Title: | Core Utils for Mass Spectrometry Data |
---|---|
Description: | MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...), misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages. |
Authors: | RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] , Johannes Rainer [aut] , Sebastian Gibb [aut] , Philippine Louail [aut] , Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake [ctb], Josep Maria Badia Aparicio [ctb] , Michael Witting [ctb] , Samuel Wieczorek [ctb], Roger Gine Bertomeu [ctb] , Mar Garcia-Aloy [ctb] |
Maintainer: | RforMassSpectrometry Package Maintainer <[email protected]> |
License: | Artistic-2.0 |
Version: | 1.19.0 |
Built: | 2024-11-14 06:01:02 UTC |
Source: | https://github.com/bioc/MsCoreUtils |
These functions take a matrix of quantitative features x
and
aggregate the features (rows) according to either a vector (or
factor) INDEX
or an adjacency matrix MAT
. The aggregation
method is defined by function FUN
.
Adjacency matrices are an elegant way to explicitly encode for shared peptides (see example below) during aggregation.
colMeansMat(x, MAT, na.rm = FALSE) colSumsMat(x, MAT, na.rm = FALSE) aggregate_by_matrix(x, MAT, FUN, ...) aggregate_by_vector(x, INDEX, FUN, ...)
colMeansMat(x, MAT, na.rm = FALSE) colSumsMat(x, MAT, na.rm = FALSE) aggregate_by_matrix(x, MAT, FUN, ...) aggregate_by_vector(x, INDEX, FUN, ...)
x |
A |
MAT |
An adjacency matrix that defines peptide-protein
relations with |
na.rm |
A |
FUN |
A |
... |
Additional arguments passed to |
INDEX |
A |
aggregate_by_matrix()
returns a matrix
(or Matrix
)
of dimensions ncol(MAT)
and ncol(x), with
dimnamesequal to
colnames(x)and
rownames(MAT)'.
aggregate_by_vector()
returns a new matrix
(if x
is
a matrix
) or HDF5Matrix
(if x
is an HDF5Matrix
)
of dimensions length(INDEX)
and ncol(x), with
dimnames equal to
colnames(x)and
INDEX'.
When aggregating with a vector/factor, user-defined functions
must return a vector of length equal to ncol(x)
for each level
in INDEX
. Examples thereof are:
medianPolish()
to fits an additive model (two way
decomposition) using Tukey's median polish procedure using
stats::medpolish()
;
robustSummary()
to calculate a robust aggregation using
MASS::rlm()
;
base::colMeans()
to use the mean of each column;
base::colSums()
to use the sum of each column;
matrixStats::colMedians()
to use the median of each column.
When aggregating with an adjacency matrix, user-defined functions must return a new matrix. Examples thereof are:
colSumsMat(x, MAT)
aggregates by the summing the peptide intensities
for each protein. Shared peptides are re-used multiple times.
colMeansMat(x, MAT)
aggregation by the calculating the mean of
peptide intensities. Shared peptides are re-used multiple
times.
By default, missing values in the quantitative data will propagate
to the aggregated data. You can provide na.rm = TRUE
to most
functions listed above to ignore missing values, except for
robustSummary()
where you should supply na.action = na.omit
(see ?MASS::rlm
).
Laurent Gatto and Samuel Wieczorek (aggregation from an adjacency matrix).
Other Quantitative feature aggregation:
colCounts()
,
medianPolish()
,
robustSummary()
x <- matrix(c(10.39, 17.16, 14.10, 12.85, 10.63, 7.52, 3.91, 11.13, 16.53, 14.17, 11.94, 11.51, 7.69, 3.97, 11.93, 15.37, 14.24, 11.21, 12.29, 9.00, 3.83, 12.90, 14.37, 14.16, 10.12, 13.33, 9.75, 3.81), nrow = 7, dimnames = list(paste0("Pep", 1:7), paste0("Sample", 1:4))) x ## ------------------------- ## Aggregation by vector ## ------------------------- (k <- paste0("Prot", c("B", "E", "X", "E", "B", "B", "E"))) aggregate_by_vector(x, k, colMeans) aggregate_by_vector(x, k, robustSummary) aggregate_by_vector(x, k, medianPolish) ## ------------------------- ## Aggregation by matrix ## ------------------------- adj <- matrix(c(1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1), nrow = 7, dimnames = list(paste0("Pep", 1:7), paste0("Prot", c("B", "E", "X")))) adj ## Peptide 4 is shared by 2 proteins (has a rowSums of 2), ## namely proteins B and E rowSums(adj) aggregate_by_matrix(x, adj, colSumsMat) aggregate_by_matrix(x, adj, colMeansMat) ## --------------- ## Missing values ## --------------- x <- matrix(c(NA, 2:6), ncol = 2, dimnames = list(paste0("Pep", 1:3), c("S1", "S2"))) x ## simply use na.rm = TRUE to ignore missing values ## during the aggregation (k <- LETTERS[c(1, 1, 2)]) aggregate_by_vector(x, k, colSums) aggregate_by_vector(x, k, colSums, na.rm = TRUE) (adj <- matrix(c(1, 1, 0, 0, 0, 1), ncol = 2, dimnames = list(paste0("Pep", 1:3), c("A", "B")))) aggregate_by_matrix(x, adj, colSumsMat, na.rm = FALSE) aggregate_by_matrix(x, adj, colSumsMat, na.rm = TRUE)
