Package 'PSMatch'

Description

There are two ways that peptide/protein matches are commonly stored: either as a vector or an adjacency matrix. The functions described below convert between these two format.

Usage

makeAdjacencyMatrix(
  x,
  split = ";",
  peptide = psmVariables(x)["peptide"],
  protein = psmVariables(x)["protein"],
  score = psmVariables(x)["score"],
  binary = FALSE
)

makePeptideProteinVector(m, collapse = ";")

plotAdjacencyMatrix(
  m,
  protColors = 0,
  pepColors = NULL,
  layout = igraph::layout_nicely
)

Arguments

Details

The makeAdjacencyMatrix() function creates a peptide-by-protein adjacency matrix from a character or an instance of class PSM().

The character is formatted as x <- c("ProtA", "ProtB", "ProtA;ProtB", ...), as commonly encountered in proteomics data spreadsheets. It defines that the first peptide is mapped to protein "ProtA", the second one to protein "ProtB", the third one to "ProtA" and "ProtB", and so on. The resulting matrix contains as many rows as there are unique peptides and as many columns as there are unique protein identifiers in x. The columns are named after the protein identifiers and the peptide/protein vector names are used to name the matrix rows (retaining only the unique names).

The makePeptideProteinVector() function does the opposite operation, taking an adjacency matrix as input and returning a peptide/protein vector. The matrix colnames are used to populate the vector and the matrix rownames are used to name the vector elements.

Note that when creating an adjacency matrix from a PSM object, the matrix is not necessarily binary, as multiple PSMs can match the same peptide (sequence), such as for example precursors with different charge states. A binary matrix can either be generated with the binary argument (setting all non-0 values to 1) or by reducing the PSM object accordingly (see example below).

It is also possible to generate adjacency matrices populated with identification scores or probabilites by setting the "score" PSM variable upon construction of the PSM object (see PSM() for details). In case multiple PSMs occur, their respective scores get summed.

The plotAdjacencyMatrix() function is useful to visualise small adjacency matrices, such as those representing protein groups modelled as connected components, as described and illustrated in ConnectedComponents(). The function generates a graph modelling the relation between proteins (represented as squares) and peptides (represented as circes), which can further be coloured (see the protColors and pepColors arguments). The function invisibly returns the graph igraph object for additional tuning and/or interactive visualisation using, for example igraph::tkplot().

Such as illustrated in the examples below, each row/peptide is expected to refer to protein groups or individual proteins (groups of size 1). These have to be split accordingly.

Value

A peptide-by-protein sparce adjacency matrix (or class dgCMatrix as defined in the Matrix package) or peptide/protein vector.

Author(s)

Laurent Gatto

Examples

## -----------------------
## From a character
## -----------------------

## Protein vector without names
prots <- c("ProtA", "ProtB", "ProtA;ProtB")
makeAdjacencyMatrix(prots)

## Named protein vector
names(prots) <- c("pep1", "pep2", "pep3")
prots
m <- makeAdjacencyMatrix(prots)
m

## Back to vector
vec <- makePeptideProteinVector(m)
vec
identical(prots, vec)

## ----------------------------
## PSM object from a data.frame

## ----------------------------
## Case 1: Duplicate identifications

psmdf <- data.frame(psm = paste0("psm", 1:10),
                    peptide = paste0("pep", c(1, 1, 2, 2, 3, 4, 6, 7, 8, 8)),
                    protein = paste0("Prot", LETTERS[c(1, 1, 2, 2, 3, 4, 3, 5, 6, 6)]))
psmdf
psm <- PSM(psmdf, peptide = "peptide", protein = "protein")
psm
makeAdjacencyMatrix(psm)

## Reduce PSM object to peptides
rpsm <- reducePSMs(psm, k = psm$peptide)
rpsm
makeAdjacencyMatrix(rpsm)

## Or set binary to TRUE
makeAdjacencyMatrix(psm, binary = TRUE)

## ----------------------------
## Case 2: Protein groups are separated by a semicolon
psmdf <- data.frame(psm = paste0("psm", 1:5),
                    peptide = paste0("pep", c(1, 2, 3, 4, 5)),
                    protein = c("ProtA", "ProtB;ProtD", "ProtA;ProtC",
                                "ProtC", "ProtA;ProtC;ProtD"))
psmdf
psm <- PSM(psmdf, peptide = "peptide", protein = "protein")
psm
makeAdjacencyMatrix(psm, split = ";")

## ----------------------------
## PSM object from an mzid file
## ----------------------------

f <- MsDataHub::TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.20141210.mzid()

psm <- PSM(f) |>
       filterPsmDecoy() |>
       filterPsmRank()
psm
adj <- makeAdjacencyMatrix(psm)
dim(adj)
adj[1:10, 1:4]

