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In devonjkohler/MSstatsTMTPTM: Post Translational Modification (PTM) Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling
#' Example of input PTM dataset for TMT experiments.
#'
#' It can be the output of PDtoMSstatsTMTFormat or other MSstatsTMT converter
#' functions.
#' It includes peak intensities for a variety of PTMs.
#' The variables are as follows:
#'
#' \itemize{
#' \item ProteinName : Name of protein with modification site mapped in with
#' an underscore. ie "Protein_4_Y474"
#' \item PeptideSequence
#' \item Charge
#' \item PSM
#' \item 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.
#' \item Intensity
#' }
#'
#' @format A data frame with 24704 rows and 11 variables.
#' @examples
#' head(raw.ptm)
#'
"raw.ptm"
#' Example of input Protein dataset for TMT experiments.
#'
#' It can be the output of PDtoMSstatsTMTFormat or other MSstatsTMT converter
#' functions.
#' It includes peak intensities for a variety of PTMs.
#' This is the companion file to the raw.ptm dataset, includes unmodified
#' protein data.
#' The variables are as follows:
#'
#' \itemize{
#' \item ProteinName : Name of protein
#' \item PeptideSequence
#' \item Charge
#' \item PSM
#' \item 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.
#' \item Intensity
#' }
#'
#' @format A data frame with 620476 rows and 11 variables.
#' @examples
#' head(raw.protein)
#'
"raw.protein"
#' Example of output from proteinSummarizaiton function for PTM data
#'
#' It is made from \code{\link{raw.ptm}}.
#' It is the output of proteinSummarization function from MSstatsTMT.
#' It should include the required columns as below.
#' The variables are as follows:
#'
#' \itemize{
#' \item Run : MS run ID
#' \item Protein : Protein ID with modification site mapped in. Ex.
#' Protein_1002_S836
#' \item Abundance: Protein-level summarized abundance
#' \item Channel : Labeling information (126, ... 131)
#' \item Condition : Condition (ex. Healthy, Cancer, Time0)
#' \item BioReplicate : Unique ID for biological subject.
#' \item TechRepMixture : Unique ID for technical replicate of one TMT
#' mixture.
#' \item Mixture : Unique ID for TMT mixture.
#' }
#'
#' @format A data frame with 19205 rows and 8 variables.
#' @examples
#' head(quant.msstats.ptm)
#'
"quant.msstats.ptm"
#' Example of output from proteinSummarizaiton function for Protein data
#'
#' It is made from \code{\link{raw.protein}}.
#' It is the output of proteinSummarization function from MSstatsTMT.
#' It should include the required columns as below.
#' The variables are as follows:
#'
#' \itemize{
#' \item Run : MS run ID
#' \item Protein : Protein ID
#' \item Abundance: Protein-level summarized abundance
#' \item Channel : Labeling information (126, ... 131)
#' \item Condition : Condition (ex. Healthy, Cancer, Time0)
#' \item BioReplicate : Unique ID for biological subject.
#' \item TechRepMixture : Unique ID for technical replicate of one TMT
#' mixture.
#' \item Mixture : Unique ID for TMT mixture.
#' }
#'
#' @format A data frame with 93258 rows and 8 variables.
#' @examples
#' head(quant.msstats.protein)
#'
"quant.msstats.protein"
#' Example contrast matrix for input into the groupComparisonTMTPTM function
#'
#' Manually specified comparisons of interest for contrast.matrix
#' arguement of groupComparisonTMTPTM.
#'
#' \itemize{
#' \item Condition_1, ... Condition_6 : Column names are conditions in dataset
#' \item 1-4, ... 5-6 : Row names are comparisons of interest
#' }
#'
#' @format A data frame with 9 rows and 6 variables.
#' @examples
#' head(example.contrast.matrix)
#'
"example.contrast.matrix"
#' Ouput of groupComparisonTMTPTM for full pairwise test
#'
#' Returns the a list with three dataframes for three
#' statistical models. One for each Protein, PTM,
#' and PTM adjusted for protein level.
#'
#' \itemize{
#' \item List objects: PTM.Model, Protein.Model, Adjusted.Model (all
#' \code{dataframe}). Columns as follows:
#' \item Protein : Protein ID
#' \item Label: Label of the pairwise comparision or contrast
#' \item log2FC: Log2 fold change
#' \item SE: Standard error of the comparsion of contrast results
#' \item DF: Degree of freedom
#' \item pvalue: Value of p statistic of the test
#' \item adj.pvalue: adjusted p value
#' \item issue: used for indicating the reason why a comparison is not
#' testable. NA means the comparison is testable.
