpipe_input
A data.table with 29 columns and 246 rows.
head(fragpipe_input)head(fragpipe_input)
This dataset was provided by the FragPipe team at the Nesvilab. It was processed using Philosopher and targeted Phosphorylation.
fragpipe_input_proteinfragpipe_input_protein
A data.table with 27 columns and 47 rows.
head(fragpipe_input_protein)head(fragpipe_input_protein)
Takes as input TMT experiments which are the output of Fragpipe and converts
into MSstatsPTM format. Requires msstats.csv file and an annotation file.
Optionally an additional msstats.csv file can be uploaded if a
corresponding global profiling run was performed. Site localization is
performed and only high probability localizations are kept.
FragPipetoMSstatsPTMFormat( input, annotation = NULL, input_protein = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, label_type = "TMT", protein_id_col = "Protein", peptide_id_col = "Peptide.Sequence", mod_id_col = "STY", localization_cutoff = 0.75, remove_unlocalized_peptides = TRUE, Purity_cutoff = 0.6, PeptideProphet_prob_cutoff = 0.7, useUniquePeptide = TRUE, rmPSM_withfewMea_withinRun = FALSE, rmPeptide_OxidationM = TRUE, rmProtein_with1Feature = FALSE, summaryforMultipleRows = sum, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )FragPipetoMSstatsPTMFormat( input, annotation = NULL, input_protein = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, label_type = "TMT", protein_id_col = "Protein", peptide_id_col = "Peptide.Sequence", mod_id_col = "STY", localization_cutoff = 0.75, remove_unlocalized_peptides = TRUE, Purity_cutoff = 0.6, PeptideProphet_prob_cutoff = 0.7, useUniquePeptide = TRUE, rmPSM_withfewMea_withinRun = FALSE, rmPeptide_OxidationM = TRUE, rmProtein_with1Feature = FALSE, summaryforMultipleRows = sum, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
input |
data.frame of |
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. |
input_protein |
same as |
annotation_protein |
same as |
use_unmod_peptides |
Boolean if the unmodified peptides in the input
file should be used to construct the unmodified protein output. Only used if
|
label_type |
Type of labeling used for experiment. Must be one of "LF" or "TMT". Default is "TMT". |
protein_id_col |
Use 'Protein'(default) column for TMT. This needs to be changed to "ProteinName" for label free. For TMT, 'Master.Protein.Accessions' can be used instead to get the protein ID with single protein. |
peptide_id_col |
Use 'Peptide.Sequence'(default) column for TMT. Must be changed to "PeptideSequence" for label free. "Modified.Peptide.Sequence" can be used instead to get the modified peptide sequence. |
mod_id_col |
Column containing the modified Amino Acids. For example, a Phosphorylation experiment may pass |
localization_cutoff |
Minimum localization score required to keep modification. Default is .75. |
remove_unlocalized_peptides |
Boolean indicating if peptides without all sites localized should be kept. Default is TRUE (non-localized sites will be removed). |
Purity_cutoff |
Cutoff for purity. Default is 0.6. Purity refers to how much of the detected ion signal within a specific inclusion window belongs to the target molecule or its closely related forms, compared to any other unwanted signals or noise. Higher values indicate greater purity. |
PeptideProphet_prob_cutoff |
Cutoff for the peptide identification probability. Default is 0.7. The probability is confidence score determined by PeptideProphet and higher values indicate greater confidence. |
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. |
rmProtein_with1Feature |
TRUE will remove the proteins which have only 1 peptide and charge. Defaut is FALSE. |
summaryforMultipleRows |
sum (default) or max - when there are multiple measurements for certain feature in certain run, select the feature with the largest summation or maximal value. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing wil be printed to the console. |
log_file_path |
character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If 'append = TRUE', has to be a valid path to a file. |
list of one or two data.frame of class MSstatsTMT, named PTM and PROTEIN
# TMT Example (with global profiling run) head(fragpipe_input) head(fragpipe_annotation) head(fragpipe_input_protein) head(fragpipe_annotation_protein) msstats_data = FragPipetoMSstatsPTMFormat(fragpipe_input, fragpipe_annotation, fragpipe_input_protein, fragpipe_annotation_protein, label_type="TMT", mod_id_col = "STY", localization_cutoff=.75, remove_unlocalized_peptides=TRUE) head(msstats_data$PTM) head(msstats_data$PROTEIN) # LFQ Example (w/out global profiling run) input = system.file("tinytest/raw_data/Fragpipe/MSstats.csv", package = "MSstatsPTM") input = data.table::fread(input) annot = system.file("tinytest/raw_data/Fragpipe/experiment_annotation.tsv", package = "MSstatsPTM") annot = data.table::fread(annot) msstats_data = FragPipetoMSstatsPTMFormat(input, annot, label_type="LF", mod_id_col = "STY", localization_cutoff=.75, protein_id_col = "ProteinName", peptide_id_col = "PeptideSequence") head(msstats_data$PTM) # Note that this is NULL because we did not include a global profiling run. # Ideally, you should include an independent global profiling run. head(msstats_data$PROTEIN)# TMT Example (with global profiling run) head(fragpipe_input) head(fragpipe_annotation) head(fragpipe_input_protein) head(fragpipe_annotation_protein) msstats_data = FragPipetoMSstatsPTMFormat(fragpipe_input, fragpipe_annotation, fragpipe_input_protein, fragpipe_annotation_protein, label_type="TMT", mod_id_col = "STY", localization_cutoff=.75, remove_unlocalized_peptides=TRUE) head(msstats_data$PTM) head(msstats_data$PROTEIN) # LFQ Example (w/out global profiling run) input = system.file("tinytest/raw_data/Fragpipe/MSstats.csv", package = "MSstatsPTM") input = data.table::fread(input) annot = system.file("tinytest/raw_data/Fragpipe/experiment_annotation.tsv", package = "MSstatsPTM") annot = data.table::fread(annot) msstats_data = FragPipetoMSstatsPTMFormat(input, annot, label_type="LF", mod_id_col = "STY", localization_cutoff=.75, protein_id_col = "ProteinName", peptide_id_col = "PeptideSequence") head(msstats_data$PTM) # Note that this is NULL because we did not include a global profiling run. # Ideally, you should include an independent global profiling run. head(msstats_data$PROTEIN)
To analyze the results of modeling changes in abundance of modified peptides and overall protein, groupComparisonPlotsPTM takes as input the results of the groupComparisonPTM function. It asses the results of three models: unadjusted PTM, adjusted PTM, and overall protein. To asses the results of the model, the following visualizations can be created: (1) VolcanoPlot (specify "VolcanoPlot" in option type), to plot peptides or proteins and their significance for each model. (2) Heatmap (specify "Heatmap" in option type), to evaluate the fold change between conditions and peptides/proteins
groupComparisonPlotsPTM( data = data, type, sig = 0.05, FCcutoff = FALSE, logBase.pvalue = 10, ylimUp = FALSE, ylimDown = FALSE, xlimUp = FALSE, x.axis.size = 10, y.axis.size = 10, dot.size = 3, text.size = 4, text.angle = 0, legend.size = 13, ProteinName = TRUE, colorkey = TRUE, numProtein = 50, width = 10, height = 10, which.Comparison = "all", which.PTM = "all", address = "" )groupComparisonPlotsPTM( data = data, type, sig = 0.05, FCcutoff = FALSE, logBase.pvalue = 10, ylimUp = FALSE, ylimDown = FALSE, xlimUp = FALSE, x.axis.size = 10, y.axis.size = 10, dot.size = 3, text.size = 4, text.angle = 0, legend.size = 13, ProteinName = TRUE, colorkey = TRUE, numProtein = 50, width = 10, height = 10, which.Comparison = "all", which.PTM = "all", address = "" )