## Binary adjacency matrix
adj <- makeAdjacencyMatrix(psm, binary = TRUE)
adj[1:10, 1:4]

## Peptides with rowSums > 1 match multiple proteins.
## Use filterPsmShared() to filter these out.
table(Matrix::rowSums(adj))

## -----------------------------------------------
## Binary, non-binary and score adjacency matrices
## -----------------------------------------------

## -------------------------------------
## Case 1: no scores, 1 PSM per peptides
psmdf <- data.frame(spectrum = c("sp1", "sp2", "sp3", "sp4", "sp5",
                                 "sp6", "sp7", "sp8", "sp9", "sp10"),
                    sequence = c("NKAVRTYHEQ", "IYNHSQGFCA", "YHWRLPVSEF",
                                 "YEHNGFPLKD", "WAQFDVYNLS", "EDHINCTQWP",
                                 "WSMKVDYEQT", "GWTSKMRYPL", "PMAYIWEKLC",
                                 "HWAEYFNDVT"),
                    protein = c("ProtB", "ProtB", "ProtA", "ProtD", "ProtD",
                                "ProtG", "ProtF", "ProtE", "ProtC", "ProtF"),
                    decoy = rep(FALSE, 10),
                    rank = rep(1, 10),
                    score = c(0.082, 0.310, 0.133, 0.174, 0.944, 0.0261,
                              0.375, 0.741, 0.254, 0.058))
psmdf

psm <- PSM(psmdf, spectrum = "spectrum", peptide = "sequence",
           protein = "protein", decoy = "decoy", rank = "rank")

## binary matrix
makeAdjacencyMatrix(psm)

## Case 2: sp1 and sp11 match the same peptide (NKAVRTYHEQ) as different PSMs
psmdf2 <- rbind(psmdf,
                data.frame(spectrum = "sp11",
                           sequence = psmdf$sequence[1],
                           protein = psmdf$protein[1],
                           decoy = FALSE,
                           rank = 1,
                           score = 0.011))
psmdf2
psm2 <- PSM(psmdf2, spectrum = "spectrum", peptide = "sequence",
            protein = "protein", decoy = "decoy", rank = "rank")

## Now NKAVRTYHEQ/ProtB counts 2 PSMs
makeAdjacencyMatrix(psm2)

## Force a binary matrix
makeAdjacencyMatrix(psm2, binary = TRUE)

## Case 3: Peptide (NKAVRTYHEQ) stems from multiple proteins (ProtB and
## ProtG). They are separated by a semicolon.
psmdf3 <- psmdf
psmdf3[psmdf3$sequence == "NKAVRTYHEQ","protein"] <- "ProtB;ProtG"
psmdf3
psm3 <- PSM(psmdf3, spectrum = "spectrum", peptide = "sequence",
            protein = "protein", decoy = "decoy", rank = "rank")

## Now ProtB & ProtG count 2 PSMs each: NKAVRTYHEQ and IYNHSQGFCA &
## EDHINCTQWP respectively
makeAdjacencyMatrix(psm3, split = ";")

## --------------------------------
## Case 4: set the score PSM values
psmVariables(psm) ## no score defined
psm4 <- PSM(psm, spectrum = "spectrum", peptide = "sequence",
            protein = "protein", decoy = "decoy", rank = "rank",
            score = "score")
psmVariables(psm4) ## score defined

## adjacency matrix with scores
makeAdjacencyMatrix(psm4)

## Force a binary matrix
makeAdjacencyMatrix(psm4, binary = TRUE)

## ---------------------------------
## Case 5: scores with multiple PSMs

psm5 <- PSM(psm2, spectrum = "spectrum", peptide = "sequence",
            protein = "protein", decoy = "decoy", rank = "rank",
            score = "score")

## Now NKAVRTYHEQ/ProtB has a summed score of 0.093 computed as
## 0.082 (from sp1) + 0.011 (from sp11)
makeAdjacencyMatrix(psm5)

Description

This method calculates a-, b-, c-, x-, y- and z-ions produced by fragmentation.

Available methods

• The default method with signature sequence = "character" and object = "missing" calculates the theoretical fragments for a peptide sequence. It returns a data.frame with the columns mz, ion, type, pos, z, seq and peptide.

• Additional method can be defined that will adapt their behaviour based on spectra defined in object. See for example the MSnbase package that implements a method for objects of class Spectrum2.