#' 'oneConditionMissing' means the protein has no measurements in one
#' conndition of the comparison.
#' Furtherone, when 'issue = oneConditionMissing', 'log2FC = Inf' means the
#' negative condition
#' (with coefficient -1 in the Label column) is missing and 'log2FC = -Inf'
#' means
#' the positive condition (with coefficient 1 in the Label column) is
#' missing.
#' completeMissing' means the protein has no measurements in all the
#' connditions of the comparison.
#' unfittableModel' means there is no enough measurements to fit the linear
#' model.
#' In other words, each condition has only one measurement.
#' }
#'
#' @format A list of three dataframes
#' @examples
#' names(model.results.pairwise)
#' head(model.results.pairwise[[1]])
#'
"model.results.pairwise"
#' Ouput of groupComparisonTMTPTM for specific comparisons of interest
#'
#' Returns the a list with three dataframes for three
#' statistical models. One for each Protein, PTM,
#' and PTM adjusted for protein level.
#'
#' \itemize{
#' \item List objects: PTM.Model, Protein.Model, Adjusted.Model
#' (all \code{dataframe}). Columns as follows:
#' \item Protein : Protein ID
#' \item Label: Label of the pairwise comparision or contrast
#' \item log2FC: Log2 fold change
#' \item SE: Standard error of the comparsion of contrast results
#' \item DF: Degree of freedom
#' \item pvalue: Value of p statistic of the test
#' \item adj.pvalue: adjusted p value
#' \item issue: used for indicating the reason why a comparison is not
#' testable. NA means the comparison is testable.
#' 'oneConditionMissing' means the protein has no measurements in
#' one conndition of the comparison.
#' Furtherone, when 'issue = oneConditionMissing', 'log2FC = Inf'
#' means the negative condition
#' (with coefficient -1 in the Label column) is missing and
#' 'log2FC = -Inf' means
#' the positive condition (with coefficient 1 in the Label column)
#' is missing.
#' completeMissing' means the protein has no measurements in all the
#' connditions of the comparison.
#' unfittableModel' means there is no enough measurements to fit the
#' linear model.
#' In other words, each condition has only one measurement.
#' }
#'
#' @format A list of three dataframes
#' @examples
#' names(model.results.contrast)
#' head(model.results.contrast[[1]])
#'
"model.results.contrast"
devonjkohler/MSstatsTMTPTM documentation built on Oct. 27, 2021, 7:50 a.m.
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#' Example of input PTM dataset for TMT experiments.
#'
#' It can be the output of PDtoMSstatsTMTFormat or other MSstatsTMT converter
#' functions.
#' It includes peak intensities for a variety of PTMs.
#' The variables are as follows:
#'
#' \itemize{
#' \item ProteinName : Name of protein with modification site mapped in with
#' an underscore. ie "Protein_4_Y474"
#' \item PeptideSequence
#' \item Charge
#' \item PSM
#' \item 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.
#' \item Intensity
#' }
#'
#' @format A data frame with 24704 rows and 11 variables.
#' @examples
#' head(raw.ptm)
#'
"raw.ptm"
#' Example of input Protein dataset for TMT experiments.
#'
#' It can be the output of PDtoMSstatsTMTFormat or other MSstatsTMT converter
#' functions.
#' It includes peak intensities for a variety of PTMs.
#' This is the companion file to the raw.ptm dataset, includes unmodified
#' protein data.
#' The variables are as follows:
#'
#' \itemize{
#' \item ProteinName : Name of protein
#' \item PeptideSequence
#' \item Charge
#' \item PSM
#' \item 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.
#' \item Intensity
#' }
#'
#' @format A data frame with 620476 rows and 11 variables.
#' @examples
#' head(raw.protein)
#'
"raw.protein"
#' Example of output from proteinSummarizaiton function for PTM data
#'
#' It is made from \code{\link{raw.ptm}}.
#' It is the output of proteinSummarization function from MSstatsTMT.
#' It should include the required columns as below.
#' The variables are as follows:
#'
#' \itemize{
#' \item Run : MS run ID
#' \item Protein : Protein ID with modification site mapped in. Ex.
#' Protein_1002_S836
#' \item Abundance: Protein-level summarized abundance
#' \item Channel : Labeling information (126, ... 131)
#' \item Condition : Condition (ex. Healthy, Cancer, Time0)
#' \item BioReplicate : Unique ID for biological subject.
#' \item TechRepMixture : Unique ID for technical replicate of one TMT
#' mixture.
#' \item Mixture : Unique ID for TMT mixture.
#' }
#'
#' @format A data frame with 19205 rows and 8 variables.