data |
name of the list with models, which can be the output of the
MSstatsPTM |
type |
choice of visualization, one of VolcanoPlot or Heatmap |
sig |
FDR cutoff for the adjusted p-values in heatmap and volcano plot. level of significance for comparison plot. 100(1-sig)% confidence interval will be drawn. sig=0.05 is default. |
FCcutoff |
For volcano plot or heatmap, whether involve fold change cutoff or not. FALSE (default) means no fold change cutoff is applied for significance analysis. FCcutoff = specific value means specific fold change cutoff is applied. |
logBase.pvalue |
for volcano plot or heatmap, (-) logarithm transformation of adjusted p-value with base 2 or 10(default). |
ylimUp |
for all three plots, upper limit for y-axis. FALSE (default) for volcano plot/heatmap use maximum of -log2 (adjusted p-value) or -log10 (adjusted p-value). FALSE (default) for comparison plot uses maximum of log-fold change + CI. |
ylimDown |
for all three plots, lower limit for y-axis. FALSE (default) for volcano plot/heatmap use minimum of -log2 (adjusted p-value) or -log10 (adjusted p-value). FALSE (default) for comparison plot uses minimum of log-fold change - CI. |
xlimUp |
for Volcano plot, the limit for x-axis. FALSE (default) for use maximum for absolute value of log-fold change or 3 as default if maximum for absolute value of log-fold change is less than 3. |
x.axis.size |
size of axes labels, e.g. name of the comparisons in heatmap, and in comparison plot. Default is 10. |
y.axis.size |
size of axes labels, e.g. name of targeted proteins in heatmap. Default is 10. |
dot.size |
size of dots in volcano plot and comparison plot. Default is 3. |
text.size |
size of ProteinName label in the graph for Volcano Plot. Default is 4. |
text.angle |
angle of x-axis labels represented each comparison at the bottom of graph in comparison plot. Default is 0. |
legend.size |
size of legend for color at the bottom of volcano plot. Default is 7. |
ProteinName |
for volcano plot only, whether display protein names or not. TRUE (default) means protein names, which are significant, are displayed next to the points. FALSE means no protein names are displayed. |
colorkey |
TRUE(default) shows colorkey. |
numProtein |
The number of proteins which will be presented in each heatmap. Default is 50. |
width |
width of the saved file. Default is 10. |
height |
height of the saved file. Default is 10. |
which.Comparison |
list of comparisons to draw plots. List can be labels of comparisons or order numbers of comparisons from levels(data$Label) , such as levels(testResultMultiComparisons$ComparisonResult$Label). Default is "all", which generates all plots for each protein. |
which.PTM |
Protein list to draw comparison plots. List can be names of Proteins or order numbers of Proteins from levels(testResultMultiComparisons$ComparisonResult$Protein). Default is "all", which generates all comparison plots for each protein. |
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 "VolcanoPlot.pdf" or "Heatmap.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 |
plot or pdf
model.lf.msstatsptm = groupComparisonPTM(summary.data, data.type = "LabelFree") groupComparisonPlotsPTM(data = model.lf.msstatsptm, type = "VolcanoPlot", FCcutoff= 2, logBase.pvalue = 2, address=FALSE)model.lf.msstatsptm = groupComparisonPTM(summary.data, data.type = "LabelFree") groupComparisonPlotsPTM(data = model.lf.msstatsptm, type = "VolcanoPlot", FCcutoff= 2, logBase.pvalue = 2, address=FALSE)
Takes summarized PTM and protein data from dataSummarizationPTM or
dataSummarizationPTM_TMT functions and performs differential analysis.
Leverages unmodified protein data to perform adjustment and deconvolute the
effect of the PTM and unmodified protein. If protein data is unavailable,
PTM data can still be passed into the function, however adjustment can not be
performed. All model results are returned for completeness.
groupComparisonPTM( data, data.type, contrast.matrix = "pairwise", moderated = FALSE, adj.method = "BH", log_base = 2, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL, base = "MSstatsPTM_log_" )groupComparisonPTM( data, data.type, contrast.matrix = "pairwise", moderated = FALSE, adj.method = "BH", log_base = 2, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL, base = "MSstatsPTM_log_" )
data |
list of summarized datasets. Output of MSstatsPTM summarization
function |
data.type |
string indicating experimental acquisition type. "TMT" is used for TMT labeled experiments. For all other experiments (Label Free/ DDA/ DIA) use "LabelFree". |
contrast.matrix |
comparison between conditions of interests. Default models full pairwise comparison between all conditions |
moderated |
For TMT experiments only. TRUE will moderate t statistic; FALSE (default) uses ordinary t statistic. Default is FALSE. |
adj.method |
For TMT experiemnts only. Adjusted method for multiple comparison. "BH" is default. "BH" is used for all other experiment types |
log_base |
For non-TMT experiments only. The base of the logarithm used in summarization. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing will be printed to the console. |
log_file_path |
character. Path to a file to which information about
data processing will be saved.
If not provided, such a file will be created automatically.
If |
base |
start of the file name. |
list of modeling results. Includes PTM, PROTEIN, and ADJUSTED data.tables with their corresponding model results.
model.lf.msstatsptm = groupComparisonPTM(summary.data, data.type = "LabelFree", verbose = FALSE)model.lf.msstatsptm = groupComparisonPTM(summary.data, data.type = "LabelFree", verbose = FALSE)
locateMod locates modified sites with a peptide.
locateMod(peptide, aaStart, residueSymbol)locateMod(peptide, aaStart, residueSymbol)
peptide |
A string. Peptide sequence. |
aaStart |
An integer. Starting index of the peptide. |
residueSymbol |
A string. Modification residue and denoted symbol. |
A string.
locateMod("P*EP*TIDE", 3, "\\*")locateMod("P*EP*TIDE", 3, "\\*")
PTMlocate annotates modified sites with associated peptides.
locatePTM(peptide, uniprot, fasta, modResidue, modSymbol, rmConfound = FALSE)locatePTM(peptide, uniprot, fasta, modResidue, modSymbol, rmConfound = FALSE)
peptide |
A string vector of peptide sequences. The peptide sequence does not include its preceding and following AAs. |
uniprot |
A string vector of Uniprot identifiers of the peptides' originating proteins. UniProtKB entry isoform sequence is used. |
fasta |
A data.table with FASTA information. Output of |
modResidue |
A string. Modifiable amino acid residues. |
modSymbol |
A string. Symbol of a modified site. |
rmConfound |
A logical. |
A data frame with three columns: uniprot_iso, peptide,
site.
fasta = tidyFasta(system.file("extdata", "O13297.fasta", package="MSstatsPTM")) locatePTM("DRVSYIHNDSC*TR", "O13297", fasta, "C", "\\*")fasta = tidyFasta(system.file("extdata", "O13297.fasta", package="MSstatsPTM")) locatePTM("DRVSYIHNDSC*TR", "O13297", fasta, "C", "\\*")
Must be manually created by the user and input into the MaxQtoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.
maxq_lf_annotationmaxq_lf_annotation
A data.table with 5 columns.