Usage

## S4 method for signature 'character,missing'
calculateFragments(
  sequence,
  type = c("b", "y"),
  z = 1,
  fixed_modifications = NULL,
  variable_modifications = NULL,
  addCarbamidomethyl = TRUE,
  max_mods = Inf,
  neutralLoss = defaultNeutralLoss(),
  verbose = TRUE
)

Arguments

There is a helper function `defaultNeutralLoss()` that returns
the correct list. It has two arguments `disableWaterLoss` and
`disableAmmoniaLoss` to remove single neutral loss options. See
the example section for use cases.

Value

A data.frame showing all the ions produced by fragmentation with all possible combinations of modifications. The used variable modifications are displayed in the peptide column through the use of amino acids followed by the modification within brackets. Fixed modifications are not displayed.

Author(s)

Sebastian Gibb [email protected]

Guillaume Deflandre [email protected]

Examples

## No modifications
calculateFragments("ACE")

## Multiple ion types and charge states
calculateFragments("ACE",
                   type = c("a", "b", "c", "x", "y", "z"),
                   z = 1:2)

## Positional modification written directly in the sequence string
## The annotation style must be supported by PTMods::convertAnnotation
calculateFragments("T[+79.966]CE")
calculateFragments("T[Phospho]CE")
## Notice carbamidomethylation applied by default, but ignored if already
## present.
calculateFragments("T[UNIMOD:21]C[Carbamidomethyl]E")

## neutral loss
defaultNeutralLoss()

## disable water loss on the C terminal
defaultNeutralLoss(disableWaterLoss="Cterm")

## real example
calculateFragments("PQR")
calculateFragments("PQR",
                   neutralLoss=defaultNeutralLoss(disableWaterLoss="Cterm"))
calculateFragments("PQR",
                   neutralLoss=defaultNeutralLoss(disableAmmoniaLoss="Q"))

## disable neutral loss completely
calculateFragments("PQR", neutralLoss=NULL)

## Recommended workflow: use PTMods functions to produce positional sequences
## before calling calculateFragments.

## Fixed modification (Carbamidomethyl on C) using addFixedModifications
seq_fixed <- PTMods::addFixedModifications("ACE",
                                           fixedModifications = c(C = 57.02))
calculateFragments(seq_fixed)

## Fixed modification including N-terminus using addFixedModifications
seq_nterm <- PTMods::addFixedModifications(
    "ACE",
    fixedModifications = c(C = 57.02, Nterm = 229.16))
calculateFragments(seq_nterm)

## Variable modification (delta mass on A) using addVariableModifications
seq_var <- PTMods::addVariableModifications("ACE",
                                            variableModifications = c(A = 43.25))
calculateFragments(seq_var)

## Both fixed and variable modifications using addModifications
seq_mods <- PTMods::addModifications("ARGSHKATC",
                                     fixedModifications = c(C = 57),
                                     variableModifications = c(S = 79, T = 79),
                                     maxMods = 2)
calculateFragments(seq_mods)

Description

Connected components are a useful representation when exploring identification data. They represent the relation between proteins (the connected components) and how they form groups of proteins as defined by shared peptides.

Connected components are stored as ConnectedComponents objects that can be generated using the ConnectedComponents() function.

Usage

ConnectedComponents(object, ...)

ccMatrix(x)

connectedComponents(x, i, simplify = TRUE)

## S4 method for signature 'ConnectedComponents'
length(x)

## S4 method for signature 'ConnectedComponents'
dims(x)

## S4 method for signature 'ConnectedComponents'
ncols(x)

## S4 method for signature 'ConnectedComponents'
nrows(x)

## S4 method for signature 'ConnectedComponents,integer,ANY,ANY'
x[i, j, ..., drop = FALSE]

## S4 method for signature 'ConnectedComponents,logical,ANY,ANY'
x[i, j, ..., drop = FALSE]

## S4 method for signature 'ConnectedComponents,numeric,ANY,ANY'
x[i, j, ..., drop = FALSE]

prioritiseConnectedComponents(x)

prioritizeConnectedComponents(x)

## S4 method for signature 'ConnectedComponents'
adjacencyMatrix(object)

Arguments

Value

The ConnectedComponents() constructor returns an instance of class ConnectedComponents. The Creating and manipulating objects section describes the return values of the functions that manipulate ConnectedComponents objects.

Slots

adjMatrix

The sparse adjacency matrix (class Matrix) of dimension p peptides by m proteins that was used to generate the object.

ccMatrix

The sparse connected components matrix (class Matrix) of dimension m by m proteins.

adjMatrices

A List containing adjacency matrices of each connected components.

Creating and manipulating objects

• Instances of the class are created with the ConnectedComponent() constructor from a PSM() object or directly from a sparse adjacency matrix of class Matrix. Note that if using the latter, the rows and columns must be named.

