| Title: | Complexome Profiling Analysis package |
|---|---|
| Description: | This package is for analysis of SILAC labeled complexome profiling data. It uses peptide table in tab-delimited format as an input and produces ready-to-use tables and plots. |
| Authors: | Rick Scavetta [aut],
Petra Palenikova [aut, cre] |
| Maintainer: | Petra Palenikova <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.13.0 |
| Built: | 2024-06-30 03:43:13 UTC |
| Source: | https://github.com/bioc/ComPrAn |
This function creates a plot of all peptides that belong to a single protein
allPeptidesPlot( .listDF, protein, max_frac, meanLine = FALSE, repPepLine = FALSE, separateLabStates = FALSE, grid = TRUE, titleLabel = "all", titleAlign = "left", ylabel = "Precursor Area", xlabel = "Fraction", legendLabel = "Condition", labelled = "Labeled", unlabelled = "Unlabeled", controlSample = "", textSize = 12, axisTextSize = 8 )allPeptidesPlot( .listDF, protein, max_frac, meanLine = FALSE, repPepLine = FALSE, separateLabStates = FALSE, grid = TRUE, titleLabel = "all", titleAlign = "left", ylabel = "Precursor Area", xlabel = "Fraction", legendLabel = "Condition", labelled = "Labeled", unlabelled = "Unlabeled", controlSample = "", textSize = 12, axisTextSize = 8 )
.listDF |
list, list containing data frames of peptides for each protein indexed by 'Protein Group Accessions' |
protein |
character, 'Protein Group Accession' to show in the plot |
max_frac |
numeric, total number of fractions |
meanLine |
logical, specifies whether to plot a mean line |
repPepLine |
logical, specifies whether to plot a representative peptide line |
separateLabStates |
logical, specifies whether label states will be separated into facets |
grid |
logical, specifies presence/absence of gridline in the plot |
titleLabel |
character, what to call the plot |
titleAlign |
character, one of the 'left', 'center'/'centre', 'right', specifies alignment of the title in plot |
ylabel |
character |
xlabel |
character |
legendLabel |
character |
labelled |
character, label to be used for isLabel == TRUE |
unlabelled |
character, label to be used for isLabel == FALSE |
controlSample |
character, either labelled or unlabelled, this setting will adjust coloring based on which sample is a control |
textSize |
numeric, size of text in the plot |
axisTextSize |
numeric, size of axis labels in the plot |
plot
##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides) ## Pick representative peptide for each protein for both scenarios peptide_index <- pickPeptide(peptides) ##create a plot showing all peptides of selected protein protein <- "P52815" max_frac <- 23 #default plot allPeptidesPlot(peptide_index,protein, max_frac = max_frac) #other plot version allPeptidesPlot(peptide_index,protein, max_frac = max_frac, repPepLine = TRUE, grid = FALSE, titleAlign = "center") #other plot version allPeptidesPlot(peptide_index,protein, max_frac = max_frac, repPepLine = TRUE, meanLine = TRUE, separateLabStates =TRUE, titleLabel = "GN")##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides) ## Pick representative peptide for each protein for both scenarios peptide_index <- pickPeptide(peptides) ##create a plot showing all peptides of selected protein protein <- "P52815" max_frac <- 23 #default plot allPeptidesPlot(peptide_index,protein, max_frac = max_frac) #other plot version allPeptidesPlot(peptide_index,protein, max_frac = max_frac, repPepLine = TRUE, grid = FALSE, titleAlign = "center") #other plot version allPeptidesPlot(peptide_index,protein, max_frac = max_frac, repPepLine = TRUE, meanLine = TRUE, separateLabStates =TRUE, titleLabel = "GN")
This function creates a data frame with column specifying clusters assigned ot each protein using the table and distance matrix produced by clusterComp() function.