Run : Run name that matches exactly with MaxQuant run. Used to join evidence and metadata in annotation file.
Condition : Name of condition that was used for each run.
BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.
Raw.file : Run name that matches exactly with MaxQuant run. Used to join evidence and metadata in annotation file.
IsotopeLabelType: Name of isotope label. May be all L or unique
depending on experimental design.
head(maxq_lf_annotation)head(maxq_lf_annotation)
Experiment was performed by the Olsen lab and published on Nat. Commun. (citation below).
maxq_lf_evidencemaxq_lf_evidence
a data.table with 63 columns and 511 rows, the output of MaxQuant
Bekker-Jensen, D.B., Bernhardt, O.M., Hogrebe, A. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun 11, 787 (2020). https://doi.org/10.1038/s41467-020-14609-1
The experiment was processed using MaxQuant by the computational proteomics team at Pfizer (Liang Xue and Pierre Jean).
The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.
head(maxq_lf_evidence)head(maxq_lf_evidence)
Must be manually created by the user and input into the MaxQtoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.
maxq_tmt_annotationmaxq_tmt_annotation
A data.table with 7 columns.
Run : Run name that matches exactly with MaxQuant run. Used to join evidence and metadata in annotation file.
Fraction : If multiple fractions were used (i.e. the same mixture split into multiple fractions) enter that here. TechRepMixture : Multiple runs using the same bioreplicate
Channel : Mixture channel used
Condition : Name of condition that was used for each run.
Mixture : The unique mixture (plex) name
BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.
head(maxq_tmt_annotation)head(maxq_tmt_annotation)
Experiment was performed by the Olsen lab and published on Nat. Commun. (citation below).
maxq_tmt_evidencemaxq_tmt_evidence
a data.table with 96 columns and 199 rows, the output of MaxQuant
Hogrebe, A., von Stechow, L., Bekker-Jensen, D.B. et al. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat Commun 9, 1045 (2018). https://doi.org/10.1038/s41467-018-03309-6
The experiment was processed using MaxQuant by the computational proteomics team at Pfizer (Liang Xue and Pierre Jean).
The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.
head(maxq_tmt_evidence)head(maxq_tmt_evidence)
Takes as input LF/TMT experiments from MaxQ and converts the data into the
format needed for MSstatsPTM. Requires modified evidence.txt file from MaxQ
and an annotation file for PTM data. To adjust modified peptides for changes
in global protein level, unmodified TMT experimental data must also be
returned. Optionally can use Phospho(STY)Sites.txt (or other PTM specific
files) from MaxQuant, but this is not recommended. If PTM specific file
provided, the raw intensities must be provided, not a ratio.
MaxQtoMSstatsPTMFormat( evidence = NULL, annotation = NULL, fasta_path, fasta_protein_name = "uniprot_ac", mod_id = "\\(Phospho \\(STY\\)\\)", sites_data = NULL, evidence_prot = NULL, proteinGroups = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, labeling_type = "LF", mod_num = "Single", TMT_keyword = "TMT", ptm_keyword = "phos", which_proteinid_ptm = "Proteins", which_proteinid_protein = "Proteins", remove_other_mods = TRUE, removeMpeptides = FALSE, removeOxidationMpeptides = FALSE, removeProtein_with1Peptide = FALSE, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )MaxQtoMSstatsPTMFormat( evidence = NULL, annotation = NULL, fasta_path, fasta_protein_name = "uniprot_ac", mod_id = "\\(Phospho \\(STY\\)\\)", sites_data = NULL, evidence_prot = NULL, proteinGroups = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, labeling_type = "LF", mod_num = "Single", TMT_keyword = "TMT", ptm_keyword = "phos", which_proteinid_ptm = "Proteins", which_proteinid_protein = "Proteins", remove_other_mods = TRUE, removeMpeptides = FALSE, removeOxidationMpeptides = FALSE, removeProtein_with1Peptide = FALSE, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
evidence |
name of 'evidence.txt' data, which includes feature-level data for enriched (PTM) data. |
annotation |
data frame annotation file for the ptm level data. Contains column Run, Fraction, TechRepMixture, Mixture, Channel, BioReplicate, Condition. |
fasta_path |
A string of path to a FASTA file, used to match PTM peptides. |
fasta_protein_name |
Name of fasta column that matches with protein name
in evidence file. Default is |
mod_id |
Character that indicates the modification of interest. Default
is |
sites_data |
(Not recommended. Only used if evidence file not provided. Only works for TMT labeled data) Modified peptide output from MaxQuant. For example, a phosphorylation experiment would require the Phospho(STY)Sites.txt file |
evidence_prot |
name of 'evidence.txt' data, which includes feature-level data for global profiling (unmodified) data. |
proteinGroups |
name of 'proteinGroups.txt' data. It needs to matching
protein group ID in |
annotation_protein |
data frame annotation file for the protein level data. Contains column Run, Fraction, TechRepMixture, Mixture, Channel, BioReplicate, Condition. |
use_unmod_peptides |
Boolean if the unmodified peptides in the input
file should be used to construct the unmodified protein output. Only used if
|
labeling_type |
Either |
mod_num |
(Only if |
TMT_keyword |
(Only if |
ptm_keyword |
(Only if |
which_proteinid_ptm |
For PTM dataset, which column to use for protein name. Use 'Proteins'(default) column for protein name. 'Leading.proteins' or 'Leading.razor.protein' or 'Gene.names' can be used instead to get the protein ID with single protein. However, those can potentially have the shared peptides. |
which_proteinid_protein |
For Protein dataset, which column to use for protein name. Same options as above. |
remove_other_mods |
Remove peptides which include modfications other
than the one listed in |
removeMpeptides |
If Oxidation (M) modifications should be removed. Default is TRUE. |
removeOxidationMpeptides |
TRUE will remove the peptides including 'oxidation (M)' in modification. FALSE is default. |
removeProtein_with1Peptide |
TRUE will remove the proteins which have only 1 peptide and charge. FALSE is default. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing wil be printed to the console. |
log_file_path |
character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If 'append = TRUE', has to be a valid path to a file. |
a list of two data.tables named 'PTM' and 'PROTEIN' in the format required by MSstatsPTM.