• The sparse peptide-by-protein adjacency matrix is stored in the ConnectedComponent instance and can be accessed with the adjacencyMatrix() function.

• The protein-by-protein connected components sparse matrix of object x can be accessed with the ccMatrix(x) function.

• The number of connected components of object x can be retrieved with length(x).

• The size of the connected components of object x, i.e the number of proteins in each component, can be retrieved with ncols(x). The number of peptides defining the connected components can be retrieved with nrows(x). Both can be accessed with dims(x).

• The connectedComponents(x, i, simplify = TRUE) function returns the peptide-by-protein sparse adjacency matrix (or List of matrices, if length(i) > 1), i.e. the subset of the adjacency matrix defined by the proteins in connected component(s) i. i is the numeric index (between 1 and length(x)) of the connected connected. If simplify is TRUE (default), then a matrix is returned instead of a List of matrices of length 1. If set to FALSE, a List is always returned, irrespective of its length.

• To help with the exploration of individual connected Components, the prioritiseConnectedComponents() function will take an instance of ConnectedComponents and return a data.frame where the component indices are ordered based on their potential to clean up/flag some peptides and split protein groups in small groups or individual proteins, or simply explore them. The prioritisation is based on a set of metrics computed from the component's adjacency matrix, including its dimensions, row and col sums maxima and minima, its sparsity and the number of communities and their modularity that quantifies how well the communities separate (see igraph::modularity(). Note that trivial components, i.e. those composed of a single peptide and protein are excluded from the prioritised results. This data.frame is ideally suited for a principal component analysis (using for instance prcomp()) for further inspection for component visualisation with plotAdjacencyMatrix().

Examples

## --------------------------------
## From an adjacency matrix
## --------------------------------
library(Matrix)
adj <- sparseMatrix(i = c(1, 2, 3, 3, 4, 4, 5),
                    j = c(1, 2, 3, 4, 3, 4, 5),
                    x = 1,
                    dimnames = list(paste0("Pep", 1:5),
                                   paste0("Prot", 1:5)))
adj
cc <- ConnectedComponents(adj)
cc

length(cc)
ncols(cc)

adjacencyMatrix(cc) ## same as adj above
ccMatrix(cc)

connectedComponents(cc)
connectedComponents(cc, 3) ## a singel matrix
connectedComponents(cc, 1:2) ## a List

## --------------------------------
## From an PSM object
## --------------------------------
f <- MsDataHub::TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.20141210.mzid()

psm <- PSM(f) |>
       filterPsmDecoy() |>
       filterPsmRank()

cc <- ConnectedComponents(psm)
cc

length(cc)
table(ncols(cc))

(i <- which(ncols(cc) == 4))
ccomp <- connectedComponents(cc, i)

## A group of 4 proteins that all share peptide RTRYQAEVR
ccomp[[1]]

## Visualise the adjacency matrix - here, we see how the single
## peptides (white node) 'unites' the four proteins (blue nodes)
plotAdjacencyMatrix(ccomp[[1]])

## A group of 4 proteins formed by 7 peptides: THPAERKPRRRKKR is
## found in the two first proteins, KPTARRRKRK was found twice in
## ECA3389, VVPVGLRALVWVQR was found in all 4 proteins, KLKPRRR
## is specific to ECA3399, ...
ccomp[[3]]

## See how VVPVGLRALVWVQR is shared by ECA3406 ECA3415 ECA3389 and
## links the three other componennts, namely ECA3399, ECA3389 and
## (ECA3415, ECA3406). Filtering that peptide out would split that
## protein group in three.
plotAdjacencyMatrix(ccomp[[3]])

## Colour protein node based on protein names similarity
plotAdjacencyMatrix(ccomp[[3]], 1)

## To select non-trivial components of size > 1
cc2 <- cc[ncols(cc) > 1]
cc2

## Use components features to prioritise their exploration
pri_cc <- prioritiseConnectedComponents(cc)
pri_cc

plotAdjacencyMatrix(connectedComponents(cc, 1082), 1)

Description

It is important to explore PSM results prior to any further downstream analysies. Two functions, that work on PSM() and ConnectedComponents() objects can be used for this:

• The describeProteins() function describe protein composition in terms of unique and shared peptides.

• The describePeptides() function describe unique/shared peptide composition.

Usage

describeProteins(object, ...)

describePeptides(object, ...)

Arguments

Value

describePeptides() invisibly return the table of unique and shared peptides. describeProteins() invisibly returns a data.frame with logicals indicating the unique/shared peptide composition of proteins. Both functions are used for their side effects of printing a short descriptive output about peptides and proteins.

