Using the `sager` package to import and analyse `sage` results in R

Introduction

The `sager` package

Sage is a new cross-platform, extremely performant, open source proteomics search engine Michael Lazear. It uses mzML files as input, is parameterised by a json file and is executed from the command line. In addition to peptide identification, sage also includes a variety of advanced features such as retention time prediction and quantification (both isobaric & LFQ). It produces two files: results.sage.tsv with the identification results and, when configured to quantify features, quant.tsv.

The sager package uses these two files as input. It facilitates their import into established Bioconductor classes:

Identification results are parsed and imported as PSMatch::PSM() objects with sagePSM().
Quantitation (and identification) results are parsed, merged and imported as QFeatures::QFeatures() objects with sageQFeatures().
Sage results can also be integrated with a Spectra::Spectra() instance (holding the raw data) into a MsExperiment::MsExperiment() object, as documented in this document.

The goal of the sager package can thus be summarised as a way to leverage the existing R for Mass Spectrometry and Bioconductor infrastructure to analyse sage results.

Installation instructions

To install sager and its dependencies, you’ll need the BiocManager package, that can be installed from CRAN (unless you already have it):

if (!requireNamespace("BiocManager"))
    install.packages("BiocManager")

Note that sager is under constant development, and thus will depend on some under-development dependencies, that haven’t made it into the stable releases yet. Here are the instructions to install these:

To install the devel version of PSMatch:

BiocManager::install("RforMassSpectrometry/PSMatch")

To install sager and its dependencies, you can run the following command:

BiocManager::install("UCLouvain-CBIO/sager")

Analysing `sage` results

Example data

library(sager)

The sager package provides example identification and quantitation (procuced by sage) and the raw data (mzML files) that were used the generate the former.

sagerAvailableData()
#> quant    id  mzml 
#>  TRUE  TRUE  TRUE

Data can be added with the sagerAddData() function, that avoid downloading data files if they are already available in the package’s local cache directory:

sagerAddData()
#> 'quant' already available.
#> 'id' already available.
#> 'mzml' already available.

For details, including updating local data and their provenance, please see the ?sagerData manual page.

Identification data

As noted above, identification data is handled by the PSMatch package. This section only provides a very short example of how to use it. For more details, see the package’s website and vignettes.

library(PSMatch)
psm <- sagePSM(sagerIdData())
psm
#> PSM with 977 rows and 34 columns.
#> names(34): peptide proteins ... protein_fdr ms1_intensity

Here we briefly visualise the hyperscore and spectrum FDR densities for the forward and decoy/reverse hits:

library(ggplot2)
data.frame(psm) |>
    ggplot(aes(x = hyperscore,
               colour = label)) +
    geom_density()
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.logical(col, optional = optional): Direct call of
#> 'as.data.frame.logical()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

data.frame(psm) |>
    ggplot(aes(x = spectrum_fdr,
               colour = label)) +
    geom_density()
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.logical(col, optional = optional): Direct call of
#> 'as.data.frame.logical()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

Below, we filter the PSMs removing decoy hits, PSMs of rank > 1 (none in the small testing data), shared peptides (none in the small testing data) and those that have a spectrum FDR below 0.05:

psm <- filterPSMs(psm) |>
    filterPsmFdr()
#> Starting with 977 PSMs:
#> Removed 298 decoy hits.
#> Removed 0 PSMs with rank > 1.
#> Removed 0 shared peptides.
#> 679 PSMs left.

This leaves us with 679 PSMs matched against 481 scans.

psm
#> PSM with 679 rows and 34 columns.
#> names(34): peptide proteins ... protein_fdr ms1_intensity

Quantitative data

As noted above, quantitation data is handled by the QFeatures package. This section only provides a very short example of how to use it. For more details, see the package’s website and vignettes.

We will also make use of some function from the scp package.

library(QFeatures)
library(scp)

The quantitation data has been produced from running sage on three raw data files. These three files can correspond to two different experimental designs:

The three acquisitions correspond to three fractions of the same set of 11 multiplexed samples. In this case, that we’ll call the Multiple fractions for the same set of samples, we end up with a single quantitation matrix with 11 columns and row containing the features from all files.
The three acquisitions correspond to single acquisitions from 3 different sets of 11 samples. In this case, that we’ll call the Different sets of samples per acquisition, we end up with three quantitation matrices, each with 11 columns and rows containing the features from the respective files.

