In Vitek-Lab/MSstats: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments
BiocStyle::markdown()
knitr::opts_chunk$set(fig.width=10, fig.height=7, warning=FALSE, message=FALSE)
options(width=110)
```{=html}
# __MSstats: Protein/Peptide significance analysis__
Package: MSstats
Author: Anshuman Raina & Devon Kohler
Date: 5th Semptember 2024
## __Introduction__
`MSstats`, an R package in Bioconductor, supports protein differential analysis
for statistical relative quantification of proteins and peptides in global,
targeted and data-independent proteomics. It handles shotgun, label-free and
label-based (universal synthetic peptide-based) SRM (selected reaction
monitoring), and DIA (data independent acquisition) experiments. It can be used
for experiments with complex designs (e.g. comparing more than two experimental
conditions, or a repeated measure design, such as a time course).
This vignette summarizes the introduction and various options of all
functionalities in `MSstats`. More details are available in `User Manual`.
For more information about the MSstats workflow, including a detailed
description of the available processing options and their impact on the
resulting differential analysis, please see the following publication:
Kohler et al, Nature Protocols 19, 2915–2938 (2024).
## __Installation__
To install this package, start R (version “4.0”) and enter:
``` {r code Installation}
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MSstats")
library(MSstats)
library(ggplot2)
1. Workflow
1.1 Raw Data
To begin with, we will load sample datasets, including both annotated and plain
data. The dataset you need can be found here.
We will also load the Annotation Dataset using MSstatsConvert. You can access
this dataset here.
``` {r code Load Dataset}
library(MSstats)
Load data
pd_raw = system.file("tinytest/raw_data/PD/pd_input.csv",
package = "MSstatsConvert")
annotation_raw = system.file("tinytest/raw_data/PD/annot_pd.csv",
package = "MSstatsConvert")
pd = data.table::fread(pd_raw)
annotation = data.table::fread(annotation_raw)
head(pd, 5)
head(annotation, 5)
### __1.2 Loading PD Data to MSstats__
The imported data from Step 1.1. now must be converted through `MSstatsConvert`
package's `PDtoMSstatsFormat` converter.
This function converts the Proteome Discoverer output into the required input
format for `MSstats`.
Actual data modification can be seen below:
```r
library(MSstatsConvert)
pd_imported = MSstatsConvert::PDtoMSstatsFormat(pd, annotation,
use_log_file = FALSE)
head(pd_imported)
1.3 Converters
We have the following converters, which allow you to convert various types of
output reports which include the feature level data to the required input format
of MSstats. Further information about the converters can be found in the
MSstatsConvert package.
DIANNtoMSstatsFormatDIAUmpiretoMSstatsFormatFragPipetoMSstatsFormatMaxQtoMSstatsFormatOpenMStoMSstatsFormatOpenSWATHtoMSstatsFormatPDtoMSstatsFormatProgenesistoMSstatsFormatSkylinetoMSstatsFormatSpectronauttoMSstatsFormatMetamorpheusToMSstatsFormat
We show an example of how to use the above said Converters. For more information
about using the individual converters please see the coresponding documentation.
skyline_raw = system.file("tinytest/raw_data/Skyline/skyline_input.csv",
package = "MSstatsConvert")
skyline = data.table::fread(skyline_raw)
head(skyline, 5)
msstats_format = MSstatsConvert::SkylinetoMSstatsFormat(skyline_raw,
qvalue_cutoff = 0.01,
useUniquePeptide = TRUE,
removeFewMeasurements = TRUE,
removeOxidationMpeptides = TRUE,
removeProtein_with1Feature = TRUE)
head(msstats_format)
1.4 Data Process
Once we import the dataset correctly with Converter, we need to pre-process the
data which is done by the dataProcess function. This step involves data
processing and quality control of the measured feature intensities.
This function includes 5 main processing steps (with other additional small
steps):
-
Log transformation - Transform the feature intensities from their original
scale to the log scale. This step helps make the data closer to being normally
distributed, requiring less replicates for the central limit theorem to kick in.
-
Normalization - There are three different normalization options 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.
-
Feature selection - This also has three options i.e. Select All features,
Top-N features (by mean intensity) or “Best” features.
-
Missing value imputation - We impute plausible values in case of missing
data points. The RunLevelData can be queried to show Number of imputed
intensities (censored intensities) in a RUN and Protein.
-
Summarization - After data processing the individual features are
summarized up to the protein-level using Tukey's Median Polish. Linear
summarization is also available as an option.