Examples

f <- MsDataHub::TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.20141210.mzid()

psm <- PSM(f) |>
       filterPsmDecoy() |>
       filterPsmRank()

describePeptides(psm)
describeProteins(psm)

Description

Functions to filter out PSMs matching. The PSMs should be stored in a PSM such as those produced by PSM().

• filterPsmDecoy() filters out decoy PSMs, i.e. those annotated as isDecoy.

• filterPsmRank() filters out PSMs of rank > 1.

• filterPsmShared() filters out shared PSMs, i.e. those that match multiple proteins.

• filterPsmFdr() filters out PSMs based on their FDR.

Usage

filterPSMs(
  x,
  decoy = psmVariables(x)["decoy"],
  rank = psmVariables(x)["rank"],
  protein = psmVariables(x)["protein"],
  spectrum = psmVariables(x)["spectrum"],
  peptide = psmVariables(x)["peptide"],
  verbose = TRUE
)

filterPsmDecoy(x, decoy = psmVariables(x)["decoy"], verbose = TRUE)

filterPsmRank(x, rank = psmVariables(x)["rank"], verbose = TRUE)

filterPsmShared(
  x,
  protein = psmVariables(x)["protein"],
  peptide = psmVariables(x)["peptide"],
  verbose = TRUE
)

filterPsmFdr(x, FDR = 0.05, fdr = psmVariables(x)["fdr"], verbose = TRUE)

Arguments

Value

A new filtered PSM object with the same columns as the input x.

Author(s)

Laurent Gatto

Examples

f <- MsDataHub::TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.20141210.mzid()

id <- PSM(f)
filterPSMs(id)

Description

Returns a data.frame of amino acid properties: AA, ResidueMass, Abbrev3, ImmoniumIonMass, Name, Hydrophobicity, Hydrophilicity, SideChainMass, pK1, pK2 and pI.

Usage

getAminoAcids()

Value

data.frame

Author(s)

Laurent Gatto

Examples

getAminoAcids()

Description

Returns a double of used atomic mass.

Usage

getAtomicMass()

Value

A named double.

Author(s)

Sebastian Gibb

Examples

getAtomicMass()

Description

Creates a list of annotations based on calculateFragments results.

Usage

labelFragments(x, tolerance = 0, ppm = 20, what = c("ion", "mz"), ...)

Arguments

Value

Return a list() of character() with fragment ion labels. The elements are named after the peptide they belong to (variable modifications included).

Author(s)

Johannes Rainer, Guillaume Deflandre, Sebastian Gibb, Laurent Gatto

Examples

library("Spectra")

sp <- DataFrame(msLevel = 2L, rtime = 2345, sequence = "SIGFEGDSIGR")
sp$mz <- list(c(100.048614501953, 110.069030761719, 112.085876464844,
                117.112571716309, 158.089569091797, 163.114898681641,
                175.117172241211, 177.098587036133, 214.127075195312,
                232.137542724609, 233.140335083008, 259.938415527344,
                260.084167480469, 277.111572265625, 282.680786132812,
                284.079437255859, 291.208282470703, 315.422576904297,
                317.22509765625, 327.2060546875, 362.211944580078,
                402.235290527344, 433.255004882812, 529.265991210938,
                549.305236816406, 593.217041015625, 594.595092773438,
                609.848327636719, 631.819702148438, 632.324035644531,
                632.804931640625, 640.8193359375, 641.309936523438,
                641.82568359375, 678.357238769531, 679.346252441406,
                688.291259765625, 735.358947753906, 851.384033203125,
                880.414001464844, 881.40185546875, 919.406433105469,
                938.445861816406, 1022.56658935547, 1050.50415039062,
                1059.82800292969, 1107.52734375, 1138.521484375,
                1147.51538085938, 1226.056640625))
sp$intensity <- list(c(83143.03, 65473.8, 192735.53, 3649178.5,
                       379537.81, 89117.58, 922802.69, 61190.44,
                       281353.22, 2984798.75, 111935.03, 42512.57,
                       117443.59, 60773.67, 39108.15, 55350.43,
                       209952.97, 37001.18, 439515.53, 139584.47,
                       46842.71, 1015457.44, 419382.31, 63378.77,
                       444406.66, 58426.91, 46007.71, 58711.72,
                       80675.59, 312799.97, 134451.72, 151969.72,
                       3215457.75, 1961975, 395735.62, 71002.98,
                       69405.73, 136619.47, 166158.69, 682329.75,
                       239964.69, 242025.44, 1338597.62, 50118.02,
                       1708093.12, 43119.03, 97048.02, 2668231.75,
                       83310.2, 40705.72))
sp <- Spectra(sp)

