Package: MSstats
Author: Meena Choi [email protected], Tsung-Heng Tsai [email protected], Cyril Galitzine [email protected]
Date: October 31, 2019
This vignette summarizes the introduction and various options of all
functionalities in MSstats. More details are available in
User Manual.
Preprocess MSstats input report from Skyline and convert into the required input format for MSstats.
input : name of MSstats input report from Skyline,
which includes feature-level data.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.removeiRT : removeiRT=TRUE(default) will
remove the proteins or peptides which are labeld ‘iRT’ in ‘StandardType’
column. removeiRT=FALSE will keep them.removeProtein_with1Peptide :
removeProtein_with1Peptide=TRUE will remove the proteins
which have only 1 peptide and charge.
removeProtein_with1Peptide=FALSE is default.filter_with_Qvalue :
removeProtein_with1Peptide=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.Convert MaxQuant output into the required input format for MSstats.
evidence : name of 'evidence.txt' data,
which includes feature-level data.annotation : name of 'annotation.txt' data
which includes Raw.file, Condition,
BioReplicate, Run,
IsotopeLabelType information.proteinGroups : name of
'proteinGroups.txt' data. It needs to matching protein
group ID. If proteinGroups=NULL, use
'Proteins' column in 'evidence.txt'.proteinID : 'Proteins'(default) or
'proteinGroups' in 'proteinGroup.txt' for
Protein ID.useUniquePeptide :
useUniquePeptide=TRUE(default) removes peptides that are
assigned for more than one proteins. We assume to use unique peptide for
each protein.summaryforMultipleRows :
summaryforMultipleRows=max(default) or sum, when there are
multiple measurements for certain feature and certain run, use highest
or sum of all.fewMeasurements :
fewMeasurements='remove'(default) will remove the features
that have 1 or 2 measurements across runs.removeMpeptides :
removeMpeptides=TRUE(default) will remove the peptides
including ‘M’ sequence.removeProtein_with1Peptide :
removeProtein_with1Peptide=TRUE will remove the proteins
which have only 1 peptide and charge. FALSE is
default.# Read in MaxQuant files
proteinGroups <- read.table("proteinGroups.txt", sep="\t", header=TRUE)
infile <- read.table("evidence.txt", sep="\t", header=TRUE)
# Read in annotation including condition and biological replicates per run.
# Users should make this annotation file. It is not the output from MaxQuant.
annot <- read.csv("annotation.csv", header=TRUE)
raw <- MaxQtoMSstatsFormat(evidence=infile,
annotation=annot,
proteinGroups=proteinGroups)Convert Progenesis output into the required input format for MSstats.
input : name of Progenesis output, 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
information. It will be matched with the column name of input for MS
runs.useUniquePeptide :
useUniquePeptide=TRUE(default) removes peptides that are
assigned for more than one proteins. We assume to use unique peptide for
each protein.summaryforMultipleRows :
summaryforMultipleRows=max(default) or sum,
when there are multiple measurements for certain feature and certain
run, use highest or sum of multiple intensities.fewMeasurements :
fewMeasurements='remove'(default) will remove the features
that have 1 or 2 measurements across runs.removeProtein_with1Peptide :
removeProtein_with1Peptide=TRUE will remove the proteins
which have only 1 peptide and charge. FALSE is
default.input <- read.csv("output_progenesis.csv", stringsAsFactors=FALSE)
# Read in annotation including condition and biological replicates per run.
# Users should make this annotation file. It is not the output from Progenesis.
annot <- read.csv('annotation.csv')
raw <- ProgenesistoMSstatsFormat(input, annotation=annot)Convert Spectronaut output into the required input format for MSstats.
