In const-ae/proDD: Identifying Differentially Abundant Proteins from Label-Free Mass Spectrometry Data
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
After analyzing your samples on a mass spectrometer, you want to find out if
there are interesting patterns in the data. But the first challenge is how
do you get the data from the files that your machine produces into R?
In the following, I will describe several ways of importing data from MaxQuant.
MaxQuant is a popular tool for identifying, integrating, and combining MS peaks
to derive peptide and protein intensities. MaxQuant produces serveral output
files including proteinGroups.txt. It is usually a tab separated table with
a lot of different columns, which can make it difficult to not get overwhelmed
with information.
The most important columns that every proteinGroups.txt file contains are
-
Protein IDs: a semicolon delimited text listing all protein identifiers
that match an identified set of peptides. Most of the time this is just a
single protein, but sometimes proteins are so similar to each other because
of gene duplication that it was not possible to differentiate them.
-
Majority protein IDs: a semicolon delimited text that lists all proteins
from the Protein IDs column which had more than half of their peptides
identified.
-
Identification type [SAMPLENAME]: For each sample there is one column that
explains how the peptide peaks where identified. Either they were directly
sequenced by the MS2 ("By MS/MS") or by matching the m/z peak and elution timing
across samples ("By matching").
-
Intensity [SAMPLENAME]: The combined intensity of the peptides of the protein.
Missing or non-identified proteins are simply stored as 0. In a label-free
experiment, this is also often called LFQ Intensity [SAMPLENAME].
-
iBAQ [SAMPLENAME]: iBAQ is short for intensity-based absolute quantification.
It is an attempt to make intensity values comparable across proteins. Usually
the intensity values are only relative, which means that they are only
comparable within one peptide. This is because differences in ionization
and detection efficiency. For this is reason it is usually better to just
compare the Intensity for finding differentially detected proteins.
-
Only identified by site: Contains a "+" if the protein was only identified by
a modification site.
Reverse: Contains a "+" if the protein matches the reversed part of the
decoy database.
Contaminant: Contains a "+" if the protein is a commonly occurring
contaminant.
These three columns are commonly used to filter out false hits.
Our goal is to turn this complicated matrix into a useable matrix or a
SummarizedExperiment object. There are several ways to achieve this, which I
will explain in greater detail:
- Use the base R functions (
read.delim() and [<-) to read in the data
- Use the
tidyverse to load the file and turn it into a useable object
- Use the
proteus package and the readProteinGroups() function
- Use the
DEP package and the import_MaxQuant() function
This package comes with an example file that contains the LFQ data from a BioID
experiment in Drosophila melanogaster. 11 different Palmitoyltransferases
(short DHHC) were tagged with a promiscuous biotin ligase and all biotinylated
proteins were enriched and identified using label-free mass spectrometry. The
conditions are named after the tagged DHHC and the negetive control condition
is called S2R for the cell line. Each condition was measured in triplicates,
which means that there are a total of 36 conditions. To make the file smaller,
I provide a reduced data set which only contains the first 122 rows of the data.
Base R
The example file is located in
system.file("extdata/proteinGroups.txt",
package = "proDD", mustWork = TRUE)
In this specific file, all spaces have been replaced with dots. This is an
example how each output file from MaxQuant slightly differs. This can make it
difficult to write a generic import function. Instead I will first demonstrate
the most general approach which is to simply use the base R tools for loading
the data and turning it into useful objects.
The first step is to load the full table.
full_data <- read.delim(
system.file("extdata/proteinGroups.txt",
package = "proDD", mustWork = TRUE),
stringsAsFactors = FALSE
)
head(colnames(full_data))
Next, I create a matrix of the intensity data, where each sample is a column and
each protein group is a row.