## The fragment ion labels
labelFragments(sp)

## The fragment mz labels
labelFragments(sp, what = "mz")

## Pass additional parameters to calculateFragments using a PTMods modified sequence
sp_mod <- sp
sp_mod$sequence <- PTMods::addFixedModifications("SIGFEGDSIGR",
                                                  fixedModifications = c(Nterm = 5))
labelFragments(sp_mod, type = c("a", "b", "x", "y"))

## Annotate the spectum with the fragment labels
plotSpectra(sp, labels = labelFragments, labelPos = 3)

## By default used in `plotSpectraPTM()`.
plotSpectraPTM(sp)

Description

plotSpectraPTM() creates annotated visualisations of MS/MS spectra, designed to explore fragment identifications and post-translational modifications (PTMs).

plotSpectraPTM() plots a spectrum's m/z values on the x-axis and corresponding intensities on the y-axis, labeling the peaks according to theoretical fragment ions (e.g., b, y, a, c, x, z) computed using labelFragments() and calculateFragments().

Usage

plotSpectraPTM(
  x,
  deltaMz = TRUE,
  ppm = 20,
  xlab = "m/z",
  ylab = "intensity [%]",
  xlim = numeric(),
  ylim = numeric(),
  main = character(),
  col = c(y = "darkred", b = "darkblue", acxy = "darkgreen", other = "grey40"),
  labelCex = 1,
  labelSrt = 0,
  labelAdj = NULL,
  labelPos = 3,
  labelOffset = 0.5,
  asp = 1,
  minorTicks = TRUE,
  USI = TRUE,
  fixedModifications = NULL,
  variableModifications = NULL,
  addCarbamidomethyl = TRUE,
  ...
)

Arguments

Value

Creates a plot depicting an MS/MS-MS spectrum.

Author(s)

Johannes Rainer, Sebastian Gibb, Guillaume Deflandre, Laurent Gatto

See Also

Spectra::plotSpectra()

Examples

library("Spectra")

sp <- DataFrame(msLevel = 2L, rtime = 2345, sequence = "SIGFEGDSIGR")
sp$mz <- list(c(75.048614501953, 81.069030761719, 86.085876464844,
                88.039, 158.089569091797, 163.114898681641,
                173.128, 177.098587036133, 214.127075195312,
                232.137542724609, 233.140335083008, 259.938415527344,
                260.084167480469, 277.111572265625, 282.680786132812,
                284.079437255859, 291.208282470703, 315.422576904297,
                317.22509765625, 327.2060546875, 362.211944580078,
                402.235290527344, 433.255004882812, 534.258783,
                549.305236816406, 593.217041015625, 594.595092773438,
                609.848327636719, 631.819702148438, 632.324035644531,
                632.804931640625, 640.8193359375, 641.309936523438,
                641.82568359375, 678.357238769531, 679.346252441406,
                706.309623, 735.358947753906, 851.384033203125,
                880.414001464844, 881.40185546875, 906.396433105469,
                938.445861816406, 1022.56658935547, 1050.50415039062,
                1059.82800292969, 1107.52734375, 1138.521484375,
                1147.51538085938, 1226.056640625))
sp$intensity <- list(c(83143.03, 65473.8, 192735.53, 3649178.5,
                       379537.81, 89117.58, 922802.69, 61190.44,
                       281353.22, 2984798.75, 111935.03, 42512.57,
                       117443.59, 60773.67, 39108.15, 55350.43,
                       209952.97, 37001.18, 439515.53, 139584.47,
                       46842.71, 1015457.44, 419382.31, 63378.77,
                       444406.66, 58426.91, 46007.71, 58711.72,
                       80675.59, 312799.97, 134451.72, 151969.72,
                       1961975, 69405.76, 395735.62, 71002.98,
                       3215457.75, 136619.47, 166158.69, 682329.75,
                       239964.69, 242025.44, 1338597.62, 50118.02,
                       1708093.12, 43119.03, 97048.02, 2668231.75,
                       83310.2, 40705.72))
sp <- Spectra(sp)

## Annotate the spectum with the fragment labels
plotSpectraPTM(sp, main = "An example of an annotated plot")

## Annotate the spectrum without the delta m/z plot
plotSpectraPTM(sp, deltaMz = FALSE)

## Annotate the spectrum with different ion types
plotSpectraPTM(sp, type = c("a", "b", "x", "y"))

## Annotate the spectrum with modifications using PTMods
sp_mod <- sp
sp_mod$sequence <- PTMods::addFixedModifications("SIGFEGDSIGR", fixedModifications = c(Nterm = "Acetyl"))
plotSpectraPTM(sp_mod)

## Or call them within the function directly:
plotSpectraPTM(sp, fixedModifications = NULL,
variableModifications = c(R = "Methyl"))

## Annotate multiple spectra at a time
plotSpectraPTM(c(sp, sp))

## Color the peaks with different colors
plotSpectraPTM(sp, col = c(y = "red", b = "blue", acxy = "chartreuse3", other = "black"))

Description

The PSM class is a simple class to store and manipulate peptide-spectrum matches. The class encapsulates PSM data as a DataFrame (or more specifically a DFrame) with additional lightweight metadata annotation.