assignClusters(.listDf, sample, method = "complete", cutoff = 0.5)assignClusters(.listDf, sample, method = "complete", cutoff = 0.5)
.listDf |
list of data frames produced by clusterComp() function |
sample |
which of the two samples you want to apply the function to (labeled/unlabeled). |
method |
character, One of 'average', 'single' or 'complete' (default), specifies the linkage method to be used inside R hclust() function |
cutoff |
numeric, specifies the h value in R cutree() function, height at which to 'cut the tree', everything with distance below this value is assigned into same cluster everything with larger distance is in a different cluster extreme possible values are 0 to 2 (might not be reached for all data sets) |
dataframe
##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) # create components necessary for clustering clusteringDF <- clusterComp(forAnalysis,scenar = "A", PearsCor = "centered") #assign clusters labTab_clust <- assignClusters(.listDf = clusteringDF,sample = "labeled", method = 'complete', cutoff = 0.5) unlabTab_clust <- assignClusters(.listDf = clusteringDF,sample = "unlabeled", method = 'complete', cutoff = 0.5)##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) # create components necessary for clustering clusteringDF <- clusterComp(forAnalysis,scenar = "A", PearsCor = "centered") #assign clusters labTab_clust <- assignClusters(.listDf = clusteringDF,sample = "labeled", method = 'complete', cutoff = 0.5) unlabTab_clust <- assignClusters(.listDf = clusteringDF,sample = "unlabeled", method = 'complete', cutoff = 0.5)
Perform initial, mandatory, cleaning of data Function to process raw input data into format required for subsequent analysis. .data is a data frame containing raw input data. This function checks (not neccessarily in this order):
renames Sequence ID column to Fraction and converts values in this column from letters to numbers
reorders Protein Group Accessions containing multiple proteins
removes rows in which PSM Ambiguity == 'Rejected'
removes rows in which # Protein Groups == 0
removes rows in which Precursor Area is NA
removes cols that are not used at all
cleanData(.data, fCol = "Search ID")cleanData(.data, fCol = "Search ID")
.data |
dataframe |
fCol |
character The column containing the fractions, e.g. "Search ID" (default) |
dataframe
Petra Palenikova [email protected]
Rick Scavetta [email protected]
##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID")##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID")
Reformat the table for the one neccessary for assignClusters function. Calculate the distance matirx using selected variant of correlation.
clusterComp(.df, scenar = "A", PearsCor = "centered")clusterComp(.df, scenar = "A", PearsCor = "centered")
.df |
data frame, table of normalised protein values |
scenar |
character, scenario intended for clustering, either "A" or "B" |
PearsCor |
character, pearsons correlation variant (centered/uncentered) |
list of data frames
##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) # create components necessary for clustering clusteringDF <- clusterComp(forAnalysis,scenar = "A", PearsCor = "centered")##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) # create components necessary for clustering clusteringDF <- clusterComp(forAnalysis,scenar = "A", PearsCor = "centered")
Execute the complexomics Shiny app
compranApp()compranApp()
Shiny app
#' @examples ##to start the shiny app associated with ComPrAn package run if(interactive()){ compranApp() }#' @examples ##to start the shiny app associated with ComPrAn package run if(interactive()){ compranApp() }
Covert clustered tables into format for export
exportClusterAssignments(labClustTable, unlabClustTable)exportClusterAssignments(labClustTable, unlabClustTable)
labClustTable |
output: data frame containing columns: 'Protein Group Accessions' character 'Protein Descriptions' character 'Cluster number - unlabeled' integer 'Cluster number - labeled' integer |
unlabClustTable |
labClustTable, unlabClustTable: data frames, contain columns: 'Protein Group Accessions' character 'Protein Descriptions' character isLabel character ('TRUE'/'FALSE') - here in one data frame all are TRUE in second one all are FALSE columns 1 to n, numeric, n is the total number of fractions/slices, each of this columns contains 'Precursor Area' values in a given fraction(columns) for a protein(rows) cluster integer |
dataframe
##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) # create components necessary for clustering clusteringDF <- clusterComp(forAnalysis,scenar = "A", PearsCor = "centered") #assign clusters labTab_clust <- assignClusters(.listDf = clusteringDF,sample = "labeled", method = 'complete', cutoff = 0.5) unlabTab_clust <- assignClusters(.listDf = clusteringDF,sample = "unlabeled", method = 'complete', cutoff = 0.5) #make table of cluster assginment tableClusterExport <- exportClusterAssignments(labTab_clust,unlabTab_clust)##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) # create components necessary for clustering clusteringDF <- clusterComp(forAnalysis,scenar = "A", PearsCor = "centered") #assign clusters labTab_clust <- assignClusters(.listDf = clusteringDF,sample = "labeled", method = 'complete', cutoff = 0.5) unlabTab_clust <- assignClusters(.listDf = clusteringDF,sample = "unlabeled", method = 'complete', cutoff = 0.5) #make table of cluster assginment tableClusterExport <- exportClusterAssignments(labTab_clust,unlabTab_clust)
Incomplete labelling - there are cases when in peptides containing multiple Lys/Arg not all of them are heavy in labelled samples. As in SILAC we assume that addition of label does not affect peptide properties, we are taking a mean 'Precursor Area' value as the representative 'Precursor Area' in such cases.