# I use a regular expression (regex) to select the intensity columns
intensity_colnames <- colnames(full_data)[grepl("^LFQ\\.intensity\\.",
colnames(full_data))]
# New data set which only contains the intensity columns
data <- as.matrix(full_data[, intensity_colnames])
colnames(data) <- sub("^LFQ\\.intensity\\.", "", intensity_colnames)
data[data == 0] <- NA
data <- log2(data)
data[1:7, 1:6]
rownames(data) <- full_data$Protein.IDs
In the next step I will create an annotation data.frame that contains information
on the sample name, the condition and the replicate.
annotation_df <- data.frame(
Condition = sub("\\.\\d+", "", sub("^LFQ\\.intensity\\.",
"", intensity_colnames)),
Replicate = as.numeric(sub("^LFQ\\.intensity\\.[[:alnum:]]+\\.",
"", intensity_colnames)),
stringsAsFactors = FALSE, row.names = colnames(data)
)
head(annotation_df)
Optionally, we can turn this into a SummarizedExperiment or MSnSet object
library(SummarizedExperiment)
library(MSnbase)
library(SummarizedExperiment)
se <- SummarizedExperiment(SimpleList(LFQ=data), colData=annotation_df)
rowData(se) <- full_data[, c("Only.identified.by.site",
"Reverse", "Potential.contaminant")]
se
library(MSnbase)
fData <- AnnotatedDataFrame(full_data[, c("Only.identified.by.site",
"Reverse", "Potential.contaminant")])
rownames(fData) <- rownames(data)
ms <- MSnSet(data, pData=AnnotatedDataFrame(annotation_df), fData=fData)
ms
Tidyverse
The tidyverse is a set of coherent R packages that provide many useful functions
for common data analysis tasks. It replicates many of the functionalities
already available in base R packages, but learns from its mistakes and avoids
some of the suprising behaviors. For example strings are never automatically
converted to factors. Another popular feature in the tidyverse is the pipe
operator (%>%) that makes it easy to chain complex transformations.
library(dplyr)
library(stringr)
library(readr)
library(tidyr)
library(tibble)
library(dplyr)
library(stringr)
library(readr)
library(tidyr)
library(tibble)
# Or short
# library(tidyverse)
I first load the full data file
# The read_tsv function works faster and more reliable than read.delim
# But it sometimes needs help to identify the right type for each column,
# because it looks only at the first 1,000 elements.
# I do that here for the Reverse column.
full_data <- read_tsv(
system.file("extdata/proteinGroups.txt",
package = "proDD", mustWork = TRUE),
col_types = cols(Reverse = col_character())
)
full_data
Next, I create a tidy version of the data set. I pipe (%>%) the
results from each transformation to the next transformation, to
first select the columns of interest, reshape (gather) the dataset from
wide to long format, and lastly create new columns with mutate.
tidy_data <- full_data %>%
dplyr::select(ProteinID=Protein.IDs, starts_with("LFQ.intensity.")) %>%
gather(Sample, Intensity, starts_with("LFQ.intensity.")) %>%
mutate(Condition = str_match(Sample,
"LFQ\\.intensity\\.([[:alnum:]]+)\\.\\d+")[,2]) %>%
mutate(Replicate = as.numeric(str_match(Sample,
"LFQ\\.intensity\\.[[:alnum:]]+\\.(\\d+)")[,2])) %>%
mutate(SampleName = paste0(Condition, ".", Replicate))
tidy_data
Using the tidy data, I create the annotation data frame and the data matrix.
data <- tidy_data %>%
mutate(Intensity = ifelse(Intensity == 0, NA, log2(Intensity))) %>%
select(ProteinID, SampleName, Intensity) %>%
spread(SampleName, Intensity) %>%
column_to_rownames("ProteinID") %>%
as.matrix()
annotation_df <- tidy_data %>%
select(SampleName, Condition, Replicate) %>%
distinct() %>%
arrange(Condition, Replicate) %>%
as.data.frame() %>%
column_to_rownames("SampleName")
Optionally, we can again turn this into a SummarizedExperiment or MSnSet object
library(SummarizedExperiment)
se <- SummarizedExperiment(SimpleList(LFQ=data), colData=annotation_df)
rowData(se) <- full_data[, c("Only.identified.by.site",
"Reverse", "Potential.contaminant")]
se
library(MSnbase)
fData <- AnnotatedDataFrame(full_data[, c("Only.identified.by.site",
"Reverse", "Potential.contaminant")])
rownames(fData) <- rownames(data)
ms <- MSnSet(data, pData=AnnotatedDataFrame(annotation_df), fData=fData)
ms
Proteus
Proteus is an R package specifically designed for the analysis of MaxQuant
output. But as the package is not yet on CRAN, I cannot run the code
library(proteus)
To load the data I use the readProteinGroups() function. It needs a vector
of all column names that contain the intensity values, which are stored in the
meta data frame.