``` {r code dataProcess}
summarized = dataProcess(
pd_imported,
logTrans = 2,
normalization = "equalizeMedians",
featureSubset = "all",
n_top_feature = 3,
summaryMethod = "TMP",
equalFeatureVar = TRUE,
censoredInt = "NA",
MBimpute = TRUE
)
head(summarized$FeatureLevelData)
head(summarized$ProteinLevelData)
head(summarized$SummaryMethod)
### __1.4.1 Data Process Plots__
After processing the input data, `MSstats` provides multiple plots to analyze the
results. Here we show the various types of plots we can use. By default, a
pdf file will be downloaded with corresponding feature level data and the Plot
generated. Alternatively, the `address` parameter can be set to `FALSE` which
will output the plots directly.
```r
# Profile plot
dataProcessPlots(data=summarized, type="ProfilePlot",
address = FALSE, which.Protein = "P0ABU9")
# Quality control plot
dataProcessPlots(data=summarized, type="QCPlot",
address = FALSE, which.Protein = "P0ABU9")
# Quantification plot for conditions
dataProcessPlots(data=summarized, type="ConditionPlot",
address = FALSE, which.Protein = "P0ABU9")
1.5 Modeling
In this step we test for differential changes in protein abundance across
conditions using a linear mixed-effects model. The model will be automatically
adjusted based on your experimental design.
A contrast matrix must be provided to the model. Alternatively, all pairwise
comparisons can be made by passing pairwise to the function. For more
information on creating contrast matrices, please see the citation linked
at the beginning of this document.
``` {r code groupComparison}
model = groupComparison("pairwise", summarized)
Model Details
``` {r Model }
head(model$ModelQC)
head(model$ComparisonResult)
1.5.1 groupComparisonPlot
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 illustrates 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.
``` {r GroupComparisonPlots}
groupComparisonPlots(
model$ComparisonResult,
type="Heatmap",
sig = 0.05,
FCcutoff = FALSE,
logBase.pvalue = 10,
ylimUp = FALSE,
ylimDown = FALSE,
xlimUp = FALSE,
x.axis.size = 10,
y.axis.size = 10,
dot.size = 3,
text.size = 4,
text.angle = 0,
legend.size = 13,
ProteinName = TRUE,
colorkey = TRUE,
numProtein = 100,
clustering = "both",
width = 800,
height = 600,
which.Comparison = "all",
which.Protein = "all",
address = FALSE,
isPlotly = FALSE
)
groupComparisonPlots(
model$ComparisonResult,
type="VolcanoPlot",
sig = 0.05,
FCcutoff = FALSE,
logBase.pvalue = 10,
ylimUp = FALSE,
ylimDown = FALSE,
xlimUp = FALSE,
x.axis.size = 10,
y.axis.size = 10,
dot.size = 3,
text.size = 4,
text.angle = 0,
legend.size = 13,
ProteinName = TRUE,
colorkey = TRUE,
numProtein = 100,
clustering = "both",
width = 800,
height = 600,
which.Comparison = "Condition2 vs Condition4",
which.Protein = "all",
address = FALSE,
isPlotly = FALSE
)
### __1.6 GroupComparisonQCPlots__
To check and verify that the resultant data of `groupComparison` offers a linear
model for whole plot inference, `groupComparisonQC` plots take the fitted data
and provide two ways of plotting:
1. Normal Q-Q plot : Quantile-Quantile plots represents normal quantile-quantile
plot for each protein after fitting models
2. Residual plot : represents a plot of residuals versus fitted values for each
protein in the dataset.
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.
``` {r GroupComparisonQCplots, results='hide', message=FALSE, warning=FALSE}
source("..//R//groupComparisonQCPlots.R")
groupComparisonQCPlots(data=model, type="QQPlots", address=FALSE,
which.Protein = "P0ABU9")
groupComparisonQCPlots(data=model, type="ResidualPlots", address=FALSE,
which.Protein = "P0ABU9")
1.7 Sample Size Calculation
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:
- number of biological replicates per condition
- power
sample_size_calc = designSampleSize(model$FittedModel,
desiredFC=c(1.75,2.5),
power = TRUE,
numSample=5)
1.7.1 Sample Size Calculation Plot
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.
designSampleSizePlots(sample_size_calc, isPlotly=FALSE)
1.8 Quantification from groupComparison Data
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 (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.
sample_quant_long = quantification(summarized,
type = "Sample",
format = "long")
sample_quant_long
sample_quant_wide = quantification(summarized,
type = "Sample",
format = "matrix")
sample_quant_wide
group_quant_long = quantification(summarized,
type = "Group",
format = "long")
group_quant_long
group_quant_wide = quantification(summarized,
type = "Group",
format = "matrix")
group_quant_wide
Vitek-Lab/MSstats documentation built on Nov. 9, 2024, 5:35 p.m.