extractRepPeps(.data, scenario, label = "Label neccessary for scenario A")extractRepPeps(.data, scenario, label = "Label neccessary for scenario A")
.data |
dataframe containing all peptides of one protein |
scenario |
character "A", or "B" |
label |
character, selects for which label state the representative peptides will be exported, can have value of "TRUE" or "FALSE", required only for scenario "A" |
dataframe containing only representative peptide
Extracts values for representative peptides for each protein, for both scenario A and scenario B. Results are combined into one data frame in a format either indended for further analysis or for export.
getNormTable(.listDf, purpose = "analysis")getNormTable(.listDf, purpose = "analysis")
.listDf |
list of data frames |
purpose |
character, purpose of use of function output, values either "analysis" of "export" |
dataframe
##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides) ## Pick representative peptide for each protein for both scenarios peptide_index <- pickPeptide(peptides) ## extract table with normalised protein values for both scenarios forAnalysis <- getNormTable(peptide_index,purpose = "analysis")##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides) ## Pick representative peptide for each protein for both scenarios peptide_index <- pickPeptide(peptides) ## extract table with normalised protein values for both scenarios forAnalysis <- getNormTable(peptide_index,purpose = "analysis")
This function creates a heatmap for a subset of proteins in dataFrame specified in groupData, heatmap is divided into facets according to isLabel
groupHeatMap( dataFrame, groupData, groupName, titleAlign = "left", newNamesCol = NULL, colNumber = 2, ylabel = "Protein", xlabel = "Fraction", legendLabel = "Relative Protein Abundance", legendPosition = "right", grid = TRUE, labelled = "labeled", unlabelled = "unlabeled", orderColumn = NULL )groupHeatMap( dataFrame, groupData, groupName, titleAlign = "left", newNamesCol = NULL, colNumber = 2, ylabel = "Protein", xlabel = "Fraction", legendLabel = "Relative Protein Abundance", legendPosition = "right", grid = TRUE, labelled = "labeled", unlabelled = "unlabeled", orderColumn = NULL )
dataFrame |
data frame, contains columns: 'Protein Group Accessions' character 'Protein Descriptions' character Fraction integer isLabel character ('TRUE'/'FALSE' values) 'Precursor Area' double scenario character |
groupData |
data frame, mandatory column: 'Protein Group Accessions' character - this column is used for filtering optional columns: any other column of type character that should be used for renaming |
groupName |
character, name that should be used for the group specified in groupData |
titleAlign |
character, one of the 'left', 'center'/'centre', 'right', specifies alignment of the title in plot |
newNamesCol |
character, if groupData contains column for re-naming and you want to use it, specify the column name in here |
colNumber |
numeric, values of 1 or 2, specifies whether facets will be shown side-by-side or above each other |
ylabel |
character |
xlabel |
character |
legendLabel |
character |
legendPosition |
character, one of "right" or "bottom" |
grid |
logical, specifies presence/absence of gridline in the plot |
labelled |
character, label to be used for isLabel == TRUE |
unlabelled |
character, label to be used for isLabel == FALSE |
orderColumn |
character, if groupData contains column for re-ordering and you want to use it, specify the column name in here |
plot