# TMT experiment head(maxq_tmt_evidence) head(maxq_tmt_annotation) msstats_format_tmt = MaxQtoMSstatsPTMFormat(evidence=maxq_tmt_evidence, annotation=maxq_tmt_annotation, fasta=system.file("extdata", "maxq_tmt_fasta.fasta", package="MSstatsPTM"), fasta_protein_name="uniprot_ac", mod_id="\\(Phospho \\(STY\\)\\)", use_unmod_peptides=TRUE, labeling_type = "TMT", which_proteinid_ptm = "Proteins") head(msstats_format_tmt$PTM) head(msstats_format_tmt$PROTEIN) # LF experiment head(maxq_lf_evidence) head(maxq_lf_annotation) msstats_format_lf = MaxQtoMSstatsPTMFormat(evidence=maxq_lf_evidence, annotation=maxq_lf_annotation, fasta=system.file("extdata", "maxq_lf_fasta.fasta", package="MSstatsPTM"), fasta_protein_name="uniprot_ac", mod_id="\\(Phospho \\(STY\\)\\)", use_unmod_peptides=TRUE, labeling_type = "LF", which_proteinid_ptm = "Proteins") head(msstats_format_lf$PTM) head(msstats_format_lf$PROTEIN)# TMT experiment head(maxq_tmt_evidence) head(maxq_tmt_annotation) msstats_format_tmt = MaxQtoMSstatsPTMFormat(evidence=maxq_tmt_evidence, annotation=maxq_tmt_annotation, fasta=system.file("extdata", "maxq_tmt_fasta.fasta", package="MSstatsPTM"), fasta_protein_name="uniprot_ac", mod_id="\\(Phospho \\(STY\\)\\)", use_unmod_peptides=TRUE, labeling_type = "TMT", which_proteinid_ptm = "Proteins") head(msstats_format_tmt$PTM) head(msstats_format_tmt$PROTEIN) # LF experiment head(maxq_lf_evidence) head(maxq_lf_annotation) msstats_format_lf = MaxQtoMSstatsPTMFormat(evidence=maxq_lf_evidence, annotation=maxq_lf_annotation, fasta=system.file("extdata", "maxq_lf_fasta.fasta", package="MSstatsPTM"), fasta_protein_name="uniprot_ac", mod_id="\\(Phospho \\(STY\\)\\)", use_unmod_peptides=TRUE, labeling_type = "LF", which_proteinid_ptm = "Proteins") head(msstats_format_lf$PTM) head(msstats_format_lf$PROTEIN)
Import Metamorpheus files into PTM format
MetamorpheusToMSstatsPTMFormat( input, annotation, fasta_path, input_protein = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, mod_ids = c("\\[Common Biological:Phosphorylation on S\\]"), useUniquePeptide = TRUE, removeFewMeasurements = TRUE, removeProtein_with1Feature = FALSE, summaryforMultipleRows = max, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )MetamorpheusToMSstatsPTMFormat( input, annotation, fasta_path, input_protein = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, mod_ids = c("\\[Common Biological:Phosphorylation on S\\]"), useUniquePeptide = TRUE, removeFewMeasurements = TRUE, removeProtein_with1Feature = FALSE, summaryforMultipleRows = max, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
input |
name of Metamorpheus output file, which is tabular format. Use the AllQuantifiedPeaks.tsv file from the Metamorpheus output. |
annotation |
name of 'annotation.txt' data which includes Condition, BioReplicate. |
fasta_path |
string containing path to the corresponding fasta file for the modified peptide dataset. |
input_protein |
same as |
annotation_protein |
same as |
use_unmod_peptides |
If |
mod_ids |
List of modifications of interest. Default
is a list with only |
useUniquePeptide |
TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein. |
removeFewMeasurements |
TRUE (default) will remove the features that have 1 or 2 measurements across runs. |
removeProtein_with1Feature |
TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default. |
summaryforMultipleRows |
max(default) or sum - when there are multiple measurements for certain feature and certain run, use highest or sum of multiple intensities. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing wil be printed to the console. |
log_file_path |
character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If 'append = TRUE', has to be a valid path to a file. |
a list of two data.tables named 'PTM' and 'PROTEIN' in the format required by MSstatsPTM.
Anthony Wu
input = system.file("tinytest/raw_data/Metamorpheus/AllQuantifiedPeaks.tsv", package = "MSstatsPTM") input = data.table::fread(input) annot = system.file("tinytest/raw_data/Metamorpheus/ExperimentalDesign.tsv", package = "MSstatsPTM") annot = data.table::fread(annot) input_protein = system.file("tinytest/raw_data/Metamorpheus/AllQuantifiedPeaksGlobalProteome.tsv", package = "MSstatsPTM") input_protein = data.table::fread(input_protein) annot_protein = system.file("tinytest/raw_data/Metamorpheus/ExperimentalDesignGlobalProteome.tsv", package = "MSstatsPTM") annot_protein = data.table::fread(annot_protein) fasta_path=system.file("extdata", "metamorpheus_fasta.fasta", package="MSstatsPTM") metamorpheus_imported = MetamorpheusToMSstatsPTMFormat( input, annot, fasta_path=fasta_path, input_protein=input_protein, annotation_protein=annot_protein, use_unmod_peptides=FALSE, mod_ids = c("\\[Common Fixed:Carbamidomethyl on C\\]") ) head(metamorpheus_imported$PTM) head(metamorpheus_imported$PROTEIN)input = system.file("tinytest/raw_data/Metamorpheus/AllQuantifiedPeaks.tsv", package = "MSstatsPTM") input = data.table::fread(input) annot = system.file("tinytest/raw_data/Metamorpheus/ExperimentalDesign.tsv", package = "MSstatsPTM") annot = data.table::fread(annot) input_protein = system.file("tinytest/raw_data/Metamorpheus/AllQuantifiedPeaksGlobalProteome.tsv", package = "MSstatsPTM") input_protein = data.table::fread(input_protein) annot_protein = system.file("tinytest/raw_data/Metamorpheus/ExperimentalDesignGlobalProteome.tsv", package = "MSstatsPTM") annot_protein = data.table::fread(annot_protein) fasta_path=system.file("extdata", "metamorpheus_fasta.fasta", package="MSstatsPTM") metamorpheus_imported = MetamorpheusToMSstatsPTMFormat( input, annot, fasta_path=fasta_path, input_protein=input_protein, annotation_protein=annot_protein, use_unmod_peptides=FALSE, mod_ids = c("\\[Common Fixed:Carbamidomethyl on C\\]") ) head(metamorpheus_imported$PTM) head(metamorpheus_imported$PROTEIN)
A set of tools for detecting differentially abundant PTMs and proteins in shotgun mass spectrometry-based proteomic experiments. The package can handle a variety of acquisition types, including label free and TMT experiments, acquired with DDA, DIA, SRM or PRM acquisition methods. The package includes tools to convert raw data from different spectral processing tools, summarize feature intensities, and fit a linear mixed effects model. A major advantage of the package is to leverage a separate global profiling run and adjust the PTM fold change for changes in the unmodified protein, showing the unconvoluted PTM fold change. Finally, the package includes functionality to plot a variety of data visualizations.