x <- matrix(c(10.39, 17.16, 14.10, 12.85, 10.63, 7.52, 3.91, 11.13, 16.53, 14.17, 11.94, 11.51, 7.69, 3.97, 11.93, 15.37, 14.24, 11.21, 12.29, 9.00, 3.83, 12.90, 14.37, 14.16, 10.12, 13.33, 9.75, 3.81), nrow = 7, dimnames = list(paste0("Pep", 1:7), paste0("Sample", 1:4))) x ## ------------------------- ## Aggregation by vector ## ------------------------- (k <- paste0("Prot", c("B", "E", "X", "E", "B", "B", "E"))) aggregate_by_vector(x, k, colMeans) aggregate_by_vector(x, k, robustSummary) aggregate_by_vector(x, k, medianPolish) ## ------------------------- ## Aggregation by matrix ## ------------------------- adj <- matrix(c(1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1), nrow = 7, dimnames = list(paste0("Pep", 1:7), paste0("Prot", c("B", "E", "X")))) adj ## Peptide 4 is shared by 2 proteins (has a rowSums of 2), ## namely proteins B and E rowSums(adj) aggregate_by_matrix(x, adj, colSumsMat) aggregate_by_matrix(x, adj, colMeansMat) ## --------------- ## Missing values ## --------------- x <- matrix(c(NA, 2:6), ncol = 2, dimnames = list(paste0("Pep", 1:3), c("S1", "S2"))) x ## simply use na.rm = TRUE to ignore missing values ## during the aggregation (k <- LETTERS[c(1, 1, 2)]) aggregate_by_vector(x, k, colSums) aggregate_by_vector(x, k, colSums, na.rm = TRUE) (adj <- matrix(c(1, 1, 0, 0, 0, 1), ncol = 2, dimnames = list(paste0("Pep", 1:3), c("A", "B")))) aggregate_by_matrix(x, adj, colSumsMat, na.rm = FALSE) aggregate_by_matrix(x, adj, colSumsMat, na.rm = TRUE)
These functions help to work with numeric
ranges.
between(x, range) x %between% range
between(x, range) x %between% range
x |
|
range |
|
logical
vector of length length(x)
.
Sebastian Gibb
Other helper functions for developers:
isPeaksMatrix()
,
rbindFill()
,
validPeaksMatrix()
,
vapply1c()
,
which.first()
between(1:4, 2:3) 1:4 %between% 2:3
between(1:4, 2:3) 1:4 %between% 2:3
Aggregate values in x
for bins defined on y
: all values
in x
for values in y
falling into a bin (defined on y
) are
aggregated with the provided function FUN
.
bin( x, y, size = 1, breaks = seq(floor(min(y)), ceiling(max(y)), by = size), FUN = max, returnMids = TRUE, .check = TRUE )
bin( x, y, size = 1, breaks = seq(floor(min(y)), ceiling(max(y)), by = size), FUN = max, returnMids = TRUE, .check = TRUE )
x |
|
y |
|
size |
|
breaks |
|
FUN |
|
returnMids |
|
.check |
|
Depending on the value of returnMids
:
returnMids = TRUE
(the default): returns a list
with elements x
(aggregated values of x
) and mids
(the bin mid points).
returnMids = FALSE
: returns a numeric
with just the binned values for
x
.
Johannes Rainer, Sebastian Gibb
Other grouping/matching functions:
closest()
,
gnps()
## Define example intensities and m/z values ints <- abs(rnorm(20, mean = 40)) mz <- seq(1:length(ints)) + rnorm(length(ints), sd = 0.001) ## Bin intensities by m/z bins with a bin size of 2 bin(ints, mz, size = 2) ## Repeat but summing up intensities instead of taking the max bin(ints, mz, size = 2, FUN = sum) ## Get only the binned values without the bin mid points. bin(ints, mz, size = 2, returnMids = FALSE)
## Define example intensities and m/z values ints <- abs(rnorm(20, mean = 40)) mz <- seq(1:length(ints)) + rnorm(length(ints), sd = 0.001) ## Bin intensities by m/z bins with a bin size of 2 bin(ints, mz, size = 2) ## Repeat but summing up intensities instead of taking the max bin(ints, mz, size = 2, FUN = sum) ## Get only the binned values without the bin mid points. bin(ints, mz, size = 2, returnMids = FALSE)
breaks_ppm
creates a sequence of numbers with increasing differences
between them. Parameter ppm
defines the amount by which the difference
between values increases. The value for an element i+1
is calculated by
adding size
to the value of element i
and in addition also the
ppm(a, ppm)
, where a
is the value of the element i
plus size
. This
iterative calculation is stopped once the value of an element is larger
than to
. The last value in the result vector will thus not be equal to
to
(which is in contrast to the base seq()
function) but slightly
higher.
A typical use case of this function would be to calculate breaks for the binning of m/z values of mass spectra. This function allows to create m/z-relative bin sizes which better represents measurement errors observed on certain mass spectrometry instruments.
breaks_ppm(from = 1, to = 1, by = 1, ppm = 0)
breaks_ppm(from = 1, to = 1, by = 1, ppm = 0)
from |
|
to |
|
by |
|
ppm |
|
numeric
with the sequence of values with increasing differences.
The returned values include from
and to
.
Johannes Rainer
res <- breaks_ppm(20, 50, by = 1, ppm = 50) res ## difference between the values increases (by ppm) diff(res)
res <- breaks_ppm(20, 50, by = 1, ppm = 50) res ## difference between the values increases (by ppm) diff(res)
These functions offer relaxed matching of one vector in another.