##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) ##example plot: groupDfn <- system.file("extData", "exampleGroup.txt", package = "ComPrAn") groupName <- 'group1' groupData <- data.table::fread(groupDfn) groupHeatMap(forAnalysis[forAnalysis$scenario == "B",], groupData, groupName)##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) ##example plot: groupDfn <- system.file("extData", "exampleGroup.txt", package = "ComPrAn") groupName <- 'group1' groupData <- data.table::fread(groupDfn) groupHeatMap(forAnalysis[forAnalysis$scenario == "B",], groupData, groupName)
Title
makeBarPlotClusterSummary(df, name = "sample 1")makeBarPlotClusterSummary(df, name = "sample 1")
df |
data frame, contains columns: 'Protein Group Accessions' character 'Protein Descriptions' character isLabel character ('TRUE'/'FALSE') columns 1 to n, numeric, n is the total number of fractions/slices, each of this columns contains 'Precursor Area' values in a given fraction(columns) for a protein(rows) cluster integer |
name |
character, specifies the name of the sample |
plot
##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) # create components necessary for clustering clusteringDF <- clusterComp(forAnalysis,scenar = "A", PearsCor = "centered") #assign clusters labTab_clust <- assignClusters(.listDf = clusteringDF,sample = "labeled", method = 'complete', cutoff = 0.5) unlabTab_clust <- assignClusters(.listDf = clusteringDF,sample = "unlabeled", method = 'complete', cutoff = 0.5) #Make bar plots for labeled and unlabeled samples makeBarPlotClusterSummary(labTab_clust, name = 'labeled') makeBarPlotClusterSummary(unlabTab_clust, name = 'unlabeled')##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) # create components necessary for clustering clusteringDF <- clusterComp(forAnalysis,scenar = "A", PearsCor = "centered") #assign clusters labTab_clust <- assignClusters(.listDf = clusteringDF,sample = "labeled", method = 'complete', cutoff = 0.5) unlabTab_clust <- assignClusters(.listDf = clusteringDF,sample = "unlabeled", method = 'complete', cutoff = 0.5) #Make bar plots for labeled and unlabeled samples makeBarPlotClusterSummary(labTab_clust, name = 'labeled') makeBarPlotClusterSummary(unlabTab_clust, name = 'unlabeled')
This function calculates distance matrix for a data frame, column by column requires uncenteredCor function to work
makeDist(df, centered = FALSE)makeDist(df, centered = FALSE)
df |
data frame, contains columns: 'Protein Group Accessions' character 'Protein Descriptions' character isLabel character ('TRUE'/'FALSE') columns 1 to n, numeric, n is the total number of fractions/slices, each of this columns contains 'Precursor Area' values in a given fraction(columns) for a protein(rows) |
centered |
centered: logical,if TRUE return dist matrix based on centered Pearson correlation (uses R cor() function, fast) ,if FALSE return dist matrix based on uncentered Pearson correlation (uses custom uncenteredCor() function, slow) |
matrix
Convert the dataframe as output from extractRepPeps() to matrix-like table return normalized or raw values of Precursor Area, by default return normalized values
normalizeTable(.data, applyNormalization = TRUE)normalizeTable(.data, applyNormalization = TRUE)
.data |
a dataframe |
applyNormalization |
logical apply normalization or not |
a matrix
This is a convenient function for plotting
normTableForExport(labTab, unlabTab, comboTab)normTableForExport(labTab, unlabTab, comboTab)
labTab |
a dataframe |
unlabTab |
a dataframe |
comboTab |
a dataframe |
a dataframe
This is a convenient function for plotting
normTableWideToLong(labTab, unlabTab, comboTab)normTableWideToLong(labTab, unlabTab, comboTab)
labTab |
a dataframe |
unlabTab |
a dataframe |
comboTab |
a dataframe |
a dataframe
This function creates a ?scatter plot? for a subset of proteins in dataFrame specified in groupData. Intended use of the function - using scenario A data, compare shape of the migration profile for a SINGLE GROUP of proteins BETWEEN the two LABEL STATES.