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Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
BiocStyle::markdown()
knitr::opts_chunk$set(fig.width=10, fig.height=7, warning=FALSE, message=FALSE) options(width=110)
```{=html}
# __MSstats: Protein/Peptide significance analysis__
Package: MSstats
Author: Anshuman Raina & Devon Kohler
Date: 5th Semptember 2024
## __Introduction__
`MSstats`, an R package in Bioconductor, supports protein differential analysis
for statistical relative quantification of proteins and peptides in global,
targeted and data-independent proteomics. It handles shotgun, label-free and
label-based (universal synthetic peptide-based) SRM (selected reaction
monitoring), and DIA (data independent acquisition) experiments. It can be used
for experiments with complex designs (e.g. comparing more than two experimental
conditions, or a repeated measure design, such as a time course).
This vignette summarizes the introduction and various options of all
functionalities in `MSstats`. More details are available in `User Manual`.
For more information about the MSstats workflow, including a detailed
description of the available processing options and their impact on the
resulting differential analysis, please see the following publication:
Kohler et al, Nature Protocols 19, 2915–2938 (2024).
## __Installation__
To install this package, start R (version “4.0”) and enter:
``` {r code Installation}
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MSstats")
library(MSstats)
library(ggplot2)
1. Workflow
1.1 Raw Data
To begin with, we will load sample datasets, including both annotated and plain data. The dataset you need can be found here.
We will also load the Annotation Dataset using MSstatsConvert. You can access this dataset here.
``` {r code Load Dataset} library(MSstats)
Load data
pd_raw = system.file("tinytest/raw_data/PD/pd_input.csv", package = "MSstatsConvert")
annotation_raw = system.file("tinytest/raw_data/PD/annot_pd.csv", package = "MSstatsConvert")
pd = data.table::fread(pd_raw) annotation = data.table::fread(annotation_raw)
head(pd, 5) head(annotation, 5)
### __1.2 Loading PD Data to MSstats__
The imported data from Step 1.1. now must be converted through `MSstatsConvert`
package's `PDtoMSstatsFormat` converter.
This function converts the Proteome Discoverer output into the required input
format for `MSstats`.
Actual data modification can be seen below:
```r
library(MSstatsConvert)
pd_imported = MSstatsConvert::PDtoMSstatsFormat(pd, annotation,
use_log_file = FALSE)
head(pd_imported)
1.3 Converters
We have the following converters, which allow you to convert various types of
output reports which include the feature level data to the required input format
of MSstats. Further information about the converters can be found in the
MSstatsConvert package.
DIANNtoMSstatsFormatDIAUmpiretoMSstatsFormatFragPipetoMSstatsFormatMaxQtoMSstatsFormatOpenMStoMSstatsFormatOpenSWATHtoMSstatsFormatPDtoMSstatsFormatProgenesistoMSstatsFormatSkylinetoMSstatsFormatSpectronauttoMSstatsFormatMetamorpheusToMSstatsFormat
We show an example of how to use the above said Converters. For more information about using the individual converters please see the coresponding documentation.
skyline_raw = system.file("tinytest/raw_data/Skyline/skyline_input.csv", package = "MSstatsConvert") skyline = data.table::fread(skyline_raw) head(skyline, 5)
msstats_format = MSstatsConvert::SkylinetoMSstatsFormat(skyline_raw, qvalue_cutoff = 0.01, useUniquePeptide = TRUE, removeFewMeasurements = TRUE, removeOxidationMpeptides = TRUE, removeProtein_with1Feature = TRUE)
head(msstats_format)
1.4 Data Process
Once we import the dataset correctly with Converter, we need to pre-process the
data which is done by the dataProcess function. This step involves data
processing and quality control of the measured feature intensities.
This function includes 5 main processing steps (with other additional small steps):
-
Log transformation - Transform the feature intensities from their original scale to the log scale. This step helps make the data closer to being normally distributed, requiring less replicates for the central limit theorem to kick in.
-
Normalization - There are three different normalization options 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.
-
Feature selection - This also has three options i.e. Select All features, Top-N features (by mean intensity) or “Best” features.
-
Missing value imputation - We impute plausible values in case of missing data points. The RunLevelData can be queried to show Number of imputed intensities (censored intensities) in a RUN and Protein.
-
Summarization - After data processing the individual features are summarized up to the protein-level using Tukey's Median Polish. Linear summarization is also available as an option.