In contrast to the similar match()
and %in%
functions they
just accept numeric
arguments but have an additional tolerance
argument that allows relaxed matching.
closest( x, table, tolerance = Inf, ppm = 0, duplicates = c("keep", "closest", "remove"), nomatch = NA_integer_, .check = TRUE ) common( x, table, tolerance = Inf, ppm = 0, duplicates = c("keep", "closest", "remove"), .check = TRUE ) join( x, y, tolerance = 0, ppm = 0, type = c("outer", "left", "right", "inner"), .check = TRUE, ... )
closest( x, table, tolerance = Inf, ppm = 0, duplicates = c("keep", "closest", "remove"), nomatch = NA_integer_, .check = TRUE ) common( x, table, tolerance = Inf, ppm = 0, duplicates = c("keep", "closest", "remove"), .check = TRUE ) join( x, y, tolerance = 0, ppm = 0, type = c("outer", "left", "right", "inner"), .check = TRUE, ... )
x |
|
table |
|
tolerance |
|
ppm |
|
duplicates |
|
nomatch |
|
.check |
|
y |
|
type |
|
... |
ignored. |
For closest
/common
the tolerance
argument could be set to 0
to get
the same results as for match()
/%in%
. If it is set to Inf
(default)
the index of the closest values is returned without any restriction.
It is not guaranteed that there is a one-to-one matching for neither the
x
to table
nor the table
to x
matching.
If multiple elements in x
match a single element in table
all their
corresponding indices are returned if duplicates="keep"
is set (default).
This behaviour is identical to match()
. For duplicates="closest"
just
the closest element in x
gets the corresponding index in table
and
for duplicates="remove"
all elements in x
that match to the same element
in table
are set to nomatch
.
If a single element in x
matches multiple elements in table
the closest
is returned for duplicates="keep"
or duplicates="closest"
(keeping
multiple matches isn't possible in this case because the return value should
be of the same length as x
). If the differences between x
and the
corresponding matches in table
are identical the lower index (the smaller
element in table
) is returned. There is one exception: if the lower index
is already returned for another x
with a smaller difference to this
index
the higher one is returned for duplicates = "closer"
(but only if there is no other x
that is closer to the higher one).
For duplicates="remove"
all multiple matches are returned as nomatch
as
above.
.checks = TRUE
tests among other input validation checks for increasingly
sorted x
and table
arguments that are mandatory assumptions for the
closest
algorithm. These checks require to loop through both vectors and
compare each element against its precursor.
Depending on the length and distribution of x
and table
these checks take
equal/more time than the whole closest
algorithm. If it is ensured by other
methods that both arguments x
and table
are sorted the tests could be
skipped by .check = FALSE
. In the case that .check = FALSE
is used
and one of x
and table
is not sorted (or decreasingly sorted)
the output would be incorrect in the best case and result in infinity
loop in the average and worst case.
join
: joins two numeric
vectors by mapping values in x
with
values in y
and vice versa if they are similar enough (provided the
tolerance
and ppm
specified). The function returns a matrix
with the
indices of mapped values in x
and y
. Parameter type
allows to define
how the vectors will be joined: type = "left"
: values in x
will be
mapped to values in y
, elements in y
not matching any value in x
will
be discarded. type = "right"
: same as type = "left"
but for y
.
type = "outer"
: return matches for all values in x
and in y
.
type = "inner"
: report only indices of values that could be mapped.
closest
returns an integer
vector of the same length as x
giving the closest position in table
of the first match or nomatch
if
there is no match.
common
returns a logical
vector of length x
that is TRUE
if the
element in x
was found in table
. It is similar to %in%
.
join
returns a matrix
with two columns, namely x
and y
,
representing the index of the values in x
matching the corresponding value
in y
(or NA
if the value does not match).
join
is based on closest(x, y, tolerance, duplicates = "closest")
.
That means for multiple matches just the closest one is reported.
Sebastian Gibb, Johannes Rainer
Other grouping/matching functions:
bin()
,
gnps()
## Define two vectors to match x <- c(1, 3, 5) y <- 1:10 ## Compare match and closest match(x, y) closest(x, y) ## If there is no exact match x <- x + 0.1 match(x, y) # no match closest(x, y) ## Some new values x <- c(1.11, 45.02, 556.45) y <- c(3.01, 34.12, 45.021, 46.1, 556.449) ## Using a single tolerance value closest(x, y, tolerance = 0.01) ## Using a value-specific tolerance accepting differences of 20 ppm closest(x, y, ppm = 20) ## Same using 50 ppm closest(x, y, ppm = 50) ## Sometimes multiple elements in `x` match to `table` x <- c(1.6, 1.75, 1.8) y <- 1:2 closest(x, y, tolerance = 0.5) closest(x, y, tolerance = 0.5, duplicates = "closest") closest(x, y, tolerance = 0.5, duplicates = "remove") ## Are there any common values? x <- c(1.6, 1.75, 1.8) y <- 1:2 common(x, y, tolerance = 0.5) common(x, y, tolerance = 0.5, duplicates = "closest") common(x, y, tolerance = 0.5, duplicates = "remove") ## Join two vectors x <- c(1, 2, 3, 6) y <- c(3, 4, 5, 6, 7) jo <- join(x, y, type = "outer") jo x[jo$x] y[jo$y] jl <- join(x, y, type = "left") jl x[jl$x] y[jl$y] jr <- join(x, y, type = "right") jr x[jr$x] y[jr$y] ji <- join(x, y, type = "inner") ji x[ji$x] y[ji$y]
## Define two vectors to match x <- c(1, 3, 5) y <- 1:10 ## Compare match and closest match(x, y) closest(x, y) ## If there is no exact match x <- x + 0.1 match(x, y) # no match closest(x, y) ## Some new values x <- c(1.11, 45.02, 556.45) y <- c(3.01, 34.12, 45.021, 46.1, 556.449) ## Using a single tolerance value closest(x, y, tolerance = 0.01) ## Using a value-specific tolerance accepting differences of 20 ppm closest(x, y, ppm = 20) ## Same using 50 ppm closest(x, y, ppm = 50) ## Sometimes multiple elements in `x` match to `table` x <- c(1.6, 1.75, 1.8) y <- 1:2 closest(x, y, tolerance = 0.5) closest(x, y, tolerance = 0.5, duplicates = "closest") closest(x, y, tolerance = 0.5, duplicates = "remove") ## Are there any common values? x <- c(1.6, 1.75, 1.8) y <- 1:2 common(x, y, tolerance = 0.5) common(x, y, tolerance = 0.5, duplicates = "closest") common(x, y, tolerance = 0.5, duplicates = "remove") ## Join two vectors x <- c(1, 2, 3, 6) y <- c(3, 4, 5, 6, 7) jo <- join(x, y, type = "outer") jo x[jo$x] y[jo$y] jl <- join(x, y, type = "left") jl x[jl$x] y[jl$y] jr <- join(x, y, type = "right") jr x[jr$x] y[jr$y] ji <- join(x, y, type = "inner") ji x[ji$x] y[ji$y]
asInteger
: convert x
to an integer
and throw an error if x
is not
a numeric
.