Both sitations are illustrated below using the sageQFeatures() function , and a differenciated by the value of the splitBy argument. Note that below, the byQuant argument is set to accomodate the old sage default header; this byQuant argument wouldn’t need to be set explicitly for newer output files.

Multiple fractions for the same set of samples

Let’s read the data in, specifying that the tables should not be split into different quantitative assays.

qf1 <- sageQFeatures(sagerQuantData(),
                     sagerIdData(),
                     byQuant = c("file", "scannr"),
                     splitBy = NULL)
qf1
#> An instance of class QFeatures containing 1 assays:
#>  [1] sage: SummarizedExperiment with 977 rows and 11 columns

The piped commands below implement the following worflow:

Filter decoy features (i.e. those with a label of -1).
Only keep features of rank 1.
Keep features that have a spectrum FDR smaller than 0.05.
Replace 0s by NA, to make missing values explicit.
Log-transform quantitative values.
Normalise data using median centring.
Aggregate features into protein-level quantities by computing the median and ignoring missing values.

qf1 |>
    filterFeatures(~ label > 0) |>            ## 1
    filterFeatures(~ rank == 1) |>            ## 2
    filterFeatures(~ spectrum_fdr < 0.05) |>  ## 3
    zeroIsNA(1) |>                            ## 4
    logTransform(i = 1, name = "log_sage") |> ## 5
    normalize(i = "log_sage",                 ## 6
              name = "norm_sage",
              method = "center.median") |>
    aggregateFeatures(i = "norm_sage",        ## 7
                      name = "proteins",
                      fcol = "proteins",
                      fun = colMedians,
                      na.rm = TRUE) -> qf1
#> 'label' found in 1 out of 1 assay(s)
#> 'rank' found in 1 out of 1 assay(s)
#> 'spectrum_fdr' found in 1 out of 1 assay(s)
#> Your quantitative data contain missing values. Please read the relevant
#> section(s) in the aggregateFeatures manual page regarding the effects
#> of missing values on data aggregation.

The new QFeatures object contains 4 assays:

qf1
#> An instance of class QFeatures containing 4 assays:
#>  [1] sage: SummarizedExperiment with 309 rows and 11 columns 
#>  [2] log_sage: SummarizedExperiment with 309 rows and 11 columns 
#>  [3] norm_sage: SummarizedExperiment with 309 rows and 11 columns 
#>  [4] proteins: SummarizedExperiment with 297 rows and 11 columns

The pipeline can then be visualised as shown below:

plot(qf1)

Different sets of samples per acquisition

In this second example, the quantitative data are automatically split based on the mzML files.

qf <- sageQFeatures(sagerQuantData(), sagerIdData(),
                    byQuant = c("file", "scannr"))
qf
#> An instance of class QFeatures containing 3 assays:
#>  [1] subset_dq_00084_11cell_90min_hrMS2_A5.mzML: SummarizedExperiment with 288 rows and 11 columns 
#>  [2] subset_dq_00086_11cell_90min_hrMS2_A9.mzML: SummarizedExperiment with 353 rows and 11 columns 
#>  [3] subset_dq_00087_11cell_90min_hrMS2_A11.mzML: SummarizedExperiment with 336 rows and 11 columns

Before processing the data, let’s annotate it. The renameqf() helper function will automate come of the file renaming. The annotation will be composed of the file name of origin (filename), the TMT tag (tmt_tag), and and acquisition identifier (acquisition).

renameqf <- function(x) sub("\\.mzML", "", sub("^.+hrMS2_", "", x))
qf$filename <- rep(names(qf), each = 11)
qf$tmt_tag <- rep(paste0("tmt_", 1:11), length(qf))
qf$acquisition <- renameqf(qf$filename)
names(qf) <- paste0("psm", renameqf(names(qf)))

Below, we update the assay and sample names.