There are two types of PSM objects:

• Objects with duplicated spectrum identifiers. This holds for multiple matches to the same spectrum, be it different peptide sequences or the same sequence with or without a post-translational modification. Such objects are typically created with the PSM() constructor starting from mzIdentML files.

• Reduced objects where the spectrum identifiers (or any equivalent column) are unique keys within the PSM table. Matches to the same scan/spectrum are merged into a single PSM data row. Reduced PSM object are created with the reducePSMs() function. See examples below.

Objects can be checked for their reduced state with the reduced() function which returns TRUE for reduced instances, FALSE when the spectrum identifiers are duplicated, or NA when unknown. The flag can also be set explicitly with the ⁠reduced()<-⁠ setter.

Usage

PSM(
  x,
  spectrum = NA,
  peptide = NA,
  protein = NA,
  decoy = NA,
  rank = NA,
  score = NA,
  fdr = NA,
  parser = c("mzR", "mzID"),
  BPPARAM = SerialParam()
)

reduced(object, spectrum = psmVariables(object)["spectrum"])

reduced(object) <- value

psmVariables(object, which = "all")

reducePSMs(object, k = object[[psmVariables(object)["spectrum"]]])

## S4 method for signature 'PSM'
adjacencyMatrix(object)

Arguments

Value

PSM() returns a PSM object.

reducePSMs() returns a reduced version of the x input.

Creating and using PSM objects

• The PSM() constructor uses parsers provided by the mzR or mzID packages to read the mzIdentML data. The vignette describes some apparent differences in their outputs. The constructor input is a character of one more multiple file names.

• PSM objects can also be created from a data.frame object (or any variable that can be coerced into a S4Vectors::DataFrame.

• Finally, PSM() can also take a PSM object as input, which leaves the PSM data as is and is used to set/update the PSM variables.

• The constructor can also initialise variables (called PSM variables) needed for downstream processing, notably filtering (see filterPSMs()) and to generate a peptide-by-protein adjacency matrix (see makeAdjacencyMatrix()). These variables can be extracted with the psmVariables() function. They represent the columns in the PSM table that identify spectra, peptides, proteins, decoy peptides hit ranks and, optionally, a PSM score. The value of these variables will depend on the backend used to create the object, or left blank (i.e. encoded as NA) when building an object by hand from a data.frame. In such situation, they need to be passed explicitly by the user as arguments to PSM().

• The adjacencyMatrix() accessor can be used to retrieve the binary sparse peptide-by-protein adjacency matrix from the PSM object. It also relies on PSM variables which thus need to be set beforehand. For more flexibility in the generation of the adjacency matrix (for non-binary matrices), use makeAdjacencyMatrix().

Examples

## ---------------------------------
## Example with a single mzid file
## ---------------------------------

f <- MsDataHub::TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.20141210.mzid()

## mzR parser (default)
psm <- PSM(f)
psm

## PSM variables
psmVariables(psm)

## mzID parser
psm_mzid <- PSM(f, parser = "mzID")
psm_mzid

## different PSM variables
psmVariables(psm_mzid)

## Reducing the PSM data
(i <- which(duplicated(psm$spectrumID))[1:2])
(i <- which(psm$spectrumID %in% psm$spectrumID[i]))
psm2 <- psm[i, ]
reduced(psm2)

## Peptide sequence CIDRARHVEVQIFGDGKGRVVALGERDCSLQRR with
## Carbamidomethyl modifications at positions 1 and 28.
DataFrame(psm2[, c("sequence", "spectrumID", "modName", "modLocation")])
reduced(psm2) <- FALSE
reduced(psm2)

## uses by default the spectrum PSM variable, as defined during
## the construction - see psmVariables()
rpsm2 <- reducePSMs(psm2)
rpsm2
DataFrame(rpsm2[, c("sequence", "spectrumID", "modName", "modLocation")])
reduced(rpsm2)

## ---------------------------------
## Multiple mzid files
## ---------------------------------

library(rpx)
PXD022816 <- PXDataset("PXD022816")
PXD022816

(mzids <- pxget(PXD022816, grep("mzID", pxfiles(PXD022816))[1:2]))
psm <- PSM(mzids)
psm
psmVariables(psm)