asInteger(x)
asInteger(x)
x |
input argument. |
Johannes Rainer
## Convert numeric to integer asInteger(3.4) asInteger(3)
## Convert numeric to integer asInteger(3.4) asInteger(3)
Returns the number of non-NA features in a features by sample matrix.
colCounts(x, ...)
colCounts(x, ...)
x |
A |
... |
Currently ignored. |
A numeric
vector of length identical to ncol(x)
.
Laurent Gatto
Other Quantitative feature aggregation:
aggregate()
,
medianPolish()
,
robustSummary()
m <- matrix(c(1, NA, 2, 3, NA, NA, 4, 5, 6), nrow = 3) colCounts(m) m <- matrix(rnorm(30), nrow = 3) colCounts(m)
m <- matrix(c(1, NA, 2, 3, NA, NA, 4, 5, 6), nrow = 3) colCounts(m) m <- matrix(rnorm(30), nrow = 3) colCounts(m)
Find the common part of the path up to a provided set of files. Be aware that the last element (after the last file separator) is treated as a file. Thus, if only directories, without files are submitted, the common path containing these directories is returned.
common_path(x, fsep = .Platform$file.sep)
common_path(x, fsep = .Platform$file.sep)
x |
|
fsep |
|
character(1)
representing the path common to all files in x
.
This function uses "(\\\\)|/"
to split the provided paths into the
individual directories to support both Windows-specific and
unix-specific separators between folders. File and folder names
should thus not contain these characters.
Johannes Rainer
## Find the common part of the file path pths <- c("/tmp/some/dir/a.txt", "/tmp/some/dir/b.txt", "/tmp/some/other/dir/c.txt", "/tmp/some/other/dir/d.txt") common_path(pths) ## If there is no common part common_path(c("/a/b", "b")) ## Windows paths; note that "/" is used as file separator in the result common_path(c("C:\\some\\path\\a.txt", "C:\\some\\path\\b.txt")) ## No input common_path(character()) ## No path common_path(c("a.txt", "b.txt")) ## Same path for all common_path(c("a/a.txt", "a/a.txt"))
## Find the common part of the file path pths <- c("/tmp/some/dir/a.txt", "/tmp/some/dir/b.txt", "/tmp/some/other/dir/c.txt", "/tmp/some/other/dir/d.txt") common_path(pths) ## If there is no common part common_path(c("/a/b", "b")) ## Windows paths; note that "/" is used as file separator in the result common_path(c("C:\\some\\path\\a.txt", "C:\\some\\path\\b.txt")) ## No input common_path(character()) ## No path common_path(c("a.txt", "b.txt")) ## Same path for all common_path(c("a/a.txt", "a/a.txt"))
These functions provide different normalized similariy/distance measurements.
ndotproduct(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...) dotproduct(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...) neuclidean(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...) navdist(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...) nspectraangle(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...)
ndotproduct(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...) dotproduct(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...) neuclidean(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...) navdist(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...) nspectraangle(x, y, m = 0L, n = 0.5, na.rm = TRUE, ...)
x |
|
y |
|
m |
|
n |
|
na.rm |
|
... |
ignored. |
All functions that calculate normalized similarity/distance measurements are prefixed with a n.
ndotproduct
: the normalized dot product is described in Stein and Scott
1994 as: ; where
, where
and
are the m/z and intensity
values, respectively. Please note also that
; where NCos
is the cosine value (i.e. the orthodox normalized dot product) of the
intensity vectors as described in Yilmaz et al. 2017. Stein and Scott 1994
empirically determined the optimal exponents as
m = 3
and n = 0.6
by
analyzing ca. 12000 EI-MS data of 8000 organic compounds in the NIST Mass
Spectral Library.
MassBank (Horai et al. 2010) uses m = 2
and n = 0.5
for small compounds. In general with increasing values for m
,
high m/z values will be taken more into account for similarity calculation.