qf <- renamePrimary(qf, paste(qf$acquisition, qf$tmt_tag, sep = "."))
colnames(qf) <- CharacterList(lapply(colnames(qf), renameqf))

colData(qf)
#> DataFrame with 33 rows and 3 columns
#>                 filename     tmt_tag acquisition
#>              <character> <character> <character>
#> A5.tmt_1   subset_dq_...       tmt_1          A5
#> A5.tmt_2   subset_dq_...       tmt_2          A5
#> A5.tmt_3   subset_dq_...       tmt_3          A5
#> A5.tmt_4   subset_dq_...       tmt_4          A5
#> A5.tmt_5   subset_dq_...       tmt_5          A5
#> ...                  ...         ...         ...
#> A11.tmt_7  subset_dq_...       tmt_7         A11
#> A11.tmt_8  subset_dq_...       tmt_8         A11
#> A11.tmt_9  subset_dq_...       tmt_9         A11
#> A11.tmt_10 subset_dq_...      tmt_10         A11
#> A11.tmt_11 subset_dq_...      tmt_11         A11
qf
#> An instance of class QFeatures containing 3 assays:
#>  [1] psmA5: SummarizedExperiment with 288 rows and 11 columns 
#>  [2] psmA9: SummarizedExperiment with 353 rows and 11 columns 
#>  [3] psmA11: SummarizedExperiment with 336 rows and 11 columns

The piped commands below implement the following worflow:

Filter decoy features (i.e. those with a label of -1) in all assays.
Only keep features of rank 1 in all assays.
Keep features that have a spectrum FDR smaller than 0.05 in all assays.
Replace 0s by NA in all assays, to make missing values explicit.
Log-transform quantitative values - three new assays are created from the three initial ones and named with the “log_” prefix.
Aggregate each PSMs into peptide-level quantities by computing the median and ignoring missing values. The new assays names are defined as ‘peptide’ followed by the orifinal assay identifier.
Join the 3 peptide assays into a new assay that will contain the 33 samples. Missing values are incorporated accordingly, when a peptide is observed in part of the sets.
Normalise the joined peptide data using median centring.
Aggregate the peptide-level quantities into protein by computing the median and ignoring missing values. The new assay names will be ‘protein’.

qf |>
    filterFeatures(~ label > 0) |>           ## 1
    filterFeatures(~ rank == 1) |>           ## 2
    filterFeatures(~ spectrum_fdr < 0.05) |> ## 3
    zeroIsNA(1:3) |>                         ## 4
    logTransform(i = 1:3,                    ## 5
                 name = paste0("log_", names(qf))) |>
    aggregateFeaturesOverAssays(i = 4:6,     ## 6
                                fcol = "peptide",
                                name = sub("psm", "peptide", names(qf)),
                                fun = colMedians,
                                na.rm = TRUE) |>
    joinAssays(i = 7:9,                      ## 7
               name = "peptides") |>
    normalize(i = 10,                        ## 8
              name = "norm_peptides",
              method = "center.median") |>
    aggregateFeatures(i = "norm_peptides",   ## 9
                      name = "proteins",
                      fcol = "proteins",
                      fun = colMedians,
                      na.rm = TRUE) -> qf
#> 'label' found in 3 out of 3 assay(s)
#> 'rank' found in 3 out of 3 assay(s)
#> 'spectrum_fdr' found in 3 out of 3 assay(s)
#> Aggregated: 1/3
#> Aggregated: 2/3
#> Aggregated: 3/3
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead

#> Warning in as.data.frame.numeric(col, optional = optional): Direct call of
#> 'as.data.frame.numeric()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Warning in as.data.frame.integer(col, optional = optional): Direct call of
#> 'as.data.frame.integer()' is deprecated.  Use 'as.data.frame.vector()' or
#> 'as.data.frame()' instead
#> Your quantitative data contain missing values. Please read the relevant
#> section(s) in the aggregateFeatures manual page regarding the effects
#> of missing values on data aggregation.

The final QFeatures object now contains 12 assays:

qf
#> An instance of class QFeatures containing 12 assays:
#>  [1] psmA5: SummarizedExperiment with 91 rows and 11 columns 
#>  [2] psmA9: SummarizedExperiment with 109 rows and 11 columns 
#>  [3] psmA11: SummarizedExperiment with 109 rows and 11 columns 
#>  ...
#>  [10] peptides: SummarizedExperiment with 308 rows and 33 columns 
#>  [11] norm_peptides: SummarizedExperiment with 308 rows and 33 columns 
#>  [12] proteins: SummarizedExperiment with 297 rows and 33 columns

The pipeline can then be visualised as shown below:

plot(qf)

Raw data

We have seen above how to import and process identification and quantitation data produced by sage using standard R/Bioconductor tools. Our main goal is to integrate these with the raw MS data that was used to generate them.