meta <- data.frame(
sample = c("LFQ.intensity.CG1407.01", "LFQ.intensity.CG1407.02", "LFQ.intensity.CG1407.03", "LFQ.intensity.CG4676.01", "LFQ.intensity.CG4676.02", "LFQ.intensity.CG4676.03", "LFQ.intensity.CG51963.01", "LFQ.intensity.CG51963.02", "LFQ.intensity.CG51963.03","LFQ.intensity.CG5620A.01", "LFQ.intensity.CG5620A.02", "LFQ.intensity.CG5620A.03", "LFQ.intensity.CG5620B.01","LFQ.intensity.CG5620B.02", "LFQ.intensity.CG5620B.03", "LFQ.intensity.CG5880.01", "LFQ.intensity.CG5880.02", "LFQ.intensity.CG5880.03", "LFQ.intensity.CG6017.01", "LFQ.intensity.CG6017.02", "LFQ.intensity.CG6017.03", "LFQ.intensity.CG6618.01", "LFQ.intensity.CG6618.02", "LFQ.intensity.CG6618.03", "LFQ.intensity.CG6627.01", "LFQ.intensity.CG6627.02", "LFQ.intensity.CG6627.03", "LFQ.intensity.CG8314.01", "LFQ.intensity.CG8314.02", "LFQ.intensity.CG8314.03", "LFQ.intensity.GsbPI.001", "LFQ.intensity.GsbPI.002", "LFQ.intensity.GsbPI.003", "LFQ.intensity.S2R.01", "LFQ.intensity.S2R.02", "LFQ.intensity.S2R.03"),
condition = c("CG1407", "CG1407", "CG1407", "CG4676", "CG4676", "CG4676", "CG51963", "CG51963", "CG51963", "CG5620A", "CG5620A", "CG5620A", "CG5620B", "CG5620B", "CG5620B", "CG5880", "CG5880", "CG5880", "CG6017", "CG6017", "CG6017", "CG6618", "CG6618", "CG6618", "CG6627", "CG6627", "CG6627", "CG8314", "CG8314", "CG8314", "GsbPI", "GsbPI", "GsbPI", "S2R", "S2R", "S2R" ),
stringsAsFactors = FALSE
)
head(meta)
# The columns in the example file are separated with a dot instead of a space
data_col_names <- list(protein = "Majority.protein.IDs", reverse = "Reverse",
contaminant = "Potential.contaminant")
proteus_data <- readProteinGroups(
file = system.file("extdata/proteinGroups.txt",
package = "proDD", mustWork = TRUE),
meta = meta, measure.cols = setNames(meta$sample, meta$sample),
data.cols = data_col_names)
After loading the data I turn the proteusData object into a useable matrix
and create the annotation data frame.
data <- proteus_data$tab
data <- log2(data)
annotation_df <- proteus_data$metadata
rownames(annotation_df) <- annotation_df$sample
annotation_df$sample <- NULL
Optionally, we can again turn this into a SummarizedExperiment or MSnSet object
library(SummarizedExperiment)
se <- SummarizedExperiment(SimpleList(LFQ=data), colData=annotation_df)
se
library(MSnbase)
fData <- AnnotatedDataFrame(data.frame(row.names = rownames(data)))
ms <- MSnSet(data, pData=AnnotatedDataFrame(annotation_df), fData=fData)
ms
DEP
DEP has a similar motivation as proteus. It provides helper functions
to impute missing values and makes it easy to call limma on the result.
