Package 'MSstatsQC'

Title: Longitudinal system suitability monitoring and quality control for proteomic experiments
Description: MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments.
Authors: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut]
Maintainer: Eralp Dogu <[email protected]>
License: Artistic License 2.0
Version: 2.23.0
Built: 2024-07-17 12:03:15 UTC
Source: https://github.com/bioc/MSstatsQC

Help Index

A function to identify the time of a change in the mean or variability of a metric

Description

A function to identify the time of a change in the mean or variability of a metric

Usage

ChangePointEstimator(
  data = NULL,
  peptide,
  L = 1,
  U = 5,
  metric,
  normalization = TRUE,
  ytitle = "Change Point Plot - mean",
  type = "mean",
  selectMean = NULL,
  selectSD = NULL
)

Arguments

data

comma-separated (.csv), metric file. It should contain a "Precursor" column and the metrics columns. It should also include "Annotations" for each observation.
peptide

the name of precursor of interest.
L

Lower bound of the guide set.
U

Upper bound of the guide set.
metric

the name of metric of interest.
normalization

TRUE metric is standardized and FALSE if not standardized.
ytitle

the y-axis title of the plot. Defaults to "Change Point Plot - mean". The x-axis title is by default "QCno-name of peptide"
type

the type of the control chart. Two values can be assigned, "mean" or "variability". Default is "mean".
selectMean

the mean of a metric. It is used when mean is known. It is NULL when mean is not known. The default is NULL.
selectSD

the standard deviation of a metric. It is used when standard deviation is known. It is NULL when mean is not known. The default is NULL.

Value

A plot of likelihood statistics versus time per peptide and metric generated from CP.data.prepare data frame.

Examples

# First process the data to make sure it's ready to use
sampleData <- DataProcess(S9Site54)
head(sampleData)
# Find the name of the peptides
levels(sampleData$Precursor)
# Calculate change point statistics
ChangePointEstimator(data = sampleData, peptide = "VLVLDTDYK", metric = "BestRetentionTime")
ChangePointEstimator(data = sampleData, peptide = "VLVLDTDYK", metric = "BestRetentionTime",
                     ytitle = "Change Point Plot - variability", type = "variability")
ChangePointEstimator(data = sampleData, peptide = "VLVLDTDYK", metric = "BestRetentionTime",
                     selectMean = 27.78, selectSD = 8.19)
ChangePointEstimator(data = sampleData, peptide = "DDGSWEVIEGYR", metric = "TotalArea")
ChangePointEstimator(data = sampleData, peptide = "DDGSWEVIEGYR", metric = "TotalArea",
                     selectMean = 35097129, selectSD = 34132861)
ChangePointEstimator(data = sampleData, peptide = "TAAYVNAIEK", metric = "MaxFWHM")

A function to create cumulative sum charts for mean (CUSUMm) and cumulative sum charts for variability (CUSUMv) control charts

Description

A function to create cumulative sum charts for mean (CUSUMm) and cumulative sum charts for variability (CUSUMv) control charts

Usage

CUSUMChart(
  data = NULL,
  peptide,
  L = 1,
  U = 5,
  metric,
  normalization = TRUE,
  ytitle = "CUSUMm",
  type = "mean",
  selectMean = NULL,
  selectSD = NULL,
  referenceValue = 0.5,
  decisionInterval = 5
)

Arguments

data

comma-separated (.csv), metric file. It should contain a "Precursor" column and the metrics columns. It should also include "Annotations" for each observation.
peptide

the name of precursor of interest.
L

Lower bound of the guide set.
U

Upper bound of the guide set.
metric

the name of metric of interest.
normalization

TRUE if metric is standardized and FALSE if not standardized.
ytitle

the y-axis title of the plot. Defaults to "CUSUMm". The x-axis title is by default "Time : name of peptide"
type

the type of the control chart. Two values can be assigned, "mean" or "variability". Default is "mean"
selectMean

the mean of a metric. It is used when mean is known. It is NULL when mean is not known. The default is NULL.
selectSD

the standard deviation of a metric. It is used when standard deviation is known. It is NULL when mean is not known. The default is NULL.
referenceValue

the value that is used to tune the control chart for a proper shift size
decisionInterval

the threshold to detect an out-of-control observation

Value

A plot of pozitive and negative CUSUM statistics versus time per peptide and metric generated from CUSUM.data.prepare data frame.