Let’s start by importing these raw data into R as a Spectra object. The three mzML files can be retrieved with the sagerMzMLData() function.

library(Spectra)
sp <- Spectra(sagerMzMLData())
sp
#> MSn data (Spectra) with 598 spectra in a MsBackendMzR backend:
#>       msLevel     rtime scanIndex
#>     <integer> <numeric> <integer>
#> 1           2   279.184         1
#> 2           2   731.098         2
#> 3           2   736.768         3
#> 4           2   863.610         4
#> 5           2   866.034         5
#> ...       ...       ...       ...
#> 594         2   5155.97       203
#> 595         2   5163.48       204
#> 596         2   5178.80       205
#> 597         2   5179.03       206
#> 598         2   5249.68       207
#>  ... 33 more variables/columns.
#> 
#> file(s):
#> subset_dq_00084_11cell_90min_hrMS2_A5.mzML
#> subset_dq_00086_11cell_90min_hrMS2_A9.mzML
#> subset_dq_00087_11cell_90min_hrMS2_A11.mzML

Combining raw, quantitation and identification data

Defining keys

We can now create dedicates keys to identify features in the different data produced above. Let’s start with the spectra within the sp object. Later, we will create a similar key in the QFeatures and PSM objects generated above.

The goal of these keys will be to identify matching features across data types, such as for example to match de MS spectra to the PSMs and peptides or proteins. These different data types can be stored together in an MsExperiment object, and matched through one or multiple keys.

We can add a key (by default, names .KEY that will identify a feature/scan by concatenating the scan number and the file in which that scan was acquired.

sp$filename <- basename(dataOrigin(sp))
sp <- addKey(sp, vars = c("filename", "spectrumId"))
head(sp$.KEY)
#> [1] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=1004"
#> [2] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=4181"
#> [3] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=4224"
#> [4] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=5124"
#> [5] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=5144"
#> [6] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=5517"

Let’s do the same of the PSM object created above:

psm <- addKey(psm, vars = c("filename", "scannr"))
head(psm$.KEY)
#> [1] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=31852" 
#> [2] "subset_dq_00087_11cell_90min_hrMS2_A11.mzML.controllerType=0 controllerNumber=1 scan=31961"
#> [3] "subset_dq_00087_11cell_90min_hrMS2_A11.mzML.controllerType=0 controllerNumber=1 scan=16197"
#> [4] "subset_dq_00087_11cell_90min_hrMS2_A11.mzML.controllerType=0 controllerNumber=1 scan=32666"
#> [5] "subset_dq_00086_11cell_90min_hrMS2_A9.mzML.controllerType=0 controllerNumber=1 scan=22318" 
#> [6] "subset_dq_00086_11cell_90min_hrMS2_A9.mzML.controllerType=0 controllerNumber=1 scan=11726"

And finally, with the QFeatures object.

qf <- addKey(qf, vars = c("file", "scannr"))

Running addKey on a QFeatures object will only add a key to assays that do have the relevant variables:

sapply(rowData(qf), function(x) ".KEY" %in% names(x))
#>         psmA5         psmA9        psmA11     log_psmA5     log_psmA9 
#>          TRUE          TRUE          TRUE          TRUE          TRUE 
#>    log_psmA11     peptideA5     peptideA9    peptideA11      peptides 
#>          TRUE          TRUE         FALSE          TRUE         FALSE 
#> norm_peptides      proteins 
#>         FALSE         FALSE
head(unname(rowData(qf[["psmA5"]])$.KEY))
#> [1] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=10460"
#> [2] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=10530"
#> [3] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=10648"
#> [4] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=10803"
#> [5] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=11681"
#> [6] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=11785"

Let’s now bundle these three types of data together into and MsExperiment object:

library(MsExperiment)
mse <- MsExperiment()
spectra(mse) <- sp
qdata(mse) <- qf
otherData(mse)$PSM <- psm

mse
#> Object of class MsExperiment 
#>  Spectra: MS2 (598) 
#>  QFeatures: 12 assay(s)
#>  Other data: PSM

Searching for features across data types

Each data element mse is referenced by the same key. We can now query for such keys direcly on the MsExperiment object, that will percolate the request to its components.

k <- c("subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=31852",
       "subset_dq_00087_11cell_90min_hrMS2_A11.mzML.controllerType=0 controllerNumber=1 scan=32666")

filterKey(mse, k)
#> harmonizing input:
#>   removing 132 sampleMap rows not in names(experiments)
#>   removing 11 colData rownames not in sampleMap 'primary'
#> Object of class MsExperiment 
#>  Spectra: MS2 (2) 
#>  QFeatures: 6 assay(s)
#>  Other data: PSM

It is however not convenient to search an arbitrary key. The more useful usecase would be to look for a protein of interest, for example, and then identify the features across other data types using the relevant key(s).