FragPipetoMSstatsPTMFormat : Generates MSstatsPTM
required input format for TMT FragePipe outputs.
MaxQtoMSstatsPTMFormat : Generates MSstatsPTM required
input format for label-free and TMT MaxQuant outputs.
ProgenesistoMSstatsPTMFormat : Generates MSstatsPTM
required input format for label-free Progenesis outputs.
SpectronauttoMSstatsPTMFormat : Generates MSstatsPTM
required input format for label-free Spectronaut outputs.
SkylinetoMSstatsPTMFormat : Generates
MSstatsPTM required input format for Skyline outputs.
PStoMSstatsPTMFormat : Generates
MSstatsPTM required input format for PEAKS outputs.
PDtoMSstatsPTMFormat : Generates
MSstatsPTM required input format for Proteome Discoverer outputs.
dataSummarizationPTM : Summarizes PSM level
quantification to peptide (modification) and protein level quantification.
For use in label-free analysis
dataSummarizationPTM_TMT : Summarizes PSM level
quantification to peptide (modification) and protein level quantification.
For use in TMT analysis.
dataProcessPlotsPTM : Visualization for explanatory
data analysis. Specifically gives ability to plot Profile and Quality
Control plots.
groupComparisonPTM : Tests for significant changes in
PTM and protein abundance across conditions. Adjusts PTM fold change for
changes in protein abundance.
groupComparisonPlotsPTM : Visualization for model-based
analysis and summarization
Locate modification site number and amino acid
MSstatsPTMSiteLocator( data, protein_name_col = "ProteinName", unmod_pep_col = "PeptideSequence", mod_pep_col = "PeptideModifiedSequence", clean_mod = FALSE, fasta_file = NULL, fasta_protein_name = "header", mod_id = "\\*", localization_scores = FALSE, localization_cutoff = 0.75, remove_unlocalized_peptides = TRUE, terminus_included = FALSE, terminus_id = "\\.", mod_id_is_numeric = FALSE, remove_underscores = FALSE, remove_other_mods = FALSE, bracket = FALSE, replace_text = FALSE )MSstatsPTMSiteLocator( data, protein_name_col = "ProteinName", unmod_pep_col = "PeptideSequence", mod_pep_col = "PeptideModifiedSequence", clean_mod = FALSE, fasta_file = NULL, fasta_protein_name = "header", mod_id = "\\*", localization_scores = FALSE, localization_cutoff = 0.75, remove_unlocalized_peptides = TRUE, terminus_included = FALSE, terminus_id = "\\.", mod_id_is_numeric = FALSE, remove_underscores = FALSE, remove_other_mods = FALSE, bracket = FALSE, replace_text = FALSE )
data |
|
protein_name_col |
Name of column indicating protein. Default is
|
unmod_pep_col |
Name of column indicating unmodified peptide sequence. Default
is |
mod_pep_col |
Name of column indicating modified peptide sequence. Default
is |
clean_mod |
Remove special characters and numbers around modification
name. Default is |
fasta_file |
File path to FASTA file that matches with proteins in
|
fasta_protein_name |
Name of fasta file column that matches with
|
mod_id |
String that indicates what amino acid was modified in
|
localization_scores |
Boolean indicating if mod id is a localization
score. If TRUE, |
localization_cutoff |
Default is .75. Localization probabilities below
cutoffs will be removed. |
remove_unlocalized_peptides |
Default is TRUE. If |
terminus_included |
Boolean indicating if the |
terminus_id |
String that indicates what the terminus amino acid is. Default is '.'. |
mod_id_is_numeric |
Boolean indicating if modification identifier is a number instead of a character (i.e. +80 vs *). |
remove_underscores |
Boolean indicating if underscores around peptide exist. These should be removed to properly count where in sequence the modification occurred. |
remove_other_mods |
keeping mods that are not of interest can mess up the amino acid count. Remove them if they are causing issues. |
bracket |
bracket type that encompasses PTM (usually |
replace_text |
If PTM is noted by text (i.e. |
data.table with site location added into Protein column.
##TODO##TODO
Must be manually created by the user and input into the PDtoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.
pd_annotationpd_annotation
A data.table with 3 columns.
Run : Run name that matches exactly with PD run. Used to join evidence and metadata in annotation file.
Condition : Name of condition that was used for each run.
BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.
head(pd_annotation)head(pd_annotation)
Experiment was performed by the Olsen lab and published on Nat. Commun. (citation below).
pd_psm_inputpd_psm_input
a data.table with 60 columns and 1657 rows, the output of PD
Bekker-Jensen, D.B., Bernhardt, O.M., Hogrebe, A. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun 11, 787 (2020). https://doi.org/10.1038/s41467-020-14609-1
The experiment was processed using Proteome Discoverer by the computational proteomics team at Pfizer (Liang Xue and Pierre Jean).
The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.
head(pd_psm_input)head(pd_psm_input)
output using example data provided in package
pd_testing_outputpd_testing_output
a list with 2 data.frames
The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.
head(pd_testing_output)head(pd_testing_output)
Import Proteome Discoverer files, identify modification site location.