oneGroupTwoLabelsCoMigration( dataFrame, max_frac, groupData = NULL, groupName = "group1", meanLine = FALSE, medianLine = FALSE, ylabel = "Relative Protein Abundance", xlabel = "Fraction", legendLabel = "Condition", labelled = "Labeled", unlabelled = "Unlabeled", jitterPoints = 0.3, pointSize = 2.5, grid = FALSE, titleAlign = "left", alphaValue = 1, controlSample = "", textSize = 12, axisTextSize = 8 )oneGroupTwoLabelsCoMigration( dataFrame, max_frac, groupData = NULL, groupName = "group1", meanLine = FALSE, medianLine = FALSE, ylabel = "Relative Protein Abundance", xlabel = "Fraction", legendLabel = "Condition", labelled = "Labeled", unlabelled = "Unlabeled", jitterPoints = 0.3, pointSize = 2.5, grid = FALSE, titleAlign = "left", alphaValue = 1, controlSample = "", textSize = 12, axisTextSize = 8 )
dataFrame |
dataFrame: data frame, data frame of normalised values for proteins from SCENARIO A, contains columns: 'Protein Group Accessions' character 'Protein Descriptions' character Fraction integer isLabel character ('TRUE'/'FALSE' values) 'Precursor Area' double scenario character |
max_frac |
numeric, total number of fractions |
groupData |
character vector, contins list of Protein Group Accessions that belong to the group we want to plot |
groupName |
character, name that should be used for the group specified in groupData |
meanLine |
logical, specifies whether to plot a mean line for all values in the group |
medianLine |
logical, specifies whether to plot a median line for all values in the group |
ylabel |
character |
xlabel |
character |
legendLabel |
character |
labelled |
character, label to be used for isLabel == TRUE |
unlabelled |
character, label to be used for isLabel == FALSE |
jitterPoints |
numeric |
pointSize |
numeric, size of the point in the plot |
grid |
logical, specifies presence/absence of gridline in the plot |
titleAlign |
character, one of the 'left', 'center'/'centre', 'right', specifies alignment of the title in plot |
alphaValue |
numeric, transparency of the point, values 0 to 1 |
controlSample |
character, either labelled or unlabelled, this setting will adjust plot coloring based on which sample is a control |
textSize |
numeric, size of text in the plot |
axisTextSize |
numeric, size of axis labels in the plot |
plot
##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) ##example plot: groupDV <- c("Q16540","P52815","P09001","Q13405","Q9H2W6") groupName <- 'group1' max_frac <- 23 oneGroupTwoLabelsCoMigration(forAnalysis, max_frac, groupDV,groupName)##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) ##example plot: groupDV <- c("Q16540","P52815","P09001","Q13405","Q9H2W6") groupName <- 'group1' max_frac <- 23 oneGroupTwoLabelsCoMigration(forAnalysis, max_frac, groupDV,groupName)
This function returns NAMES of proteins present in only labelled/only unlabelld or both label states
onlyInOneLabelState(.data)onlyInOneLabelState(.data)
.data |
An environment containing dataframes |
a list with 3 items, each item is a vector containing names belonging to one of 3 groups
##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides) ## Pick representative peptide for each protein for both scenarios peptide_index <- pickPeptide(peptides) ## extract list of names of proteins present in one/both samples oneStateList <- onlyInOneLabelState(peptide_index)##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides) ## Pick representative peptide for each protein for both scenarios peptide_index <- pickPeptide(peptides) ## extract list of names of proteins present in one/both samples oneStateList <- onlyInOneLabelState(peptide_index)
Check presence of required columns inputFile is a character vector containing the location of peptide file This function checks:
are all required columns present
are these columns in correct format
peptideImport(inputFile)peptideImport(inputFile)
inputFile |
character |
dataframe
Petra Palenikova [email protected]
Rick Scavetta [email protected]
##Use example peptide data set, read in data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile)##Use example peptide data set, read in data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile)
This function selects a single unique peptide to represent each 'Protein Group Accession' There are 2 ways of selecting peptides, both are perform as they are needed for different tasks later on.
Scenario A: select peptide occuring in most fractions, do this individually for labelled/unlabelled (max value for any peptide is equal to number of fractions) in case of ties, pick peptide whith highest 'Precursor Area' in any fraction.