To load the data, we again need to provide all the column names of the
intensity values. I then call the import_MaxQuant() function that directly
creates a SummarizedExperiment object.
library(DEP)
full_data <- read.delim(
system.file("extdata/proteinGroups.txt",
package = "proDD", mustWork = TRUE),
stringsAsFactors = FALSE
)
exp_design <- data.frame(
label =c("LFQ.intensity.CG1407.01", "LFQ.intensity.CG1407.02", "LFQ.intensity.CG1407.03", "LFQ.intensity.CG4676.01", "LFQ.intensity.CG4676.02", "LFQ.intensity.CG4676.03", "LFQ.intensity.CG51963.01", "LFQ.intensity.CG51963.02", "LFQ.intensity.CG51963.03","LFQ.intensity.CG5620A.01", "LFQ.intensity.CG5620A.02", "LFQ.intensity.CG5620A.03", "LFQ.intensity.CG5620B.01","LFQ.intensity.CG5620B.02", "LFQ.intensity.CG5620B.03", "LFQ.intensity.CG5880.01", "LFQ.intensity.CG5880.02", "LFQ.intensity.CG5880.03", "LFQ.intensity.CG6017.01", "LFQ.intensity.CG6017.02", "LFQ.intensity.CG6017.03", "LFQ.intensity.CG6618.01", "LFQ.intensity.CG6618.02", "LFQ.intensity.CG6618.03", "LFQ.intensity.CG6627.01", "LFQ.intensity.CG6627.02", "LFQ.intensity.CG6627.03", "LFQ.intensity.CG8314.01", "LFQ.intensity.CG8314.02", "LFQ.intensity.CG8314.03", "LFQ.intensity.GsbPI.001", "LFQ.intensity.GsbPI.002", "LFQ.intensity.GsbPI.003", "LFQ.intensity.S2R.01", "LFQ.intensity.S2R.02", "LFQ.intensity.S2R.03"),
condition = c("CG1407", "CG1407", "CG1407", "CG4676", "CG4676", "CG4676", "CG51963", "CG51963", "CG51963", "CG5620A", "CG5620A", "CG5620A", "CG5620B", "CG5620B", "CG5620B", "CG5880", "CG5880", "CG5880", "CG6017", "CG6017", "CG6017", "CG6618", "CG6618", "CG6618", "CG6627", "CG6627", "CG6627", "CG8314", "CG8314", "CG8314", "GsbPI", "GsbPI", "GsbPI", "S2R", "S2R", "S2R" ),
replicate = rep(1:3, times=12),
stringsAsFactors = FALSE
)
se <- import_MaxQuant(full_data, exp_design)
se
assay(se)[1:5, 1:5]
const-ae/proDD documentation built on Jan. 14, 2020, 9:34 a.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
After analyzing your samples on a mass spectrometer, you want to find out if there are interesting patterns in the data. But the first challenge is how do you get the data from the files that your machine produces into R?
In the following, I will describe several ways of importing data from MaxQuant. MaxQuant is a popular tool for identifying, integrating, and combining MS peaks to derive peptide and protein intensities. MaxQuant produces serveral output files including proteinGroups.txt. It is usually a tab separated table with a lot of different columns, which can make it difficult to not get overwhelmed with information.
The most important columns that every proteinGroups.txt file contains are
-
Protein IDs: a semicolon delimited text listing all protein identifiers that match an identified set of peptides. Most of the time this is just a single protein, but sometimes proteins are so similar to each other because of gene duplication that it was not possible to differentiate them.
-
Majority protein IDs: a semicolon delimited text that lists all proteins from the Protein IDs column which had more than half of their peptides identified.
-
Identification type [SAMPLENAME]: For each sample there is one column that explains how the peptide peaks where identified. Either they were directly sequenced by the MS2 ("By MS/MS") or by matching the m/z peak and elution timing across samples ("By matching").
-
Intensity [SAMPLENAME]: The combined intensity of the peptides of the protein. Missing or non-identified proteins are simply stored as
0. In a label-free experiment, this is also often called LFQ Intensity [SAMPLENAME]. -
iBAQ [SAMPLENAME]: iBAQ is short for intensity-based absolute quantification. It is an attempt to make intensity values comparable across proteins. Usually the intensity values are only relative, which means that they are only comparable within one peptide. This is because differences in ionization and detection efficiency. For this is reason it is usually better to just compare the Intensity for finding differentially detected proteins.
-
Only identified by site: Contains a "+" if the protein was only identified by a modification site.
Reverse: Contains a "+" if the protein matches the reversed part of the decoy database.
