R/dataDoc.R

In 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"