PDtoMSstatsPTMFormat( input, annotation, fasta_path, protein_input = NULL, annotation_protein = NULL, labeling_type = "LF", mod_id = "\\(Phospho\\)", use_localization_cutoff = FALSE, keep_all_mods = FALSE, use_unmod_peptides = FALSE, fasta_protein_name = "uniprot_iso", localization_cutoff = 75, remove_unlocalized_peptides = TRUE, useNumProteinsColumn = FALSE, useUniquePeptide = TRUE, summaryforMultipleRows = max, removeFewMeasurements = TRUE, removeOxidationMpeptides = FALSE, removeProtein_with1Peptide = FALSE, which_quantification = "Precursor.Area", which_proteinid = "Protein.Group.Accessions", use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )PDtoMSstatsPTMFormat( input, annotation, fasta_path, protein_input = NULL, annotation_protein = NULL, labeling_type = "LF", mod_id = "\\(Phospho\\)", use_localization_cutoff = FALSE, keep_all_mods = FALSE, use_unmod_peptides = FALSE, fasta_protein_name = "uniprot_iso", localization_cutoff = 75, remove_unlocalized_peptides = TRUE, useNumProteinsColumn = FALSE, useUniquePeptide = TRUE, summaryforMultipleRows = max, removeFewMeasurements = TRUE, removeOxidationMpeptides = FALSE, removeProtein_with1Peptide = FALSE, which_quantification = "Precursor.Area", which_proteinid = "Protein.Group.Accessions", use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
input |
PD report corresponding with enriched experimental data. |
annotation |
name of 'annotation.txt' or 'annotation.csv' data which includes Condition, BioReplicate, Run information. 'Run' will be matched with 'Spectrum.File' |
fasta_path |
string containing path to the corresponding fasta file for the modified peptide dataset. |
protein_input |
PD report corresponding with unmodified experimental data. |
annotation_protein |
Same format as |
labeling_type |
type of experimental design, must be one of |
mod_id |
Character that indicates the modification of interest. Default
is |
use_localization_cutoff |
Boolean indicating whether to use a custom
localization cutoff or rely on PD's modifications column. |
keep_all_mods |
Boolean indicating whether to keep or remove peptides
not in |
use_unmod_peptides |
If |
fasta_protein_name |
Name of fasta column that matches with protein name
in evidence file. Default is |
localization_cutoff |
Minimum localization score required to keep modification. Default is .75. |
remove_unlocalized_peptides |
Boolean indicating if peptides without all sites localized should be kept. Default is TRUE (non-localized sites will be removed). |
useNumProteinsColumn |
TRUE removes peptides which have more than 1 in Proteins column of PD output. |
useUniquePeptide |
TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein. |
summaryforMultipleRows |
max(default) or sum - when there are multiple measurements for certain feature and certain run, use highest or sum of multiple intensities. |
removeFewMeasurements |
TRUE (default) will remove the features that have 1 or 2 measurements across runs. |
removeOxidationMpeptides |
TRUE will remove the peptides including 'oxidation (M)' in modification. FALSE is default. |
removeProtein_with1Peptide |
TRUE will remove the proteins which have only 1 peptide and charge. FALSE is default. |
which_quantification |
Use 'Precursor.Area'(default) column for quantified intensities. 'Intensity' or 'Area' can be used instead. |
which_proteinid |
Use 'Protein.Accessions'(default) column for protein name. 'Master.Protein.Accessions' can be used instead. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing will be printed to the console |
log_file_path |
character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If 'append = TRUE', has to be a valid path to a file. |
list of data.table
head(pd_psm_input) head(pd_annotation) msstats_format = PDtoMSstatsPTMFormat(pd_psm_input, pd_annotation, system.file("extdata", "pd_fasta.fasta", package="MSstatsPTM"), use_unmod_peptides=TRUE, which_proteinid = "Master.Protein.Accessions") head(msstats_format$PTM) head(msstats_format$PROTEIN)head(pd_psm_input) head(pd_annotation) msstats_format = PDtoMSstatsPTMFormat(pd_psm_input, pd_annotation, system.file("extdata", "pd_fasta.fasta", package="MSstatsPTM"), use_unmod_peptides=TRUE, which_proteinid = "Master.Protein.Accessions") head(msstats_format$PTM) head(msstats_format$PROTEIN)
Converts non-TMT Progenesis output into the format needed for MSstatsPTM
ProgenesistoMSstatsPTMFormat( ptm_input, annotation, global_protein_input = FALSE, fasta_path = FALSE, useUniquePeptide = TRUE, summaryforMultipleRows = max, removeFewMeasurements = TRUE, removeOxidationMpeptides = FALSE, removeProtein_with1Peptide = FALSE, mod.num = "Single" )ProgenesistoMSstatsPTMFormat( ptm_input, annotation, global_protein_input = FALSE, fasta_path = FALSE, useUniquePeptide = TRUE, summaryforMultipleRows = max, removeFewMeasurements = TRUE, removeOxidationMpeptides = FALSE, removeProtein_with1Peptide = FALSE, mod.num = "Single" )
ptm_input |
name of Progenesis output with modified peptides, which is wide-format. 'Accession', Sequence', 'Modification', 'Charge' and one column for each run are required |
annotation |
name of 'annotation.txt' or 'annotation.csv' data which includes Condition, BioReplicate, Run, and Type (PTM or Protein) information. It will be matched with the column name of input for MS runs. Please note PTM and global Protein run names are often different, which is why an additional Type column indicating Protein or PTM is required. |
global_protein_input |
name of Progenesis output with unmodified peptides, which is wide-format. 'Accession', Sequence', 'Modification', 'Charge' and one column for each run are required |
fasta_path |
string containing path to the corresponding fasta file for the modified peptide dataset. |
useUniquePeptide |
TRUE(default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein. |
summaryforMultipleRows |
max(default) or sum - when there are multiple measurements for certain feature and certain run, use highest or sum of multiple intensities. |
removeFewMeasurements |
TRUE (default) will remove the features that have 1 or 2 measurements across runs. |
removeOxidationMpeptides |
TRUE will remove the modified peptides including 'Oxidation (M)' sequence. FALSE is default. |
removeProtein_with1Peptide |
TRUE will remove the proteins which have only 1 peptide and charge. FALSE is default. |
mod.num |
For modified peptide dataset, must be one of |
a list of two data.tables named 'PTM' and 'PROTEIN' in the format required by MSstatsPTM.
input = system.file("tinytest/raw_data/Progenesis/progenesis_peptide.csv", package = "MSstatsPTM") input = data.table::fread(input) colnames(input) = unlist(input[1,]) input = input[-1,] annot = system.file("tinytest/raw_data/Progenesis/phospho_annotation.csv", package = "MSstatsPTM") annot = data.table::fread(annot) prog_imported = ProgenesistoMSstatsPTMFormat( input, annot ) head(prog_imported$PTM)input = system.file("tinytest/raw_data/Progenesis/progenesis_peptide.csv", package = "MSstatsPTM") input = data.table::fread(input) colnames(input) = unlist(input[1,]) input = input[-1,] annot = system.file("tinytest/raw_data/Progenesis/phospho_annotation.csv", package = "MSstatsPTM") annot = data.table::fread(annot) prog_imported = ProgenesistoMSstatsPTMFormat( input, annot ) head(prog_imported$PTM)
Currently only supports label-free quantification.
PStoMSstatsPTMFormat( input, annotation, input_protein = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, target_modification = NULL, remove_oxidation_peptides = FALSE, remove_multi_mod_types = FALSE, summaryforMultipleRows = max, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )PStoMSstatsPTMFormat( input, annotation, input_protein = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, target_modification = NULL, remove_oxidation_peptides = FALSE, remove_multi_mod_types = FALSE, summaryforMultipleRows = max, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
input |
name of Peaks Studio PTM output |
annotation |
name of annotation file which includes Raw.file, Condition, BioReplicate, Run. For example annotation see example below. |
input_protein |
name of Peaks Studio unmodified protein output (optional) |
annotation_protein |
name of annotation file which includes Raw.file, Condition, BioReplicate, Run for unmodified protein output. |
use_unmod_peptides |
Boolean if the unmodified peptides in the input
file should be used to construct the unmodified protein output. Only used if
|
target_modification |
Character name of modification of interest. To
use all mod types, leave as |
remove_oxidation_peptides |
Boolean if Oxidation (M) modifications
should be removed. Default is |
remove_multi_mod_types |
Used if |
summaryforMultipleRows |
max(default) or sum - when there are multiple measurements for certain feature and certain run, use highest or sum of multiple intensities. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing wil be printed to the console. |
log_file_path |
character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If 'append = TRUE', has to be a valid path to a file. |
list of data.table
# The output should be in the following format. head(raw.input$PTM) head(raw.input$PROTEIN)# The output should be in the following format. head(raw.input$PTM) head(raw.input$PROTEIN)
It can be the output of MSstatsPTM converter ProgenesistoMSstatsPTMFormat or other MSstats converter functions (Please see MSstatsPTM_LabelFree_Workflow vignette). The dataset is formatted as a list with two data.tables named PTM and PROTEIN. In each data.table the variables are as follows:
raw.inputraw.input
A list of two data.tables named PTM and PROTEIN with 1745 and 478 rows respectively.