Contaminant: Contains a "+" if the protein is a commonly occurring contaminant.
These three columns are commonly used to filter out false hits.
Our goal is to turn this complicated matrix into a useable matrix or a
SummarizedExperiment object. There are several ways to achieve this, which I
will explain in greater detail:
- Use the base R functions (
read.delim()and[<-) to read in the data - Use the
tidyverseto load the file and turn it into a useable object - Use the
proteuspackage and thereadProteinGroups()function - Use the
DEPpackage and theimport_MaxQuant()function
This package comes with an example file that contains the LFQ data from a BioID experiment in Drosophila melanogaster. 11 different Palmitoyltransferases (short DHHC) were tagged with a promiscuous biotin ligase and all biotinylated proteins were enriched and identified using label-free mass spectrometry. The conditions are named after the tagged DHHC and the negetive control condition is called S2R for the cell line. Each condition was measured in triplicates, which means that there are a total of 36 conditions. To make the file smaller, I provide a reduced data set which only contains the first 122 rows of the data.
Base R
The example file is located in
system.file("extdata/proteinGroups.txt", package = "proDD", mustWork = TRUE)
In this specific file, all spaces have been replaced with dots. This is an example how each output file from MaxQuant slightly differs. This can make it difficult to write a generic import function. Instead I will first demonstrate the most general approach which is to simply use the base R tools for loading the data and turning it into useful objects.
The first step is to load the full table.
full_data <- read.delim( system.file("extdata/proteinGroups.txt", package = "proDD", mustWork = TRUE), stringsAsFactors = FALSE ) head(colnames(full_data))
Next, I create a matrix of the intensity data, where each sample is a column and each protein group is a row.
# I use a regular expression (regex) to select the intensity columns intensity_colnames <- colnames(full_data)[grepl("^LFQ\\.intensity\\.", colnames(full_data))] # New data set which only contains the intensity columns data <- as.matrix(full_data[, intensity_colnames]) colnames(data) <- sub("^LFQ\\.intensity\\.", "", intensity_colnames) data[data == 0] <- NA data <- log2(data) data[1:7, 1:6] rownames(data) <- full_data$Protein.IDs
In the next step I will create an annotation data.frame that contains information
on the sample name, the condition and the replicate.
annotation_df <- data.frame( Condition = sub("\\.\\d+", "", sub("^LFQ\\.intensity\\.", "", intensity_colnames)), Replicate = as.numeric(sub("^LFQ\\.intensity\\.[[:alnum:]]+\\.", "", intensity_colnames)), stringsAsFactors = FALSE, row.names = colnames(data) ) head(annotation_df)
Optionally, we can turn this into a SummarizedExperiment or MSnSet object
library(SummarizedExperiment) library(MSnbase)
library(SummarizedExperiment) se <- SummarizedExperiment(SimpleList(LFQ=data), colData=annotation_df) rowData(se) <- full_data[, c("Only.identified.by.site", "Reverse", "Potential.contaminant")] se
library(MSnbase) fData <- AnnotatedDataFrame(full_data[, c("Only.identified.by.site", "Reverse", "Potential.contaminant")]) rownames(fData) <- rownames(data) ms <- MSnSet(data, pData=AnnotatedDataFrame(annotation_df), fData=fData) ms
Tidyverse
The tidyverse is a set of coherent R packages that provide many useful functions
for common data analysis tasks. It replicates many of the functionalities
already available in base R packages, but learns from its mistakes and avoids
some of the suprising behaviors. For example strings are never automatically
converted to factors. Another popular feature in the tidyverse is the pipe
operator (%>%) that makes it easy to chain complex transformations.
library(dplyr) library(stringr) library(readr) library(tidyr) library(tibble)
library(dplyr) library(stringr) library(readr) library(tidyr) library(tibble) # Or short # library(tidyverse)
I first load the full data file
# The read_tsv function works faster and more reliable than read.delim # But it sometimes needs help to identify the right type for each column, # because it looks only at the first 1,000 elements. # I do that here for the Reverse column. full_data <- read_tsv( system.file("extdata/proteinGroups.txt", package = "proDD", mustWork = TRUE), col_types = cols(Reverse = col_character()) ) full_data
Next, I create a tidy version of the data set. I pipe (%>%) the
results from each transformation to the next transformation, to
first select the columns of interest, reshape (gather) the dataset from
wide to long format, and lastly create new columns with mutate.