Scenario B: select peptide occuring in most fractions counting both label states together (max value for any peptide is equal to twice the number of fractions) in case of ties, pick peptide with highest 'Precursor Area' in any fraction. Representative peptide in Scenario B is picked only for proteins that have shared peptide between label states.
pickPeptide(.data)pickPeptide(.data)
.data |
a dataframe |
list of data frames
##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides) ## Pick representative peptide for each protein for both scenarios peptide_index <- pickPeptide(peptides)##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides) ## Pick representative peptide for each protein for both scenarios peptide_index <- pickPeptide(peptides)
This function creates a line plot for a proteins in dataFrame specified by protein
proteinPlot( dataFrame, protein, max_frac, grid = TRUE, titleLabel = "all", titleAlign = "left", ylabel = "Relative Protein Abundance", xlabel = "Fraction", legendLabel = "Condition", labelled = "Labeled", unlabelled = "Unlabeled", controlSample = "", textSize = 12, axisTextSize = 8 )proteinPlot( dataFrame, protein, max_frac, grid = TRUE, titleLabel = "all", titleAlign = "left", ylabel = "Relative Protein Abundance", xlabel = "Fraction", legendLabel = "Condition", labelled = "Labeled", unlabelled = "Unlabeled", controlSample = "", textSize = 12, axisTextSize = 8 )
dataFrame |
data frame, contains columns: 'Protein Group Accessions' character; 'Protein Descriptions' character;bFraction integer; isLabel character ("TRUE"/"FALSE" values);'Precursor Area' double; scenario character |
protein |
character the protein of interest |
max_frac |
integer total number of fractions |
grid |
logical specifies presence/absence of gridline in the plot |
titleLabel |
character, if it is 'all' or 'GN', it specifies whether to show full label or just the gene name, if any other character is used, the value of titleLabel will be used as plot title |
titleAlign |
character one of the 'left', 'center'/'centre', 'right', specifies alignment of the title in plot |
ylabel |
character |
xlabel |
character |
legendLabel |
character |
labelled |
character label to be used for isLabel == TRUE |
unlabelled |
character label to be used for isLabel == FALSE |
controlSample |
character, either labelled or unlabelled, this setting will adjust plot coloring based on which sample is a control |
textSize |
numeric, size of text in the plot |
axisTextSize |
numeric, size of axis labels in the plot |
a plot
##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) ##example plot: protein <- "P52815" max_frac <- 23 proteinPlot(forAnalysis, protein, max_frac)##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) ##example plot: protein <- "P52815" max_frac <- 23 proteinPlot(forAnalysis, protein, max_frac)
This function converts imported protien table into format compatible with downstream analysis Imported file needs to contain following columns:
"Protein Group Accessions" - character/factor
"Protein Descriptions" - character
"scenario" - character/factor
"label" - logical
columns "1" to "n" - numeric
protImportForAnalysis(inputFile)protImportForAnalysis(inputFile)
inputFile |
- character vector containing the location of protein file |
data frame
##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile)##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package = "ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile)
For rows: Keep only one row with highest Precursor Area in cases where for a single Protein Group Accession in a single fraction there are multiple rows with the same combination of Sequence, Mods and Charge
For cols: remove columns that are not neccessary any more
simplifyProteins(.data, direction = c("rows", "cols"))simplifyProteins(.data, direction = c("rows", "cols"))
.data |
a dataframe |
direction |
character, rows, cols or both |
a dataframe
##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides)##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides) ## remove unneccessary columns, simplify rows peptides <- simplifyProteins(peptides)
Splits up the Modifications column into lists of vectors for modifications(Mods) and labels(Labels) It adds two more columns to the data frame:
UniqueCombinedID_A: Unique combinations of Sequence, Mods and Charge for "scenario A".