#'
ProteinName : Name of protein with modification site mapped in with an underscore. ie "Protein_4_Y474"
PeptideSequence
Condition : Condition (ex. Healthy, Cancer, Time0)
BioReplicate : Unique ID for biological subject.
Run : MS run ID.
Intensity
PrecursorCharge
FragmentIon
ProductCharge
IsotopeLabelType
head(raw.input$PTM) head(raw.input$PROTEIN)head(raw.input$PTM) head(raw.input$PROTEIN)
It can be the output of MSstatsPTM converter MaxQtoMSstatsPTMFormat or other MSstatsTMT converter functions (Please see MSstatsPTM_TMT_Workflow vignette). The dataset is formatted as a list with two data.tables named PTM and PROTEIN. In each data.table the variables are as follows:
raw.input.tmtraw.input.tmt
A list of two data.tables named PTM and PROTEIN with 1716 and 29221 rows respectively.
ProteinName : Name of protein with modification site mapped in with an underscore. ie "Protein_4_Y474"
PeptideSequence
Charge
PSM
Mixture : Mixture of samples labeled with different TMT reagents,
which can be analyzed in
a single mass spectrometry experiment. If the channal doesn't have sample,
please add Empty' under Condition. \item TechRepMixture : Technical replicate of one mixture. One mixture may have multiple technical replicates. For example, if TechRepMixture' = 1, 2 are the two technical replicates of
one mixture, then they should match
with same Mixture' value. \item Run : MS run ID. \item Channel : Labeling information (126, ... 131). \item Condition : Condition (ex. Healthy, Cancer, Time0) \item BioReplicate : Unique ID for biological subject. If the channal doesn't have sample, please add Empty' under BioReplicate.
Intensity
head(raw.input.tmt$PTM) head(raw.input.tmt$PROTEIN)head(raw.input.tmt$PTM) head(raw.input.tmt$PROTEIN)
Currently only supports label-free quantification.
SkylinetoMSstatsPTMFormat( input, fasta_path, fasta_protein_name = "uniprot_iso", annotation = NULL, input_protein = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, removeiRT = TRUE, filter_with_Qvalue = TRUE, qvalue_cutoff = 0.01, use_unique_peptide = TRUE, remove_few_measurements = FALSE, remove_oxidation_peptides = FALSE, removeProtein_with1Feature = FALSE, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )SkylinetoMSstatsPTMFormat( input, fasta_path, fasta_protein_name = "uniprot_iso", annotation = NULL, input_protein = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, removeiRT = TRUE, filter_with_Qvalue = TRUE, qvalue_cutoff = 0.01, use_unique_peptide = TRUE, remove_few_measurements = FALSE, remove_oxidation_peptides = FALSE, removeProtein_with1Feature = FALSE, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
input |
name of Skyline PTM output |
fasta_path |
A string of path to a FASTA file, used to match PTM peptides. |
fasta_protein_name |
Name of fasta column that matches with protein name
in evidence file. Default is |
annotation |
name of 'annotation.txt' data which includes Condition, BioReplicate, Run. If annotation is already complete in Skyline, use annotation=NULL (default). It will use the annotation information from input. |
input_protein |
name of Skyline unmodified protein output (optional) |
annotation_protein |
name of 'annotation.txt' data which includes Condition,
BioReplicate, Run for unmodified protein output. This can be the same as
|
use_unmod_peptides |
Boolean if the unmodified peptides in the input
file should be used to construct the unmodified protein output. Only used if
|
removeiRT |
TRUE (default) will remove the proteins or peptides which are labeld 'iRT' in 'StandardType' column. FALSE will keep them. |
filter_with_Qvalue |
TRUE(default) will filter out the intensities that have greater than qvalue_cutoff in DetectionQValue column. Those intensities will be replaced with zero and will be considered as censored missing values for imputation purpose. |
qvalue_cutoff |
Cutoff for DetectionQValue. default is 0.01. |
use_unique_peptide |
TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein. |
remove_few_measurements |
TRUE will remove the features that have 1 or 2 measurements across runs. FALSE is default. |
remove_oxidation_peptides |
TRUE will remove the peptides including 'oxidation (M)' in modification. FALSE is default. |
removeProtein_with1Feature |
TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing wil be printed to the console. |
log_file_path |
character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If 'append = TRUE', has to be a valid path to a file. |
list of data.table
# The output should be in the following format. head(raw.input$PTM) head(raw.input$PROTEIN)# The output should be in the following format. head(raw.input$PTM) head(raw.input$PROTEIN)
Must be manually created by the user and input into the SpectronauttoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.
spectronaut_annotationspectronaut_annotation
A data.table with 5 columns.
Run : Run name that matches exactly with Spectronaut run. Used to join evidence and metadata in annotation file.
Condition : Name of condition that was used for each run.
BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.
Raw.file : Run name that matches exactly with Spectronaut run. Used to join evidence and metadata in annotation file.
head(spectronaut_annotation)head(spectronaut_annotation)
Experiment was performed by the Olsen lab and published on Nat. Commun. (citation below).
spectronaut_inputspectronaut_input
a data.table with 23 columns and 2683 rows, the output of Spectronaut
Bekker-Jensen, D.B., Bernhardt, O.M., Hogrebe, A. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun 11, 787 (2020). https://doi.org/10.1038/s41467-020-14609-1
The experiment was processed using Spectronaut by the computational proteomics team at Pfizer (Liang Xue and Pierre Jean).
The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.
head(spectronaut_input)head(spectronaut_input)
Converters label-free Spectronaut data into MSstatsPTM format. Requires PSM output from Spectronaut and a custom made annotation file, mapping the run name to the condition and bioreplicate. Can optionally take a seperate PSM file for a global profiling run. If no global profiling run provided, the function can extract the unmodified peptides from the PTM PSM file and use them as a global profiling run (not recommended).