tidy_data <- full_data %>% dplyr::select(ProteinID=Protein.IDs, starts_with("LFQ.intensity.")) %>% gather(Sample, Intensity, starts_with("LFQ.intensity.")) %>% mutate(Condition = str_match(Sample, "LFQ\\.intensity\\.([[:alnum:]]+)\\.\\d+")[,2]) %>% mutate(Replicate = as.numeric(str_match(Sample, "LFQ\\.intensity\\.[[:alnum:]]+\\.(\\d+)")[,2])) %>% mutate(SampleName = paste0(Condition, ".", Replicate)) tidy_data
Using the tidy data, I create the annotation data frame and the data matrix.
data <- tidy_data %>% mutate(Intensity = ifelse(Intensity == 0, NA, log2(Intensity))) %>% select(ProteinID, SampleName, Intensity) %>% spread(SampleName, Intensity) %>% column_to_rownames("ProteinID") %>% as.matrix() annotation_df <- tidy_data %>% select(SampleName, Condition, Replicate) %>% distinct() %>% arrange(Condition, Replicate) %>% as.data.frame() %>% column_to_rownames("SampleName")
Optionally, we can again turn this into a SummarizedExperiment or MSnSet object
library(SummarizedExperiment) se <- SummarizedExperiment(SimpleList(LFQ=data), colData=annotation_df) rowData(se) <- full_data[, c("Only.identified.by.site", "Reverse", "Potential.contaminant")] se
library(MSnbase) fData <- AnnotatedDataFrame(full_data[, c("Only.identified.by.site", "Reverse", "Potential.contaminant")]) rownames(fData) <- rownames(data) ms <- MSnSet(data, pData=AnnotatedDataFrame(annotation_df), fData=fData) ms
Proteus
Proteus is an R package specifically designed for the analysis of MaxQuant output. But as the package is not yet on CRAN, I cannot run the code
library(proteus)
To load the data I use the readProteinGroups() function. It needs a vector
of all column names that contain the intensity values, which are stored in the
meta data frame.
meta <- data.frame( sample = c("LFQ.intensity.CG1407.01", "LFQ.intensity.CG1407.02", "LFQ.intensity.CG1407.03", "LFQ.intensity.CG4676.01", "LFQ.intensity.CG4676.02", "LFQ.intensity.CG4676.03", "LFQ.intensity.CG51963.01", "LFQ.intensity.CG51963.02", "LFQ.intensity.CG51963.03","LFQ.intensity.CG5620A.01", "LFQ.intensity.CG5620A.02", "LFQ.intensity.CG5620A.03", "LFQ.intensity.CG5620B.01","LFQ.intensity.CG5620B.02", "LFQ.intensity.CG5620B.03", "LFQ.intensity.CG5880.01", "LFQ.intensity.CG5880.02", "LFQ.intensity.CG5880.03", "LFQ.intensity.CG6017.01", "LFQ.intensity.CG6017.02", "LFQ.intensity.CG6017.03", "LFQ.intensity.CG6618.01", "LFQ.intensity.CG6618.02", "LFQ.intensity.CG6618.03", "LFQ.intensity.CG6627.01", "LFQ.intensity.CG6627.02", "LFQ.intensity.CG6627.03", "LFQ.intensity.CG8314.01", "LFQ.intensity.CG8314.02", "LFQ.intensity.CG8314.03", "LFQ.intensity.GsbPI.001", "LFQ.intensity.GsbPI.002", "LFQ.intensity.GsbPI.003", "LFQ.intensity.S2R.01", "LFQ.intensity.S2R.02", "LFQ.intensity.S2R.03"), condition = c("CG1407", "CG1407", "CG1407", "CG4676", "CG4676", "CG4676", "CG51963", "CG51963", "CG51963", "CG5620A", "CG5620A", "CG5620A", "CG5620B", "CG5620B", "CG5620B", "CG5880", "CG5880", "CG5880", "CG6017", "CG6017", "CG6017", "CG6618", "CG6618", "CG6618", "CG6627", "CG6627", "CG6627", "CG8314", "CG8314", "CG8314", "GsbPI", "GsbPI", "GsbPI", "S2R", "S2R", "S2R" ), stringsAsFactors = FALSE ) head(meta) # The columns in the example file are separated with a dot instead of a space data_col_names <- list(protein = "Majority.protein.IDs", reverse = "Reverse", contaminant = "Potential.contaminant") proteus_data <- readProteinGroups( file = system.file("extdata/proteinGroups.txt", package = "proDD", mustWork = TRUE), meta = meta, measure.cols = setNames(meta$sample, meta$sample), data.cols = data_col_names)
After loading the data I turn the proteusData object into a useable matrix
and create the annotation data frame.