Especially when working with small molecules, a value m > 0
can be set
to give a weight on the m/z values to accommodate that shared fragments
with higher m/z are less likely and will mean that molecules might be more
similar. Increasing n
will result in a higher importance of the intensity
values. Most commonly m = 0
and n = 0.5
are used.
neuclidean
: the normalized euclidean distance is described in Stein and
Scott 1994 as:
; where
, where
and
are the m/z and intensity
values, respectively. See the details section about
ndotproduct
for an
explanation how to set m
and n
.
navdist
: the normalized absolute values distance is described in Stein and
Scott 1994 as:
; where
, where
and
are the m/z and intensity
values, respectively. See the details section about
ndotproduct
for an
explanation how to set m
and n
.
nspectraangle
: the normalized spectra angle is described in Toprak et al
2014 as:
; where
, where
and
are the m/z and intensity
values, respectively. The weighting was not originally proposed by Toprak et
al. 2014. See the details section about
ndotproduct
for an explanation how
to set m
and n
.
double(1)
value between 0:1
, where 0
is completely different
and 1
identically.
These methods are implemented as described in Stein and Scott 1994
(navdist
, ndotproduct
, neuclidean
) and Toprak et al. 2014
(nspectraangle
) but because there is no reference implementation available
we are unable to guarantee that the results are identical.
Note that the Stein and Scott 1994 normalized dot product method (and by
extension ndotproduct
) corresponds to the square of the orthodox
normalized dot product (or cosine distance) used also commonly as spectrum
similarity measure (Yilmaz et al. 2017).
Please see also the corresponding discussion at the github pull request
linked below. If you find any problems or reference implementation please
open an issue at
https://github.com/rformassspectrometry/MsCoreUtils/issues.
navdist
, neuclidean
, nspectraangle
: Sebastian Gibb
ndotproduct
: Sebastian Gibb and
Thomas Naake, [email protected]
Stein, S. E., and Scott, D. R. (1994). Optimization and testing of mass spectral library search algorithms for compound identification. Journal of the American Society for Mass Spectrometry, 5(9), 859–866. doi:10.1016/1044-0305(94)87009-8.
Yilmaz, S., Vandermarliere, E., and Lennart Martens (2017). Methods to Calculate Spectrum Similarity. In S. Keerthikumar and S. Mathivanan (eds.), Proteome Bioinformatics: Methods in Molecular Biology, vol. 1549 (pp. 81). doi:10.1007/978-1-4939-6740-7_7.
Horai et al. (2010). MassBank: a public repository for sharing mass spectral data for life sciences. Journal of mass spectrometry, 45(7), 703–714. doi:10.1002/jms.1777.
Toprak et al. (2014). Conserved peptide fragmentation as a benchmarking tool for mass spectrometers and a discriminating feature for targeted proteomics. Molecular & Cellular Proteomics : MCP, 13(8), 2056–2071. doi:10.1074/mcp.O113.036475.
Pull Request for these distance/similarity measurements: https://github.com/rformassspectrometry/MsCoreUtils/pull/33
Other distance/similarity functions:
gnps()
x <- matrix(c(1:5, 1:5), ncol = 2, dimnames = list(c(), c("mz", "intensity"))) y <- matrix(c(1:5, 5:1), ncol = 2, dimnames = list(c(), c("mz", "intensity"))) ndotproduct(x, y) ndotproduct(x, y, m = 2, n = 0.5) ndotproduct(x, y, m = 3, n = 0.6) neuclidean(x, y) navdist(x, y) nspectraangle(x, y)
x <- matrix(c(1:5, 1:5), ncol = 2, dimnames = list(c(), c("mz", "intensity"))) y <- matrix(c(1:5, 5:1), ncol = 2, dimnames = list(c(), c("mz", "intensity"))) ndotproduct(x, y) ndotproduct(x, y, m = 2, n = 0.5) ndotproduct(x, y, m = 3, n = 0.6) neuclidean(x, y) navdist(x, y) nspectraangle(x, y)
These functions allow to calculate entropy measurements of an MS/MS spectrum based on the metrics suggested by Li et al. (https://doi.org/10.1038/s41592-021-01331-z). Spectral entropy and normalized entropy are used to measure the complexity of an spectra. MassBank of North America (MoNA) defines spectra entropy as the intensity weighted spectral peak number (https://mona.fiehnlab.ucdavis.edu/documentation/entropy). Additionally it is suggested to consider spectra with a normalized entropy larger than 0.8, or a spectral entropy larger than 3 as low-quality spectra.
entropy(x) nentropy(x)
entropy(x) nentropy(x)
x |
|
numeric
: (normalized) entropy of x
.
Mar Garcia-Aloy
spectrum <- rbind(c(41.04, 37.16), c(69.07, 66.83), c(86.1, 999.0)) entropy(spectrum[,2]) nentropy(spectrum[,2])
spectrum <- rbind(c(41.04, 37.16), c(69.07, 66.83), c(86.1, 999.0)) entropy(spectrum[,2]) nentropy(spectrum[,2])
This function estimates the the baseline of mass spectrometry data, represented by numeric vectors of masses and intensities of identical lengths.
estimateBaseline( x, y, method = c("SNIP", "TopHat", "ConvexHull", "median"), ... ) estimateBaselineConvexHull(x, y) estimateBaselineMedian(x, y, halfWindowSize = 100L) estimateBaselineSnip(x, y, iterations = 100L, decreasing = TRUE) estimateBaselineTopHat(x, y, halfWindowSize = 100L)
estimateBaseline( x, y, method = c("SNIP", "TopHat", "ConvexHull", "median"), ... ) estimateBaselineConvexHull(x, y) estimateBaselineMedian(x, y, halfWindowSize = 100L) estimateBaselineSnip(x, y, iterations = 100L, decreasing = TRUE) estimateBaselineTopHat(x, y, halfWindowSize = 100L)
x |
|
y |
|
method |
|
... |
Additional parameters passed to the respective functions. |
halfWindowSize |
|
iterations |
|
decreasing |
|
SNIP: This baseline estimation is based on the Statistics-sensitive Non-linear Iterative Peak-clipping algorithm (SNIP) described in Ryan et al 1988.