input: name of Spectronaut 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.summaryforMultipleRows :
summaryforMultipleRows=max(default) or sum,
when there are multiple measurements for certain feature and certain
run, use highest or sum of multiple intensities.Data pre-processing and quality control of MS runs of the original raw data into quantitative data for model fitting and group comparison. Log transformation is automatically applied and additional variables are created in columns for model fitting and group comparison process. Three options of data pre-processing and quality control of MS runs in dataProcess are
raw : name of the raw (input) data set.logTrans : logarithm transformation with base
2(default) or 10. If logTrans=2, the measurement of
Variable ABUNDANCE is log-transformed with base 2. Same
apply to logTrans=10.normalization : normalization 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.nameStandards : vector of global standard peptide
names. only for normalization with global standard peptides.fillIncompleteRows : If the input dataset has
incomplete rows, TRUE (default) adds the rows with
intensity value=NA for missing peaks. FALSE reports error
message with list of features which have incomplete rows.featureSubset : "all" (default) uses all
features that the data set has. "top3" uses top 3 features
which have highest average of log2(intensity) across runs.
"topN" uses top N features which has highest average of
log2(intensity) across runs. It needs the input for
n_top_feature option. "highQuality" flags
uninformative feature and outliers.remove_uninformative_feature_outlier : 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.n_top_feature : The number of top features for
featureSubset='topN'. Default is
n_top_feature=3, which means to use top 3 features.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 and Progenesis should use ‘0’. Null assumes that all
NA intensites are randomly missing.cutoffCensored : Cutoff value for censoring. Only with
censoredInt='NA' or '0'. Default is
'minFeature', which uses minimum value for each feature.
'minFeatureNRun' uses the smallest between minimum value of
corresponding feature and minimum value of corresponding run.
'minRun' uses minumum value for each run.MBimpute : 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.remove50missing : only for
summaryMethod="TMP". TRUE removes the runs
which have more than 50% missing values. FALSE is
default.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 csv file is automatically created with the default name of
“BetweenRunInterferenceFile.csv”. The command address can help to
specify where to store the file as well as how to modify the beginning
of the file name.maxQuantileforCensored : Maximum quantile for deciding
censored missing values. Default is 0.999.RUN
and Protein.RUN and Protein. It counts feature intensities
greater than 1, after selecting all/topN/highquality features and
removing features with only one measurement across MS runs.RUN and Protein /total number of features in a
Proteins.RUN and Protein. This column
is shown only if users allow to impute the missing value.Visualization for explanatory data analysis. To illustrate the
quantitative data after data-preprocessing and quality control of MS
runs, dataProcessPlots takes the quantitative data from
function dataProcess as input and automatically generate
three types of figures in pdf files as output :
Profile plot : to identify the potential sources of variation for
each protein. One of output from dataProcess,
xxx$ProcessedData, is used for profile plots. Another
output from dataProcess, xxx$RunlevelData, is
used for profile plots with summarization. X-axis represents MS runs.
Y-axis is for log-intensities of transitions. Reference/endogenous
signals are in the left/right panel. Line colors indicate peptides and
line types indicate transitions. Type of dots(filled vs empty dot)
indicates censored missing values or not. In summarization plots, gray
dots and lines are the same as original profile plots with
xxx$ProcessedData. Dark dots and lines are for summarized
intensities from xxx$RunlevelData.
Quality control plot : to illustrate the systematic bias between
MS runs and to check normalization. After normalization, the reference
signals for all proteins should be stable across MS runs.
xxx$ProcessedData is used for plots. X-axis is for MS runs.
Y-axis is for log-intensities of transition
(xxx$ProcessedData$ABUNDANCE). Reference/endogenous signals
are in the left/right panel. The pdf file contains (1) QC plot for all
proteins and (2) QC plots for each protein separately.
Mean plot for conditions : to illustrate mean and variability of
each condition per protein. Summarized intensnties from
xxx$RunlevelData are used for plots. X-axis is for
condition. Y-axis is for summarized log transformed intensities. If
scale=TRUE, the levels of conditions is scaled according to
its actual values at x-axis. Red points indicate the mean for each
condition. If interval="CI", error bars indicate the
confidence interval with 0.95 significant level for each condition. If
interval="SD", error bars indicate the standard deviation
for each condition. The interval is not related with model-based
analysis in groupCompaison.
data : name of the (output of dataProcess
function) data set.type : choice of visualization.