UniqueCombinedID_B: Unique combinations of Sequence, Mods, Charge and Labels for "scenario B"
splitModLab(.data)splitModLab(.data)
.data |
dataframe |
dataframe
##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides)##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ## separate chemical modifications and labelling into separate columns peptides <- splitModLab(peptides)
Filters only rows with specified values in columns Rank and Confidence Level , specified as cl
toFilter(.data, rank = 1, cl = c("Low", "Middle", "High"))toFilter(.data, rank = 1, cl = c("Low", "Middle", "High"))
.data |
dataframe |
rank |
integer |
cl |
charater any combination of one or more of 'Low', 'Middle', or 'High' |
a dataframe
##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ##optional filtering based on rank and confidence level peptides <- toFilter(peptides, rank = 1)##Use example peptide data set, read in and clean data inputFile <- system.file("extData", "data.txt", package = "ComPrAn") peptides <- peptideImport(inputFile) peptides <- cleanData(peptides, fCol = "Search ID") ##optional filtering based on rank and confidence level peptides <- toFilter(peptides, rank = 1)
This function creates a scatter plot for a subset of proteins in dataFrame specified in group1Data and group2Data, label states are always separated into facets
twoGroupsWithinLabelCoMigration( dataFrame, max_frac, group1Data = NULL, group1Name = "group1", group2Data = NULL, group2Name = "group2", meanLine = FALSE, medianLine = FALSE, ylabel = "Relative Protein Abundance", xlabel = "Fraction", legendLabel = "Group", labelled = "Labeled", unlabelled = "Unlabeled", jitterPoints = 0.3, pointSize = 2.5, grid = FALSE, showTitle = FALSE, titleAlign = "left", alphaValue = 1, textSize = 12, axisTextSize = 8 )twoGroupsWithinLabelCoMigration( dataFrame, max_frac, group1Data = NULL, group1Name = "group1", group2Data = NULL, group2Name = "group2", meanLine = FALSE, medianLine = FALSE, ylabel = "Relative Protein Abundance", xlabel = "Fraction", legendLabel = "Group", labelled = "Labeled", unlabelled = "Unlabeled", jitterPoints = 0.3, pointSize = 2.5, grid = FALSE, showTitle = FALSE, titleAlign = "left", alphaValue = 1, textSize = 12, axisTextSize = 8 )
dataFrame |
dataFrame: data frame, data frame of normalised values for proteins from SCENARIO A, contains columns: 'Protein Group Accessions' character 'Protein Descriptions' character Fraction integer isLabel character ('TRUE'/'FALSE' values) 'Precursor Area' double scenario character |
max_frac |
numeric, total number of fractions |
group1Data |
character vector, contins list of Protein Group Accessions that belong to the group we want to plot for group 1 |
group1Name |
character, name that should be used for the group specified in group1Data |
group2Data |
character vector, contins list of Protein Group Accessions that belong to the group we want to plot for group 2 |
group2Name |
character, name that should be used for the group specified in group2Data |
meanLine |
logical, specifies whether to plot a mean line for all values in the group |
medianLine |
logical, specifies whether to plot a median line for all values in the group |
ylabel |
character |
xlabel |
character |
legendLabel |
character |
labelled |
character, label to be used for isLabel == TRUE |
unlabelled |
character, label to be used for isLabel == FALSE |
jitterPoints |
numeric |
pointSize |
numeric, size of the point in the plot |
grid |
logical, specifies presence/absence of gridline in the plot |
showTitle |
logical |
titleAlign |
character, one of the 'left', 'center'/'centre', 'right', specifies alignment of the title in plot |
alphaValue |
numeric, transparency of the point, values 0 to 1 |
textSize |
numeric, size of text in the plot |
axisTextSize |
numeric, size of axis labels in the plot |
Intended use of the function - using scenario A data, compare shape of the migration profile between a TWO GROUPS of proteins WITHIN the ONE LABEL STATE
plot
##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package ="ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) ##example plot: g1D <- c("Q16540","P52815","P09001","Q13405","Q9H2W6") #group 1 data vector g1N <- 'group1' #group 1 name g2D <- c("Q9NVS2","Q9NWU5","Q9NX20","Q9NYK5","Q9NZE8") #group 2 data vector g2N <- 'group2' #group 2 name max_frac <- 23 twoGroupsWithinLabelCoMigration(forAnalysis, max_frac, g1D, g1N, g2D, g2N)##Use example normalised proteins file inputFile <- system.file("extData", "dataNormProts.txt", package ="ComPrAn") #read file in and change structure of table to required format forAnalysis <- protImportForAnalysis(inputFile) ##example plot: g1D <- c("Q16540","P52815","P09001","Q13405","Q9H2W6") #group 1 data vector g1N <- 'group1' #group 1 name g2D <- c("Q9NVS2","Q9NWU5","Q9NX20","Q9NYK5","Q9NZE8") #group 2 data vector g2N <- 'group2' #group 2 name max_frac <- 23 twoGroupsWithinLabelCoMigration(forAnalysis, max_frac, g1D, g1N, g2D, g2N)
Perform uncentered correlation
uncenteredCor(xx, yy)uncenteredCor(xx, yy)
xx |
numeric vector |
yy |
numeric vector |
vector