## Here, spectrum identifiers are repeated accross files
psm[grep("scan=20000", psm$spectrumID), "spectrumFile"]

## Let's create a new primary identifier composed of the scan
## number and the file name
psm$pkey <- paste(sub("^.+Task\\\\", "", psm$spectrumFile),
                  sub("^.+scan=", "", psm$spectrumID),
                  sep = "::")
head(psm$pkey)

## the PSM is not reduced
reduced(psm, "pkey")
DataFrame(psm[6:7, ])

## same sequence, same spectrumID, same file
psm$sequence[6:7]
psm$pkey[6:7]

## different modification locations
psm$modLocation[6:7]

## here, we need to *explicitly* set pkey to reduce
rpsm <- reducePSMs(psm, psm$pkey)
rpsm
reduced(rpsm, "pkey")

## the two rows are now merged into a single one; the distinct
## modification locations are preserved.
(i <- which(rpsm$pkey == "QEP2LC6_HeLa_50ng_251120_01-calib.mzML::12894"))
DataFrame(rpsm[i, c("sequence", "pkey", "modName", "modLocation")])

## ---------------------------------
## PSM from a data.frame
## ---------------------------------

psmdf <- data.frame(spectrum = paste0("sp", 1:10),
                    sequence = replicate(10,
                                         paste(sample(getAminoAcids()[-1, "AA"], 10),
                                               collapse = "")),
                    protein = sample(paste0("Prot", LETTERS[1:7]), 10,
                                     replace = TRUE),
                    decoy = rep(FALSE, 10),
                    rank = rep(1, 10),
                    score = runif(10))
psmdf

psm <- PSM(psmdf)
psm
psmVariables(psm)

## no PSM variables set
try(adjacencyMatrix(psm))

## set PSM variables
psm <- PSM(psm, spectrum = "spectrum", peptide = "sequence",
           protein = "protein", decoy = "decoy", rank = "rank")
psm
psmVariables(psm)

adjacencyMatrix(psm)

Description

The PSMatch package offers functionality to load, manage and analyse Peptide Spectrum Matches as generated in mass spectrometry-based proteomics. The four main objects and concepts that are proposed in this package are described below, and are aimed to proteomics practitioners to explore and understand their identification data better.

PSM objects

As mentioned in the PSM() manual page, The PSM class is a simple class to store and manipulate peptide-spectrum matches. The class encapsulates PSM data as a DataFrame (or more specifically a DFrame) with additional lightweight metadata annotation. PSM objects are typically creatd from XML-based mzID files or data.frames imported from spreadsheets. It is then possible to apply widely used filters (such as removal of decoy hits, PSMs of rank > 1, ...) as described in filterPSMs().

Adjacency matrices

PSM data, as produced by all proteomics search engines, is exported as a table-like structure where PSM are documented along the rows by variables such as identification scores, peptides sequences, modifications and the protein which the peptides originate from. There is always a level of ambiguity in such data, as peptides can be mapped to mutliple proteins; they are then called shared peptides, as opposed to unique peptides.

One convenient way to store the relation between peptides and proteins is as a peptide-by-protein adjacency matrix. Such matrices can be generated from PSM object or vectors using the makeAdjacencyMatrix() function.

The describePeptides() and describeProteins() functions are also helpful to tally the number of unique and shared peptides and the number of proteins composed of unique or shared peptides, or a combination thereof.

Connected Components

Once we model the peptide-to-protein relations explicitly using an adjacency matrix, it becomes possible to perform computations on the proteins that are grouped by the peptides they share. These groups are mathematically defined as connected components, which are implemented as ConnectedComponents() objects.

Fragment ions

The package also provides functionality to calculate ions produced by the fragmentation of a peptides (see calculateFragments()) and annotated MS2 Spectra::Spectra() objects (see labelFragments()).

Vignettes

A couple of vignette describe how to several of these concepts through illustrative use-cases. Use vignette(package = "PSMatch") to get a list and open them directly in R or read them online on the package's webpage.

Author(s)

Maintainer: Laurent Gatto [email protected] (ORCID)

Authors:

• Johannes Rainer [email protected] (ORCID)

• Sebastian Gibb [email protected] (ORCID)

Other contributors:

• Samuel Wieczorek [email protected] [contributor]

• Thomas Burger [email protected] [contributor]

• Guillaume Deflandre [email protected] (ORCID) [contributor]

See Also

Useful links:

• https://github.com/RforMassSpectrometry/PSM

• Report bugs at https://github.com/RforMassSpectrometry/PSM/issues