"ProfilePlot" represents profile plot of log intensities
across MS runs. "QCPlot" represents quality control plot of
log intensities across MS runs. "ConditionPlot" represents
mean plot of log ratios (Light/Heavy) across conditions.featureName : for "ProfilePlot" only,
"Transition" (default) means printing feature legend in
transition-level. "Peptide" means printing feature legend
in peptide-level. "NA" means no feature legend
printing.ylimUp : upper limit for y-axis in the log scale.
FALSE (Default) for Profile Plot and QC Plot use the upper
limit as rounded off maximum of log2(intensities) after normalization +
3. FALSE (Default) for Condition Plot is maximum of log
ratio + SD or CI.ylimDown : lower limit for y-axis in the log scale.
FALSE (Default) for Profile Plot and QC Plot is 0. `FALSE``
(Default) for Condition Plot is minumum of log ratio - SD or CI.scale : for "ConditionPlot" only,
FALSE (default) means each conditional level is not scaled
at x-axis according to its actual value (equal space at x-axis).
TRUE means each conditional level is scaled at x-axis
according to its actual value (unequal space at x-axis).interval : for "ConditionPlot" only,
"CI" (default) uses confidence interval with 0.95
significant level for the width of error bar. "SD" uses
standard deviation for the width of error bar.x.axis.size : size of x-axis labeling for “MS Runs” in
Profile Plot and QC Plot, and “Condition” in Condition Plot. Default is
10.y.axis.size : size of y-axis labels. Default is
10.text.size : size of labels represented each condition
at the top of graph in Profile Plot and QC plot. Default is
4text.angle : angle of labels represented each condition
at the top of graph in Profile Plot and QC plot or x-axis labeling in
Condition plot. Default is 0.legend.size : size of feature legend (transition-level
or peptide-level) above graph in Profile Plot. Default is
7.dot.size.profile : size of dots in profile plot.
Default is 2.dot.size.condition : size of dots in condition plot.
Default is 3.width : width of the saved file. Default is 10.height : height of the saved file. Default is 10.which.Protein : Protein list to draw plots. List can be
names of Proteins or order numbers of Proteins from
levels(xxx$ProcessedData$PROTEIN). Default is
"all", which generates all plots for each protein.originalPlot : TRUE (default) draws
original profile plots.summaryPlot : TRUE (default) draws profile
plots with summarization for run levels.save_condition_plot_result : TRUE saves
the table with values using condition plots. Default is FALSE.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" or
"ConditionPlot.pdf" or
"ConditionPlot_value.csv". 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.Tests for significant changes in protein abundance across conditions based on a family of linear mixed-effects models in targeted Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiment. It is applicable to multiple types of sample preparation, including label-free workflows, workflows that use stable isotope labeled reference proteins and peptides, and workflows that use fractionation. Experimental design of case-control study (patients are not repeatedly measured) or time course study (patients are repeatedly measured) is automatically determined based on proper statistical model.
contrast.matrix : comparison between conditions of
interests. Based on the levels of conditions, specify 1 or -1 to the
conditions of interests and 0 otherwise. The levels of conditions are
sorted alphabetically. Command
levels(xxx$ProcessedData$GROUP_ORIGINAL) after using
dataProcess function can illustrate the actual order of the
levels of conditions.data : name of the (output of dataProcess function)
data set.# QuantData <- dataProcess(SRMRawData)
#
# levels(QuantData$ProcessedData$GROUP_ORIGINAL)
# comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0), nrow=1)
# row.names(comparison) <- "T7-T1"
#
# # Tests for differentially abundant proteins with models:
# testResultOneComparison <- groupComparison(contrast.matrix=comparison, data=QuantData)Visualization for model-based analysis and summarizing differentially
abundant proteins. To summarize the results of log-fold changes and
adjusted p-values for differentially abundant proteins,
groupComparisonPlots takes testing results from function
groupComparison as input and automatically generate three
types of figures in pdf files as output :
Volcano plot : For each comparison separately. It illustrates
actual log-fold changes and adjusted p-values for each comparison
separately with all proteins. The x-axis is the log fold change. The
base of logarithm transformation is the same as specified in “logTrans”
from dataProcess. The y-axis is the negative log2 or log10
adjusted p-values. The horizontal dashed line represents the FDR cutoff.