``` {r code dataProcess} summarized = dataProcess( pd_imported, logTrans = 2, normalization = "equalizeMedians", featureSubset = "all", n_top_feature = 3, summaryMethod = "TMP", equalFeatureVar = TRUE, censoredInt = "NA", MBimpute = TRUE )
head(summarized$FeatureLevelData)
head(summarized$ProteinLevelData)
head(summarized$SummaryMethod)
### __1.4.1 Data Process Plots__
After processing the input data, `MSstats` provides multiple plots to analyze the
results. Here we show the various types of plots we can use. By default, a
pdf file will be downloaded with corresponding feature level data and the Plot
generated. Alternatively, the `address` parameter can be set to `FALSE` which
will output the plots directly.
```r
# Profile plot
dataProcessPlots(data=summarized, type="ProfilePlot",
address = FALSE, which.Protein = "P0ABU9")
# Quality control plot
dataProcessPlots(data=summarized, type="QCPlot",
address = FALSE, which.Protein = "P0ABU9")
# Quantification plot for conditions
dataProcessPlots(data=summarized, type="ConditionPlot",
address = FALSE, which.Protein = "P0ABU9")
1.5 Modeling
In this step we test for differential changes in protein abundance across conditions using a linear mixed-effects model. The model will be automatically adjusted based on your experimental design.
A contrast matrix must be provided to the model. Alternatively, all pairwise
comparisons can be made by passing pairwise to the function. For more
information on creating contrast matrices, please see the citation linked
at the beginning of this document.
``` {r code groupComparison}
model = groupComparison("pairwise", summarized)
Model Details
``` {r Model }
head(model$ModelQC)
head(model$ComparisonResult)
1.5.1 groupComparisonPlot
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 illustrates 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.
``` {r GroupComparisonPlots}
groupComparisonPlots( model$ComparisonResult, type="Heatmap", sig = 0.05, FCcutoff = FALSE, logBase.pvalue = 10, ylimUp = FALSE, ylimDown = FALSE, xlimUp = FALSE, x.axis.size = 10, y.axis.size = 10, dot.size = 3, text.size = 4, text.angle = 0, legend.size = 13, ProteinName = TRUE, colorkey = TRUE, numProtein = 100, clustering = "both", width = 800, height = 600, which.Comparison = "all", which.Protein = "all", address = FALSE, isPlotly = FALSE )
groupComparisonPlots( model$ComparisonResult, type="VolcanoPlot", sig = 0.05, FCcutoff = FALSE, logBase.pvalue = 10, ylimUp = FALSE, ylimDown = FALSE, xlimUp = FALSE, x.axis.size = 10, y.axis.size = 10, dot.size = 3, text.size = 4, text.angle = 0, legend.size = 13, ProteinName = TRUE, colorkey = TRUE, numProtein = 100, clustering = "both", width = 800, height = 600, which.Comparison = "Condition2 vs Condition4", which.Protein = "all", address = FALSE, isPlotly = FALSE )
### __1.6 GroupComparisonQCPlots__
To check and verify that the resultant data of `groupComparison` offers a linear
model for whole plot inference, `groupComparisonQC` plots take the fitted data
and provide two ways of plotting:
1. Normal Q-Q plot : Quantile-Quantile plots represents normal quantile-quantile
plot for each protein after fitting models
2. Residual plot : represents a plot of residuals versus fitted values for each
protein in the dataset.
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.
``` {r GroupComparisonQCplots, results='hide', message=FALSE, warning=FALSE}
source("..//R//groupComparisonQCPlots.R")
groupComparisonQCPlots(data=model, type="QQPlots", address=FALSE,
which.Protein = "P0ABU9")
groupComparisonQCPlots(data=model, type="ResidualPlots", address=FALSE,
which.Protein = "P0ABU9")
1.7 Sample Size Calculation
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:
- number of biological replicates per condition
- power
sample_size_calc = designSampleSize(model$FittedModel, desiredFC=c(1.75,2.5), power = TRUE, numSample=5)
1.7.1 Sample Size Calculation Plot
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.
designSampleSizePlots(sample_size_calc, isPlotly=FALSE)
1.8 Quantification from groupComparison Data
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(RunlevelDatafromdataProcess). 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.
sample_quant_long = quantification(summarized, type = "Sample", format = "long") sample_quant_long sample_quant_wide = quantification(summarized, type = "Sample", format = "matrix") sample_quant_wide group_quant_long = quantification(summarized, type = "Group", format = "long") group_quant_long group_quant_wide = quantification(summarized, type = "Group", format = "matrix") group_quant_wide
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