Let’s imagine that protein DNLI3_HUMAN is of interest to us. It is referenced as "sp|P49916|DNLI3_HUMAN" in our data. The dropEmptyAssay() simply drops assays that didn’t reference the protein of interest - it is used to simplify the output here.

qdata(mse)["sp|P49916|DNLI3_HUMAN", , ] |>
    dropEmptyAssays()
#> Warning: 'experiments' dropped; see 'drops()'
#> harmonizing input:
#>   removing 66 sampleMap rows not in names(experiments)
#> An instance of class QFeatures containing 6 assays:
#>  [1] psmA5: SummarizedExperiment with 1 rows and 11 columns 
#>  [2] log_psmA5: SummarizedExperiment with 1 rows and 11 columns 
#>  [3] peptideA5: SummarizedExperiment with 1 rows and 11 columns 
#>  [4] peptides: SummarizedExperiment with 1 rows and 33 columns 
#>  [5] norm_peptides: SummarizedExperiment with 1 rows and 33 columns 
#>  [6] proteins: SummarizedExperiment with 1 rows and 33 columns

These are features that we might be interested in tracking across all data using their key… which we can easily retrieve with the getKey() function:

qdata(mse)["sp|P49916|DNLI3_HUMAN", , ] |>
    dropEmptyAssays() |>
    getKey()
#> Warning: 'experiments' dropped; see 'drops()'
#> List of length 3
#> names(3): psmA5 log_psmA5 peptideA5

Given that getKey() is run on a QFeatures object, it returns the keys of all assays. Let’s extract the unique keys as a character:

my_key <- qdata(mse)["sp|P49916|DNLI3_HUMAN", , ] |>
    dropEmptyAssays() |>
    getKey() |>
    unlist() |>
    unique()
#> Warning: 'experiments' dropped; see 'drops()'
my_key
#> [1] "subset_dq_00084_11cell_90min_hrMS2_A5.mzML.controllerType=0 controllerNumber=1 scan=17928"

And now use mykey to subset the different component of the MsExperiment object:

my_mse <- filterKey(mse, my_key)
#> harmonizing input:
#>   removing 165 sampleMap rows not in names(experiments)
#>   removing 22 colData rownames not in sampleMap 'primary'
my_mse
#> Object of class MsExperiment 
#>  Spectra: MS2 (1) 
#>  QFeatures: 3 assay(s)
#>  Other data: PSM

From the above, we can see that DNLI3_HUMAN was identified and quantified by a single peptides, that has the following quantitation profile acorss the 11 samples…

assay(qdata(my_mse)[[3]])
#>                                  A5.tmt_1 A5.tmt_2 A5.tmt_3 A5.tmt_4 A5.tmt_5
#> [+229.1629]-SATNLPQLK[+229.1629] 18.48759 19.64292      NaN      NaN  18.4555
#>                                  A5.tmt_6 A5.tmt_7 A5.tmt_8 A5.tmt_9 A5.tmt_10
#> [+229.1629]-SATNLPQLK[+229.1629]      NaN 20.28052 18.19301      NaN  18.82892
#>                                  A5.tmt_11
#> [+229.1629]-SATNLPQLK[+229.1629]       NaN

… and that that peptides was matched by a single spectrum, shown below:

plotSpectra(spectra(my_mse))

Misc

Getting help

Please open an issue on the package’s Github repository.