The algorithm based on the following equation:
It has two additional arguments namely an integer iterations
and a logical decreasing
.
TopHat: This algorithm applies a moving minimum (erosion filter)
and subsequently a moving maximum (dilation filter) filter on
the intensity values. The implementation is based on van Herk
(1996). It has an additional halfWindowSize
argument
determining the half size of the moving window for the TopHat
filter.
ConvexHull: The baseline estimation is based on a convex hull constructed below the spectrum.
Median: This baseline estimation uses a moving median. It is
based on stats::runmed()
. The additional argument
halfWindowSize
corresponds to the k
argument in
stats::runmed()
(k = 2 * halfWindowSize + 1
) and controls
the half size of the moving window.
numeric()
with estimated baseline intensities.
Sebastian Gibb
These functions have been ported from the MALDIqaunt package.
SNIP:
C.G. Ryan, E. Clayton, W.L. Griffin, S.H. Sie, and D.R. Cousens (1988). Snip, a statistics-sensitive background treatment for the quantitative analysis of pixe spectra in geoscience applications. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 34(3): 396-402.
M. Morhac (2009). An algorithm for determination of peak regions and baseline elimination in spectroscopic data. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 600(2), 478-487.
TopHat:
M. van Herk (1992). A Fast Algorithm for Local Minimum and Maximum Filters on Rectangular and Octagonal Kernels. Pattern Recognition Letters 13.7: 517-521.
J. Y. Gil and M. Werman (1996). Computing 2-Dimensional Min, Median and Max Filters. IEEE Transactions: 504-507.
ConvexHull:
Andrew, A. M. (1979). Another efficient algorithm for convex hulls in two dimensions. Information Processing Letters, 9(5), 216-219.
## ---------------------------- ## Simulation example data nmz <- 5000 mz <- seq(1000, length.out = nmz) ## create peaks center <- seq(50, nmz, by = 500) peaks <- lapply(center, function(cc)1000 * dpois(0:100, (1000 + cc) / 75)) ## create baseline intensity <- 100 * exp(-seq_len(nmz)/2000) ## add peaks to baseline for (i in seq(along = center)) { intensity[center[i]:(center[i] + 100)] <- intensity[center[i]:(center[i] + 100)] + peaks[[i]] } ## add noise intensity <- intensity + rnorm(nmz, mean = 0, sd = 1) plot(mz, intensity, type = "l") ## ---------------------------- ## SNIP baseline base_SNIP <- estimateBaseline(mz, intensity, method = "SNIP", iterations = 20L) ## same as estimateBaselineSnip(mz, intensity, iterations = 20L) lines(mz, base_SNIP, col = "red") ## ---------------------------- ## TopHat baseline base_TH25 <- estimateBaseline(mz, intensity, method = "TopHat", halfWindowSize = 25L) ## same as estimateBaselineTopHat(mz, intenstity, halfWindowSize = 25L) lines(mz, base_TH25, col = "blue") base_TH15 <- estimateBaseline(mz, intensity, method = "TopHat", halfWindowSize = 15L) lines(mz, base_TH15, col = "steelblue") ## ---------------------------- ## Convex hull baseline base_CH <- estimateBaseline(mz, intensity, method = "ConvexHull") ## same as estimateBaselineConvexHull(mz, intensity) lines(mz, base_CH, col = "green") ## ---------------------------- ## Median baseline base_med <- estimateBaseline(mz, intensity, method = "median") ## same as estimateBaselineMedian(mz, intensity) lines(mz, base_med, col = "orange") legend("topright", lwd = 1, legend = c("SNIP", "TopHat (hws = 25)", "TopHat (hws = 15)", "ConvexHull", "Median"), col = c("red", "blue", "steelblue", "green", "orange"))
## ---------------------------- ## Simulation example data nmz <- 5000 mz <- seq(1000, length.out = nmz) ## create peaks center <- seq(50, nmz, by = 500) peaks <- lapply(center, function(cc)1000 * dpois(0:100, (1000 + cc) / 75)) ## create baseline intensity <- 100 * exp(-seq_len(nmz)/2000) ## add peaks to baseline for (i in seq(along = center)) { intensity[center[i]:(center[i] + 100)] <- intensity[center[i]:(center[i] + 100)] + peaks[[i]] } ## add noise intensity <- intensity + rnorm(nmz, mean = 0, sd = 1) plot(mz, intensity, type = "l") ## ---------------------------- ## SNIP baseline base_SNIP <- estimateBaseline(mz, intensity, method = "SNIP", iterations = 20L) ## same as estimateBaselineSnip(mz, intensity, iterations = 20L) lines(mz, base_SNIP, col = "red") ## ---------------------------- ## TopHat baseline base_TH25 <- estimateBaseline(mz, intensity, method = "TopHat", halfWindowSize = 25L) ## same as estimateBaselineTopHat(mz, intenstity, halfWindowSize = 25L) lines(mz, base_TH25, col = "blue") base_TH15 <- estimateBaseline(mz, intensity, method = "TopHat", halfWindowSize = 15L) lines(mz, base_TH15, col = "steelblue") ## ---------------------------- ## Convex hull baseline base_CH <- estimateBaseline(mz, intensity, method = "ConvexHull") ## same as estimateBaselineConvexHull(mz, intensity) lines(mz, base_CH, col = "green") ## ---------------------------- ## Median baseline base_med <- estimateBaseline(mz, intensity, method = "median") ## same as estimateBaselineMedian(mz, intensity) lines(mz, base_med, col = "orange") legend("topright", lwd = 1, legend = c("SNIP", "TopHat (hws = 25)", "TopHat (hws = 15)", "ConvexHull", "Median"), col = c("red", "blue", "steelblue", "green", "orange"))
This function performs interpolation on the non-increasing parts of a
numeric input vector to ensure its values are monotonously increasing.