SpectronauttoMSstatsPTMFormat( input, annotation = NULL, fasta_path = NULL, protein_input = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, intensity = "PeakArea", mod_id = "\\[Phospho \\(STY\\)\\]", fasta_protein_name = "uniprot_iso", remove_other_mods = TRUE, filter_with_Qvalue = TRUE, qvalue_cutoff = 0.01, useUniquePeptide = TRUE, removeFewMeasurements = TRUE, removeProtein_with1Feature = FALSE, summaryforMultipleRows = max, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )SpectronauttoMSstatsPTMFormat( input, annotation = NULL, fasta_path = NULL, protein_input = NULL, annotation_protein = NULL, use_unmod_peptides = FALSE, intensity = "PeakArea", mod_id = "\\[Phospho \\(STY\\)\\]", fasta_protein_name = "uniprot_iso", remove_other_mods = TRUE, filter_with_Qvalue = TRUE, qvalue_cutoff = 0.01, useUniquePeptide = TRUE, removeFewMeasurements = TRUE, removeProtein_with1Feature = FALSE, summaryforMultipleRows = max, use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
input |
name of Spectronaut PTM output, which is long-format. ProteinName, PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge, IsotopeLabelType, Condition, BioReplicate, Run, Intensity, F.ExcludedFromQuantification are required. Rows with F.ExcludedFromQuantification=True will be removed. |
annotation |
name of 'annotation.txt' data which includes Condition, BioReplicate, Run. If annotation is already complete in Spectronaut, use annotation=NULL (default). It will use the annotation information from input. |
fasta_path |
string containing path to the corresponding fasta file for the modified peptide dataset. |
protein_input |
name of Spectronaut global protein output, which is
as in the same format as |
annotation_protein |
name of annotation file for global protein data, in the same format as above. |
use_unmod_peptides |
If |
intensity |
'PeakArea'(default) uses not normalized peak area. 'NormalizedPeakArea' uses peak area normalized by Spectronaut. Default is NULL |
mod_id |
Character that indicates the modification of interest. Default
is |
fasta_protein_name |
Name of fasta column that matches with protein name
in evidence file. Default is |
remove_other_mods |
Remove peptides which include modfications other
than the one listed in |
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 zero 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. |
removeFewMeasurements |
TRUE (default) will remove the features that have 1 or 2 measurements across runs. |
removeProtein_with1Feature |
TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default. |
summaryforMultipleRows |
max(default) or sum - when there are multiple measurements for certain feature and certain run, use highest or sum of multiple intensities. |
use_log_file |
logical. If TRUE, information about data processing will be saved to a file. |
append |
logical. If TRUE, information about data processing will be added to an existing log file. |
verbose |
logical. If TRUE, information about data processing wil be printed to the console. |
log_file_path |
character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If 'append = TRUE', has to be a valid path to a file. |
a list of two data.tables named 'PTM' and 'PROTEIN' in the format required by MSstatsPTM.
head(spectronaut_input) head(spectronaut_annotation) msstats_input = SpectronauttoMSstatsPTMFormat(spectronaut_input, annotation=spectronaut_annotation, fasta_path=system.file("extdata", "spectronaut_fasta.fasta", package="MSstatsPTM"), use_unmod_peptides=TRUE, mod_id = "\\[Phospho \\(STY\\)\\]", fasta_protein_name = "uniprot_iso" ) head(msstats_input$PTM) head(msstats_input$PROTEIN)head(spectronaut_input) head(spectronaut_annotation) msstats_input = SpectronauttoMSstatsPTMFormat(spectronaut_input, annotation=spectronaut_annotation, fasta_path=system.file("extdata", "spectronaut_fasta.fasta", package="MSstatsPTM"), use_unmod_peptides=TRUE, mod_id = "\\[Phospho \\(STY\\)\\]", fasta_protein_name = "uniprot_iso" ) head(msstats_input$PTM) head(msstats_input$PROTEIN)
It is made from raw.input.
It is the output of dataSummarizationPTM function from MSstatsPTM.
It should include a list with two names PTM and PROTEIN. Each
of these list values is also a list with two names ProteinLevelData
and FeatureLevelData, which correspond to two data.tables.The columns
in these two data.tables are listed below. The variables are as follows:
FeatureLevelData :
PROTEIN : Protein ID with modification site mapped in. Ex. Protein_1002_S836
PEPTIDE : Full peptide with charge
TRANSITION: Charge
FEATURE : Combination of Protien, Peptide, and Transition Columns
LABEL :
GROUP : Condition (ex. Healthy, Cancer, Time0)
RUN : Unique ID for technical replicate of one TMT mixture.
SUBJECT : Unique ID for biological subject.
FRACTION : Unique Fraction ID
originalRUN : Run name
censored :
INTENSITY : Unique ID for TMT mixture.
ABUNDANCE : Unique ID for TMT mixture.
newABUNDANCE : Unique ID for TMT mixture.
predicted : Unique ID for TMT mixture.
ProteinLevelData :
RUN : MS run ID
Protein : Protein ID with modification site mapped in. Ex. Protein_1002_S836
LogIntensities: Protein-level summarized abundance
originalRUN : Labeling information (126, ... 131)
GROUP : Condition (ex. Healthy, Cancer, Time0)
SUBJECT : Unique ID for biological subject.
TotalGroupMeasurements : Unique ID for technical replicate of one TMT mixture.
NumMeasuredFeature : Unique ID for TMT mixture.
MissingPercentage : Unique ID for TMT mixture.
more50missing : Unique ID for TMT mixture.
NumImputedFeature : Unique ID for TMT mixture.
summary.datasummary.data
A list of two lists with four data.tables.
head(summary.data)head(summary.data)
It is made from raw.input.tmt.
It is the output of dataSummarizationPTM_TMT function from MSstatsPTM.
It should include a list with two names PTM and PROTEIN. Each
of these list values is also a list with two names ProteinLevelData
and FeatureLevelData, which correspond to two data.tables.The columns
in these two data.tables are listed below. The variables are as follows:
FeatureLevelData :
ProteinName : MS run ID
PSM : Protein ID with modification site mapped in. Ex. Protein_1002_S836
censored: Protein-level summarized abundance
predicted : Labeling information (126, ... 131)
log2Intensity : Condition (ex. Healthy, Cancer, Time0)
Run : Unique ID for biological subject.
Channel : Unique ID for technical replicate of one TMT mixture.
BioReplicate : Unique ID for TMT mixture.
Condition : Unique ID for TMT mixture.
Mixture : Unique ID for TMT mixture.
TechRepMixture : Unique ID for TMT mixture.
PeptideSequence : Unique ID for TMT mixture.
Charge : Unique ID for TMT mixture.
ProteinLevelData :
Mixture : MS run ID
TechRepMixture : Protein ID with modification site mapped in. Ex. Protein_1002_S836
Run: Protein-level summarized abundance
Channel : Labeling information (126, ... 131)
Protein : Condition (ex. Healthy, Cancer, Time0)
Abundance : Unique ID for biological subject.
BioReplicate : Unique ID for technical replicate of one TMT mixture.
Condition : Unique ID for TMT mixture.
summary.data.tmtsummary.data.tmt
A list of two lists with four data.tables.
head(summary.data.tmt)head(summary.data.tmt)
tidyFasta reads and tidys FASTA file. Use this function as the first
step in identifying modification sites.
tidyFasta(path)tidyFasta(path)
path |
A string of path to a FASTA file. |
A data.table with columns named header, sequence,
uniprot_ac, uniprot_iso, entry_name.
tidyFasta(system.file("extdata", "O13297.fasta", package="MSstatsPTM"))tidyFasta(system.file("extdata", "O13297.fasta", package="MSstatsPTM"))