Session information

#> R Under development (unstable) (2024-03-06 r86056)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] MsExperiment_1.5.4          Spectra_1.13.5             
#>  [3] ProtGenerics_1.35.3         BiocParallel_1.37.1        
#>  [5] scp_1.13.1                  QFeatures_1.13.2           
#>  [7] MultiAssayExperiment_1.29.1 SummarizedExperiment_1.33.3
#>  [9] Biobase_2.63.0              GenomicRanges_1.55.3       
#> [11] GenomeInfoDb_1.39.8         IRanges_2.37.1             
#> [13] MatrixGenerics_1.15.0       matrixStats_1.2.0          
#> [15] ggplot2_3.5.0               PSMatch_1.7.1              
#> [17] S4Vectors_0.41.4            BiocGenerics_0.49.1        
#> [19] sager_0.3.2                 BiocStyle_2.31.0           
#> 
#> loaded via a namespace (and not attached):
#>  [1] DBI_1.2.2                   bitops_1.0-7               
#>  [3] rlang_1.1.3                 magrittr_2.0.3             
#>  [5] clue_0.3-65                 compiler_4.4.0             
#>  [7] RSQLite_2.3.5               systemfonts_1.0.6          
#>  [9] vctrs_0.6.5                 pkgconfig_2.0.3            
#> [11] MetaboCoreUtils_1.11.2      crayon_1.5.2               
#> [13] fastmap_1.1.1               dbplyr_2.4.0               
#> [15] XVector_0.43.1              labeling_0.4.3             
#> [17] utf8_1.2.4                  rmarkdown_2.26             
#> [19] ragg_1.2.7                  purrr_1.0.2                
#> [21] bit_4.0.5                   xfun_0.42                  
#> [23] zlibbioc_1.49.0             cachem_1.0.8               
#> [25] jsonlite_1.8.8              blob_1.2.4                 
#> [27] highr_0.10                  DelayedArray_0.29.9        
#> [29] parallel_4.4.0              cluster_2.1.6              
#> [31] R6_2.5.1                    bslib_0.6.1                
#> [33] jquerylib_0.1.4             Rcpp_1.0.12                
#> [35] bookdown_0.38               knitr_1.45                 
#> [37] BiocBaseUtils_1.5.1         Matrix_1.6-5               
#> [39] igraph_2.0.2                tidyselect_1.2.1           
#> [41] abind_1.4-5                 yaml_2.3.8                 
#> [43] codetools_0.2-19            curl_5.2.1                 
#> [45] lattice_0.22-5              tibble_3.2.1               
#> [47] withr_3.0.0                 evaluate_0.23              
#> [49] desc_1.4.3                  BiocFileCache_2.11.1       
#> [51] pillar_1.9.0                BiocManager_1.30.22        
#> [53] filelock_1.0.3              ncdf4_1.22                 
#> [55] generics_0.1.3              RCurl_1.98-1.14            
#> [57] munsell_0.5.0               scales_1.3.0               
#> [59] glue_1.7.0                  lazyeval_0.2.2             
#> [61] tools_4.4.0                 mzR_2.37.2                 
#> [63] fs_1.6.3                    grid_4.4.0                 
#> [65] MsCoreUtils_1.15.4          colorspace_2.1-0           
#> [67] SingleCellExperiment_1.25.0 GenomeInfoDbData_1.2.11    
#> [69] cli_3.6.2                   textshaping_0.3.7          
#> [71] fansi_1.0.6                 S4Arrays_1.3.6             
#> [73] dplyr_1.1.4                 AnnotationFilter_1.27.0    
#> [75] gtable_0.3.4                sass_0.4.8                 
#> [77] digest_0.6.35               SparseArray_1.3.4          
#> [79] htmlwidgets_1.6.4           farver_2.1.1               
#> [81] memoise_2.0.1               htmltools_0.5.7            
#> [83] pkgdown_2.0.7.9000          lifecycle_1.0.4            
#> [85] httr_1.4.7                  bit64_4.0.5                
#> [87] MASS_7.3-60.2

Laurent Gatto

12 March 2024

Introduction

The `sager` package

Installation instructions

Analysing `sage` results

Example data

Identification data

Quantitative data

Multiple fractions for the same set of samples

Different sets of samples per acquisition

Raw data

Combining raw, quantitation and identification data

Defining keys

Searching for features across data types

Misc

Getting help

Session information

Using the sager package to import and analyse sage results in R

Laurent Gatto

12 March 2024

Introduction

The sager package

Installation instructions

Analysing sage results

Example data

Identification data

Quantitative data

Multiple fractions for the same set of samples

Different sets of samples per acquisition

Raw data

Combining raw, quantitation and identification data

Defining keys

Searching for features across data types

Misc

Getting help

Session information

Using the `sager` package to import and analyse `sage` results in R

The `sager` package

Analysing `sage` results