If the values are non-increasing at the end of the vector, these values will
be replaced by a sequence of numeric values, starting from the last
increasing value in the input vector, and increasing by a very small value,
which can be defined with parameter by
force_sorted(x, by = .Machine$double.eps)
force_sorted(x, by = .Machine$double.eps)
x |
|
by |
|
A vector with continuously increasing values.
NA values will not be replaced and be returned as-is.
x <- c(NA, NA, NA, 1.2, 1.1, 1.14, 1.2, 1.3, NA, 1.04, 1.4, 1.6, NA, NA) y <- force_sorted(x) is.unsorted(y, na.rm = TRUE) ## Vector non increasing at the end x <- c(1, 2, 1.5, 2) y <- force_sorted(x, by = 0.1) is.unsorted(y, na.rm = TRUE) ## We can see the values were not interpolated but rather replaced by the ## last increasing value `2` and increasing by 0.1. y
x <- c(NA, NA, NA, 1.2, 1.1, 1.14, 1.2, 1.3, NA, 1.04, 1.4, 1.6, NA, NA) y <- force_sorted(x) is.unsorted(y, na.rm = TRUE) ## Vector non increasing at the end x <- c(1, 2, 1.5, 2) y <- force_sorted(x, by = 0.1) is.unsorted(y, na.rm = TRUE) ## We can see the values were not interpolated but rather replaced by the ## last increasing value `2` and increasing by 0.1. y
The join_gnps
and gnps
functions allow to calculate spectra similarity
scores as used in GNPS. The approach matches first
peaks between the two spectra directly using a user-defined ppm and/or
tolerance as well as using a fixed delta m/z (considering the same ppm and
tolerance) that is defined by the difference of the two spectras' precursor
m/z values. For peaks that match multiple peaks in the
other spectrum only the matching peak pair with the higher value/similarity
is considered in the final similarity score calculation. Note that GNPS
similarity scores are calculated only if the two functions are used together.
join_gnps
: matches/maps peaks between spectra with the same approach
as in GNPS: peaks are considered matching if a) the
difference in their m/z values is smaller than defined by tolerance
and ppm
(this is the same as joinPeaks
) and b) the difference of
their m/z adjusted for the difference of the spectras' precursor is
smaller than defined by tolerance
and ppm
. Based on this definition,
peaks in x
can match up to two peaks in y
hence returned peak indices
might be duplicated. Note that if one of xPrecursorMz
or yPrecursorMz
are NA
or if both are the same, the results are the same as with
join()
. The function returns a list
of two integer
vectors with the
indices of the peaks matching peaks in the other spectrum or NA
otherwise.
gnps
: calculates the GNPS similarity score on peak matrices' previously
aligned (matched) with join_gnps
. For multi-mapping peaks the pair with
the higher similarity are considered in the final score calculation.
gnps(x, y, ...) join_gnps( x, y, xPrecursorMz = NA_real_, yPrecursorMz = NA_real_, tolerance = 0, ppm = 0, type = "outer", ... )
gnps(x, y, ...) join_gnps( x, y, xPrecursorMz = NA_real_, yPrecursorMz = NA_real_, tolerance = 0, ppm = 0, type = "outer", ... )
x |
for |
y |
for |
... |
for |
xPrecursorMz |
for |
yPrecursorMz |
for |
tolerance |
for |
ppm |
for |
type |
for |
The implementation of gnps
bases on the R code from the publication listed
in the references.
See function definition in the description section.
Johannes Rainer, Michael Witting, based on the code from Xing et al. (2020).
Xing S, Hu Y, Yin Z, Liu M, Tang X, Fang M, Huan T. Retrieving and Utilizing Hypothetical Neutral Losses from Tandem Mass Spectra for Spectral Similarity Analysis and Unknown Metabolite Annotation. Anal Chem. 2020 Nov 3;92(21):14476-14483. doi:10.1021/acs.analchem.0c02521.
Other grouping/matching functions:
bin()
,
closest()
Other distance/similarity functions:
distance
## Define spectra x <- cbind(mz = c(10, 36, 63, 91, 93), intensity = c(14, 15, 999, 650, 1)) y <- cbind(mz = c(10, 12, 50, 63, 105), intensity = c(35, 5, 16, 999, 450)) ## The precursor m/z pmz_x <- 91 pmz_y <- 105 ## Plain join identifies only 2 matching peaks join(x[, 1], y[, 1]) ## join_gnps finds 4 matches join_gnps(x[, 1], y[, 1], pmz_x, pmz_y) ## with one of the two precursor m/z being NA, the result are the same as ## with join. join_gnps(x[, 1], y[, 1], pmz_x, yPrecursorMz = NA) ## Calculate GNPS similarity score: map <- join_gnps(x[, 1], y[, 1], pmz_x, pmz_y) gnps(x[map[[1]], ], y[map[[2]], ])
## Define spectra x <- cbind(mz = c(10, 36, 63, 91, 93), intensity = c(14, 15, 999, 650, 1)) y <- cbind(mz = c(10, 12, 50, 63, 105), intensity = c(35, 5, 16, 999, 450)) ## The precursor m/z pmz_x <- 91 pmz_y <- 105 ## Plain join identifies only 2 matching peaks join(x[, 1], y[, 1]) ## join_gnps finds 4 matches join_gnps(x[, 1], y[, 1], pmz_x, pmz_y) ## with one of the two precursor m/z being NA, the result are the same as ## with join. join_gnps(x[, 1], y[, 1], pmz_x, yPrecursorMz = NA) ## Calculate GNPS similarity score: map <- join_gnps(x[, 1], y[, 1], pmz_x, pmz_y) gnps(x[map[[1]], ], y[map[[2]], ])
The group
function groups numeric values by first ordering and then putting
all values into the same group if their difference is smaller defined by
parameters tolerance
(a constant value) and ppm
(a value-specific
relative value expressed in parts-per-million).