Examples

# First process the data to make sure it's ready to use
sampleData <- DataProcess(S9Site54)
head(sampleData)
# Find the name of the peptides
levels(sampleData$Precursor)
# Calculate CUSUM statistics
CUSUMChart(data = sampleData, peptide = "VLVLDTDYK", metric = "BestRetentionTime")
CUSUMChart(data = sampleData, peptide = "VLVLDTDYK", metric = "BestRetentionTime",
           ytitle = "CUSUMv", type = "variability")
CUSUMChart(data = sampleData, peptide = "VLVLDTDYK", metric = "BestRetentionTime",
           selectMean = 27.78, selectSD = 8.19)
CUSUMChart(data = sampleData, peptide = "DDGSWEVIEGYR", metric = "TotalArea")
CUSUMChart(data = sampleData, peptide = "DDGSWEVIEGYR", metric = "TotalArea",
           selectMean = 35097129, selectSD = 34132861)
CUSUMChart(data = sampleData, peptide = "TAAYVNAIEK", metric = "MaxFWHM")

A data processing function

Description

A data processing function

Usage

DataProcess(data = NULL)

Arguments

data

Comma-separated (*.csv), QC file format. It should contain a Precursor column and the metrics columns.

Value

A data frame that processes using input.sanity.check function.

Examples

# The data is "S9Site54" which is defined in the package.
data <- DataProcess(S9Site54)

A function to create heatmaps to compare performance with user defined performance criteria

Description

A function to create heatmaps to compare performance with user defined performance criteria

Usage

DecisionMap(
  data = NULL,
  method = "XmR",
  peptideThresholdRed = 0.7,
  peptideThresholdYellow = 0.5,
  L = 1,
  U = 5,
  type = "mean",
  title = "heatmap plot",
  listMean = NULL,
  listSD = NULL
)

Arguments

data

Comma-separated (*.csv), QC file format. It should contain a Precursor column and the metrics columns.
method

It is either "CUSUM" or "XmR"
peptideThresholdRed

Is a threshold that marks percentage of peptides above it red on the heatmap. Defaults to 0.7
peptideThresholdYellow

Is a threshold that marks percentage of peptides above it and below the peptideThresholdRed, yellow on the heatmap. Defaults to 0.5
L

Lower bound of the giude set. Defaults to 1
U

Upper bound of the guide set. Defaults to 5
type

can take two values, "mean" or "dispersion". Defaults to "mean"
title

the title of the plot. Defaults to "heatmap plot"
listMean

List of the means for the metrics. If you don't know the means leave it as NULL and they will be calculated automatically by using L and U. The default is NULL.
listSD

List of the standard deviations for the metrics. If you don't know the standard deviations leave it as NULL and they will be calculated automatically by using L and U. The default is NULL.

Value

A heatmap to aggregate results per metric generated from heatmap.DataFrame data frame.

Examples

# First process the data to make sure it's ready to use
sampleData <- DataProcess(S9Site54)
head(sampleData)
# Draw Decision maker plot
DecisionMap(data = sampleData, method = "CUSUM")
DecisionMap(data = sampleData, method = "CUSUM", type = "variability")
DecisionMap(data = sampleData, method = "XmR")
DecisionMap(data = sampleData, method = "XmR", type = "variability")

A function to summarize missing values

Description

A function to summarize missing values

Usage

MissingDataMap(data)

Arguments

data

Processed data

Value

A plot of missing values.