#' @examples
#' head(quant.msstats.ptm)
#'
"quant.msstats.ptm"
#' Example of output from proteinSummarizaiton function for Protein data
#'
#' It is made from \code{\link{raw.protein}}.
#' It is the output of proteinSummarization function from MSstatsTMT.
#' It should include the required columns as below.
#' The variables are as follows:
#'
#' \itemize{
#' \item Run : MS run ID
#' \item Protein : Protein ID
#' \item Abundance: Protein-level summarized abundance
#' \item Channel : Labeling information (126, ... 131)
#' \item Condition : Condition (ex. Healthy, Cancer, Time0)
#' \item BioReplicate : Unique ID for biological subject.
#' \item TechRepMixture : Unique ID for technical replicate of one TMT
#' mixture.
#' \item Mixture : Unique ID for TMT mixture.
#' }
#'
#' @format A data frame with 93258 rows and 8 variables.
#' @examples
#' head(quant.msstats.protein)
#'
"quant.msstats.protein"
#' Example contrast matrix for input into the groupComparisonTMTPTM function
#'
#' Manually specified comparisons of interest for contrast.matrix
#' arguement of groupComparisonTMTPTM.
#'
#' \itemize{
#' \item Condition_1, ... Condition_6 : Column names are conditions in dataset
#' \item 1-4, ... 5-6 : Row names are comparisons of interest
#' }
#'
#' @format A data frame with 9 rows and 6 variables.
#' @examples
#' head(example.contrast.matrix)
#'
"example.contrast.matrix"
#' Ouput of groupComparisonTMTPTM for full pairwise test
#'
#' Returns the a list with three dataframes for three
#' statistical models. One for each Protein, PTM,
#' and PTM adjusted for protein level.
#'
#' \itemize{
#' \item List objects: PTM.Model, Protein.Model, Adjusted.Model (all
#' \code{dataframe}). Columns as follows:
#' \item Protein : Protein ID
#' \item Label: Label of the pairwise comparision or contrast
#' \item log2FC: Log2 fold change
#' \item SE: Standard error of the comparsion of contrast results
#' \item DF: Degree of freedom
#' \item pvalue: Value of p statistic of the test
#' \item adj.pvalue: adjusted p value
#' \item issue: used for indicating the reason why a comparison is not
#' testable. NA means the comparison is testable.
#' 'oneConditionMissing' means the protein has no measurements in one
#' conndition of the comparison.
#' Furtherone, when 'issue = oneConditionMissing', 'log2FC = Inf' means the
#' negative condition
#' (with coefficient -1 in the Label column) is missing and 'log2FC = -Inf'
#' means
#' the positive condition (with coefficient 1 in the Label column) is
#' missing.
#' completeMissing' means the protein has no measurements in all the
#' connditions of the comparison.
#' unfittableModel' means there is no enough measurements to fit the linear
#' model.
#' In other words, each condition has only one measurement.
#' }
#'
#' @format A list of three dataframes
#' @examples
#' names(model.results.pairwise)
#' head(model.results.pairwise[[1]])
#'
"model.results.pairwise"
#' Ouput of groupComparisonTMTPTM for specific comparisons of interest
#'
#' Returns the a list with three dataframes for three
#' statistical models. One for each Protein, PTM,
#' and PTM adjusted for protein level.
#'
#' \itemize{
#' \item List objects: PTM.Model, Protein.Model, Adjusted.Model
#' (all \code{dataframe}). Columns as follows:
#' \item Protein : Protein ID
#' \item Label: Label of the pairwise comparision or contrast
#' \item log2FC: Log2 fold change
#' \item SE: Standard error of the comparsion of contrast results
#' \item DF: Degree of freedom
#' \item pvalue: Value of p statistic of the test
#' \item adj.pvalue: adjusted p value
#' \item issue: used for indicating the reason why a comparison is not
#' testable. NA means the comparison is testable.
#' 'oneConditionMissing' means the protein has no measurements in
#' one conndition of the comparison.
#' Furtherone, when 'issue = oneConditionMissing', 'log2FC = Inf'
#' means the negative condition
#' (with coefficient -1 in the Label column) is missing and
#' 'log2FC = -Inf' means
#' the positive condition (with coefficient 1 in the Label column)
#' is missing.
#' completeMissing' means the protein has no measurements in all the
#' connditions of the comparison.
#' unfittableModel' means there is no enough measurements to fit the
#' linear model.
#' In other words, each condition has only one measurement.
#' }
#'
#' @format A list of three dataframes
#' @examples
#' names(model.results.contrast)
#' head(model.results.contrast[[1]])
#'
"model.results.contrast"
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