The points below the FDR cutoff line are non-significantly abundant
proteins (colored in black). The points above the FDR cutoff line are
significantly abundant proteins (colored in red/blue for
up-/down-regulated). If fold change cutoff is specified
(FCcutoff = specific value), the points above the FDR
cutoff line but within the FC cutoff line are non-significantly abundant
proteins (colored in black).
Heatmap : For multiple comparisons. It illustratea up-/down-regulated proteins for multiple comparisons with all proteins. Each column represents each comparison of interest. Each row represents each protein. Color red/blue represents proteins in that specific comparison are significantly up-regulated/down-regulated proteins with FDR cutoff and/or FC cutoff. The color scheme shows the evidences of significance. The darker color it is, the stronger evidence of significance it has. Color gold represents proteins are not significantly different in abundance.
Comparison plot : For multiple comparisons per protein. It illustrates log-fold change and its variation of multiple comparisons for single protein. X-axis is comparison of interest. Y-axis is the log fold change. The red points are the estimated log fold change from the model. The error bars are the confidence interval with 0.95 significant level for log fold change. This interval is only based on the standard error, which is estimated from the model.
data : xxx$ComparisonResult in testing
output from function groupComparison.type : choice of visualization.
"VolcanoPlot" represents volcano plot of log fold changes
and adjusted p-values for each comparison separately.
"Heatmap" represents heatmap of adjusted p-values for
multiple comparisons. "ComparisonPlot" represents
comparison plot of log fold changes for multiple comparisons per
protein.sig : FDR cutoff for the adjusted p-values in heatmap
and volcano 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) uses the 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.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 next to dots or not. TRUE (default) means
protein names, which are significant, are displayed next to the points.
FALSE means no protein names are displayed.numProtein : The number of proteins which will be
presented in each heatmap. Default is 100. Maximum possible
number of protein for one heatmap is 180.clustering : Determines how to order proteins and
comparisons. Hierarchical cluster analysis with Ward method(minimum
variance) is performed. 'protein' (default) means that
protein dendrogram is computed and reordered based on protein means (the
order of row is changed). 'comparison' means comparison
dendrogram is computed and reordered based on comparison means (the
order of comparison is changed). 'both' means to reorder
both protein and comparison.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(xxx$Label), such as
levels(xxx$ComparisonResult$Label). Default is
"all", which generates all 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" or
"ComparisonPlot.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.# QuantData <- dataProcess(SRMRawData)
#
# # based on multiple comparisons (T1 vs T3; T1 vs T7; T1 vs T9)
# comparison1<-matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
# comparison2<-matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
# comparison3<-matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
# comparison<-rbind(comparison1,comparison2, comparison3)
# row.names(comparison)<-c("T3-T1","T7-T1","T9-T1")
#
# testResultMultiComparisons <- groupComparison(contrast.matrix=comparison, data=QuantData)
#
# # Volcano plot
# groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="VolcanoPlot")
#
# # Heatmap
# groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="Heatmap")
#
# # Comparison Plot
# groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="ComparisonPlot")Results based on statistical models for whole plot level inference are accurate as long as the assumptions of the model are met. The model assumes that the measurement errors are normally distributed with mean 0 and constant variance. The assumption of a constant variance can be checked by examining the residuals from the model.
To check the assumption of linear model for whole plot inference,
modelBasedQCPlots takes the results after fitting models
from function groupComparison as input and automatically
generate two types of figures in pdf files as output.
Normal quantile-quantile plot : For checking normally distributed errors. A normal quantile-quantile plot for each protein is generated in order to check whether the errors are well approximated by a normal distribution. If points fall approximately along a straight line, then the assumption is appropriate for that protein. Only large deviations from the line are problematic.