Examples

# The data is "S9Site54" which is defined in the package.
data <- DataProcess(S9Site54)
MissingDataMap(data)

A function to convert MSnbase files to MSstatsQC format

Description

A function to convert MSnbase files to MSstatsQC format

Usage

MSnbaseToMSstatsQC(msfile)

Arguments

msfile

data file to be converted

Value

A data frame that can be used with MSstatsQC

A csv file that is converted from raw files

Examples

## Not run: library(RforProteomics)
## Not run: msfile <- getPXD000001mzXML()
## Not run: MSnbaseToMSstatsQC(msfile)

DDA QC data from QCloud System

Description

QC results generated from QCloud system

Usage

data(QCloudDDA)

Format

csv

Details

DDA QC data from QCloud System

Value

An example dataset generated from QCloud system

Examples

head(QCloudDDA)

SRM QC data from QCloud System

Description

QC results generated from QCloud system

Usage

data(QCloudSRM)

Format

csv

Details

SRM QC data from QCloud System

Value

An example dataset generated from QCloud system

Examples

head(QCloudSRM)

DIA iRT data from QuiC System

Description

QC results generated from QuiC system

Usage

data(QuiCDIA)

Format

csv

Details

DIA iRT data from QuiC System

Value

An example dataset generated from QuiC system

Examples

head(QuiCDIA)

A function to create radar plot to aggregate results from X and mR charts or CUSUMm and CUSUMv charts.

Description

A function to create radar plot to aggregate results from X and mR charts or CUSUMm and CUSUMv charts.

Usage

RadarPlot(
  data = NULL,
  L = 1,
  U = 5,
  method = "XmR",
  listMean = NULL,
  listSD = NULL
)

Arguments

data

omma-separated (.csv), metric file. It should contain a "Precursor" column and the metrics columns. It should also include "Annotations" for each observation.
L

lower bound of the guide set.
U

upper bound of the guide set.
method

defines the method selected to construct control charts.
listMean

list of the means for each metric. It is used when mean is known. It is NULL when mean is not known. The default is NULL.
listSD

list of the standard deviations for each metric. It is used when standard deviation is known. It is NULL when mean is not known. The default is NULL. automatically by using L and U. The default is NULL.

Value

A radar plot to aggregate results per metric generated from XmR.Radar.Plot.DataFrame data frame or CUSUM.Radar.Plot.DataFrame data frame.

Examples

# First process the data to make sure it's ready to use
sampleData <- DataProcess(S9Site54)
head(sampleData)
# Draw XmR radar plot
RadarPlot(data = sampleData)
RadarPlot(data = sampleData, method = "CUSUM")
RadarPlot(data = sampleData,
                listMean = list("BestRetentionTime" = 27.78,
                                "TotalArea" = 35097129,
                                "MaxFWHM" = 0.28,
                                "MinStartTime" = 24),
                listSD = list("BestRetentionTime" = 8.19,
                              "TotalArea" = 34132861,
                              "MaxFWHM" = 0.054,
                              "MinStartTime" = 24)
                )

A data processing function for removing missing values

Description

A data processing function for removing missing values

Usage

RemoveMissing(data = NULL)

Arguments

data

Comma-separated (*.csv), QC file format. It should contain a Precursor column and the metrics columns.

Value

A data frame that processes using input.sanity.check function.

Examples

# The data is "S9Site54" which is defined in the package.
data <- RemoveMissing(S9Site54)

A function to create river plot to aggregate results from X and mR charts or CUSUMm and CUSUMv charts.

Description

A function to create river plot to aggregate results from X and mR charts or CUSUMm and CUSUMv charts.