data <- proteus_data$tab data <- log2(data) annotation_df <- proteus_data$metadata rownames(annotation_df) <- annotation_df$sample annotation_df$sample <- NULL
Optionally, we can again turn this into a SummarizedExperiment or MSnSet object
library(SummarizedExperiment) se <- SummarizedExperiment(SimpleList(LFQ=data), colData=annotation_df) se
library(MSnbase) fData <- AnnotatedDataFrame(data.frame(row.names = rownames(data))) ms <- MSnSet(data, pData=AnnotatedDataFrame(annotation_df), fData=fData) ms
DEP
DEP has a similar motivation as proteus. It provides helper functions
to impute missing values and makes it easy to call limma on the result.
To load the data, we again need to provide all the column names of the
intensity values. I then call the import_MaxQuant() function that directly
creates a SummarizedExperiment object.
library(DEP) full_data <- read.delim( system.file("extdata/proteinGroups.txt", package = "proDD", mustWork = TRUE), stringsAsFactors = FALSE ) exp_design <- data.frame( label =c("LFQ.intensity.CG1407.01", "LFQ.intensity.CG1407.02", "LFQ.intensity.CG1407.03", "LFQ.intensity.CG4676.01", "LFQ.intensity.CG4676.02", "LFQ.intensity.CG4676.03", "LFQ.intensity.CG51963.01", "LFQ.intensity.CG51963.02", "LFQ.intensity.CG51963.03","LFQ.intensity.CG5620A.01", "LFQ.intensity.CG5620A.02", "LFQ.intensity.CG5620A.03", "LFQ.intensity.CG5620B.01","LFQ.intensity.CG5620B.02", "LFQ.intensity.CG5620B.03", "LFQ.intensity.CG5880.01", "LFQ.intensity.CG5880.02", "LFQ.intensity.CG5880.03", "LFQ.intensity.CG6017.01", "LFQ.intensity.CG6017.02", "LFQ.intensity.CG6017.03", "LFQ.intensity.CG6618.01", "LFQ.intensity.CG6618.02", "LFQ.intensity.CG6618.03", "LFQ.intensity.CG6627.01", "LFQ.intensity.CG6627.02", "LFQ.intensity.CG6627.03", "LFQ.intensity.CG8314.01", "LFQ.intensity.CG8314.02", "LFQ.intensity.CG8314.03", "LFQ.intensity.GsbPI.001", "LFQ.intensity.GsbPI.002", "LFQ.intensity.GsbPI.003", "LFQ.intensity.S2R.01", "LFQ.intensity.S2R.02", "LFQ.intensity.S2R.03"), condition = c("CG1407", "CG1407", "CG1407", "CG4676", "CG4676", "CG4676", "CG51963", "CG51963", "CG51963", "CG5620A", "CG5620A", "CG5620A", "CG5620B", "CG5620B", "CG5620B", "CG5880", "CG5880", "CG5880", "CG6017", "CG6017", "CG6017", "CG6618", "CG6618", "CG6618", "CG6627", "CG6627", "CG6627", "CG8314", "CG8314", "CG8314", "GsbPI", "GsbPI", "GsbPI", "S2R", "S2R", "S2R" ), replicate = rep(1:3, times=12), stringsAsFactors = FALSE ) se <- import_MaxQuant(full_data, exp_design) se assay(se)[1:5, 1:5]
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