Residual plot : The plots of residuals against predicted (fitted) values. If it shows a random scatter, then the assumption is appropriate.
data : output from function
groupComparison.type : choice of visualization. "QQPlots"
represents normal quantile-quantile plot for each protein after fitting
models. "ResidualPlots" represents a plot of residuals
versus fitted values for each protein in the dataset.axis.size : size of axes labels. Default is
10.dot.size : size of points in the graph for residual
plots and QQ plots. Default is 3.text.size : size of labeling for feature names only in
normal quantile-quantile plots separately for each feature. Default is
7.legend.size : size of legend for feature names only in
residual plots. Default is 7.width : width of the saved file. Default is
10.height : height of the saved file. Default is
10.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.
If type="residualPlots" or "QQPlots",
"ResidualPlots.pdf" or "QQPlots.plf" will be
generated. 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.Calculate sample size for future experiments of a Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiment based on intensity-based linear model. 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. Two options of the calculation:
data : 'fittedmodel' in testing output
from function groupComparison.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. 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.# QuantData <- dataProcess(SRMRawData)
# head(QuantData$ProcessedData)
#
# ## based on multiple comparisons (T1 vs T3; T1 vs T7; T1 vs T9)
# comparison1 <- matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
# comparison2 <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
# comparison3 <- matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
# comparison <- rbind(comparison1,comparison2, comparison3)
# row.names(comparison) <- c("T3-T1","T7-T1","T9-T1")
#
# testResultMultiComparisons <- groupComparison(contrast.matrix=comparison,data=QuantData)
#
# #(1) Minimal number of biological replicates per condition
# designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=TRUE,
# desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
#
# #(2) Power calculation
# designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=2,
# desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)To illustrate the relationship of desired fold change and the calculated minimal number sample size which are
The input is the result from function
designSampleSize.
data : output from function
designSampleSize.# # (1) Minimal number of biological replicates per condition
# result.sample <- designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=TRUE,
# desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
# designSampleSizePlots(data=result.sample)
#
# # (2) Power
# result.power <- designSampleSize(data=testResultMultiComparisons$fittedmodel, numSample=2,
# desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)
# designSampleSizePlots(data=result.power)Model-based quantification for each condition or for each biological
samples per protein in a targeted Selected Reaction Monitoring (SRM),
Data-Dependent Acquisition (DDA or shotgun), and Data-Independent
Acquisition (DIA or SWATH-MS) experiment. Quantification takes the
processed data set by dataProcess as input and
automatically generate the quantification results (data.frame) with long
or matrix format. The quantification for endogenous samples is based on
run summarization from subplot model, with TMP robust estimation.
Sample quantification : individual biological sample
quantification for each protein. The label of each biological sample is
a combination of the corresponding group and the sample ID. If there are
no technical replicates or experimental replicates per sample, sample
quantification is the same as run summarization from dataProcess
(xxx$RunlevelData from dataProcess). If there
are technical replicates or experimental replicates, sample
quantification is median among run quantification corresponding MS
runs.
Group quantification : quantification for individual group or individual condition per protein. It is median among sample quantification.
data : name of the (processed) data set from
dataPRocess.type : choice of quantification. "Sample"
or "Group" for protein sample quantification or group
quantification.format : choice of returned format. "long"
for long format which has the columns named Protein,
Condition, LogIntensities (and
BioReplicate if it is subject quantification),
NumFeature for number of transitions for a protein, and
NumPeaks for number of observed peak intensities for a
protein. "matrix" for data matrix format which has the rows
for Protein and the columns, which are Groups(or
Conditions) for group quantification or the combinations of
BioReplicate and Condition (labeled by
"BioReplicate"_"Condition") for sample quantification.
Default is "matrix", whether format="long" or
"matrix", both files,
"Group or SampleQuantification_longformat.csv" and
"Group or SampleQuantification_dataMatrix.csv" will be
stored in the assigned folder.