Usage

RiverPlot(
  data = NULL,
  L = 1,
  U = 5,
  method = "XmR",
  listMean = NULL,
  listSD = NULL
)

Arguments

data

omma-separated (.csv), metric file. It should contain a "Precursor" column and the metrics columns. It should also include "Annotations" for each observation.
L

lower bound of the guide set.
U

upper bound of the guide set.
method

defines the method selected to construct control charts.
listMean

list of the means for each metric. It is used when mean is known. It is NULL when mean is not known. The default is NULL.
listSD

list of the standard deviations for each metric. It is used when standard deviation is known. It is NULL when mean is not known. The default is NULL.

Value

A river plot to aggregate results per metric generated from XmR.Summary.DataFrame data frame or CUSUM.Summary.DataFrame data frame.

Examples

# First process the data to make sure it's ready to use
sampleData <- DataProcess(S9Site54)
head(sampleData)
# Draw XmR summary plot
RiverPlot(data = sampleData)
RiverPlot(data = sampleData, L=1, U=20, method = "XmR",
                listMean = list("BestRetentionTime" = 27.78,
                                "TotalArea" = 35097129,
                                 "MaxFWHM" = 0.28,
                                 "MinStartTime" = 24),
                listSD = list("BestRetentionTime" = 8.19,
                              "TotalArea" = 34132861,
                              "MaxFWHM" = 0.054,
                              "MinStartTime" = 24)
                )

CPTAC study 9.1 site 54 dataset

Description

system suitabilty testing results generated during CPTAC Study 9.1 for Site 54

Usage

data(S9Site54)

Format

csv

Details

CPTAC system suitability testing data for Site 54 from Study 9.1

Value

An example dataset generated from CPTAC study 9.1

References

http://www.mcponline.org/content/early/2015/02/18/mcp.M114.047050

Examples

head(S9Site54)

A function to construct individual (X) and moving range (mR) control charts

Description

A function to construct individual (X) and moving range (mR) control charts

Usage

XmRChart(
  data = NULL,
  peptide,
  L = 1,
  U = 5,
  metric,
  normalization = FALSE,
  ytitle = "Individual observations",
  type = "mean",
  selectMean = NULL,
  selectSD = NULL
)

Arguments

data

comma-separated (.csv), metric file. It should contain a "Precursor" column and the metrics columns. It should also include "Annotations" for each observation.
peptide

the name of precursor of interest.
L

Lower bound of the guide set.
U

Upper bound of the guide set.
metric

the name of metric of interest.
normalization

TRUE if metric is standardized and FALSE if not standardized.
ytitle

the y-axis title of the plot. Defaults to "Individual observations". The x-axis title is by default "Time : name of peptide"
type

the type of the control chart. Two values can be assigned, "mean" or "variability". Default is "mean".
selectMean

the mean of a metric. It is used when mean is known. It is NULL when mean is not known. The default is NULL.
selectSD

the standard deviation of a metric. It is used when standard deviation is known. It is NULL when mean is not known. The default is NULL.

Value

A plot of individual values or moving ranges versus time per peptide and metric generated from XmR.data.prepare data frame.

Examples

# First process the data to make sure it's ready to use
sampleData <- DataProcess(S9Site54)
head(sampleData)
# Find the name of the peptides
levels(sampleData$Precursor)
# Calculate X and mR statistics
XmRChart(data = sampleData, peptide = "VLVLDTDYK", metric = "BestRetentionTime")
XmRChart(data = sampleData, peptide = "VLVLDTDYK", metric = "BestRetentionTime",
         ytitle = "moving ranges", type = "variability")
XmRChart(data = sampleData, peptide = "VLVLDTDYK", metric = "BestRetentionTime",
         selectMean = 27.78, selectSD = 8.19)
XmRChart(data = sampleData, peptide = "DDGSWEVIEGYR", metric = "TotalArea")
XmRChart(data = sampleData, peptide = "DDGSWEVIEGYR", metric = "TotalArea",
         selectMean = 35097129, selectSD = 34132861)
XmRChart(data = sampleData, peptide = "TAAYVNAIEK", metric = "MaxFWHM")
XmRChart(data = sampleData, peptide = "LVNELTEFAK", metric = "MinStartTime")