group(x, tolerance = 0, ppm = 0)
group(x, tolerance = 0, ppm = 0)
x |
increasingly ordered |
tolerance |
|
ppm |
|
integer
of length equal to x
with the groups.
Since grouping is performed on pairwise differences between consecutive
values (after ordering x
), the difference between the smallest and largest
value in a group can be larger than tolerance
and ppm
.
Johannes Rainer, Sebastin Gibb
## Define a (sorted) numeric vector x <- c(34, 35, 35, 35 + ppm(35, 10), 56, 56.05, 56.1) ## With `ppm = 0` and `tolerance = 0` only identical values are grouped group(x) ## With `tolerance = 0.05` group(x, tolerance = 0.05) ## Also values 56, 56.05 and 56.1 were grouped into a single group, ## although the difference between the smallest 56 and largest value in ## this group (56.1) is 0.1. The (pairwise) difference between the ordered ## values is however 0.05. ## With ppm group(x, ppm = 10) ## Same on an unsorted vector x <- c(65, 34, 65.1, 35, 66, 65.2) group(x, tolerance = 0.1) ## Values 65, 65.1 and 65.2 have been grouped into the same group.
## Define a (sorted) numeric vector x <- c(34, 35, 35, 35 + ppm(35, 10), 56, 56.05, 56.1) ## With `ppm = 0` and `tolerance = 0` only identical values are grouped group(x) ## With `tolerance = 0.05` group(x, tolerance = 0.05) ## Also values 56, 56.05 and 56.1 were grouped into a single group, ## although the difference between the smallest 56 and largest value in ## this group (56.1) is 0.1. The (pairwise) difference between the ordered ## values is however 0.05. ## With ppm group(x, ppm = 10) ## Same on an unsorted vector x <- c(65, 34, 65.1, 35, 66, 65.2) group(x, tolerance = 0.1) ## Values 65, 65.1 and 65.2 have been grouped into the same group.
i2index
is a simple helper function to be used in subsetting functions. It
checks and converts the parameter i
, which can be of type integer
,
logical
or character
to integer vector that can be used as index for
subsetting.
i2index(i, length = length(i), names = NULL)
i2index(i, length = length(i), names = NULL)
i |
|
length |
|
names |
|
integer
with the indices
Johannes Rainer
## With `i` being an `integer` i2index(c(4, 1, 3), length = 10) ## With `i` being a `logical` i2index(c(TRUE, FALSE, FALSE, TRUE, FALSE), length = 5) ## With `i` being a `character` i2index(c("b", "a", "d"), length = 5, names = c("a", "b", "c", "d", "e"))
## With `i` being an `integer` i2index(c(4, 1, 3), length = 10) ## With `i` being a `logical` i2index(c(TRUE, FALSE, FALSE, TRUE, FALSE), length = 5) ## With `i` being a `character` i2index(c("b", "a", "d"), length = 5, names = c("a", "b", "c", "d", "e"))
The impute_matrix
function performs data imputation on matrix
objects instance using a variety of methods (see below).
Users should proceed with care when imputing data and take precautions to assure that the imputation produces valid results, in particular with naive imputations such as replacing missing values with 0.
impute_matrix(x, method, FUN, ...) imputeMethods() impute_neighbour_average(x, k = min(x, na.rm = TRUE), MARGIN = 1L) impute_knn(x, MARGIN = 1L, ...) impute_mle(x, MARGIN = 2L, ...) impute_bpca(x, MARGIN = 1L, ...) impute_RF(x, MARGIN = 2L, ...) impute_mixed(x, randna, mar, mnar, MARGIN = 1L, ...) impute_min(x) impute_MinDet(x, q = 0.01, MARGIN = 2L) impute_MinProb(x, q = 0.01, sigma = 1, MARGIN = 2L) impute_QRILC(x, sigma = 1, MARGIN = 2L) impute_zero(x) impute_with(x, val) impute_fun(x, FUN, MARGIN = 1L, ...) getImputeMargin(fun)
impute_matrix(x, method, FUN, ...) imputeMethods() impute_neighbour_average(x, k = min(x, na.rm = TRUE), MARGIN = 1L) impute_knn(x, MARGIN = 1L, ...) impute_mle(x, MARGIN = 2L, ...) impute_bpca(x, MARGIN = 1L, ...) impute_RF(x, MARGIN = 2L, ...) impute_mixed(x, randna, mar, mnar, MARGIN = 1L, ...) impute_min(x) impute_MinDet(x, q = 0.01, MARGIN = 2L) impute_MinProb(x, q = 0.01, sigma = 1, MARGIN = 2L) impute_QRILC(x, sigma = 1, MARGIN = 2L) impute_zero(x) impute_with(x, val) impute_fun(x, FUN, MARGIN = 1L, ...) getImputeMargin(fun)
x |
A matrix or an |
method |
|
FUN |
A user-provided function that takes a |
... |
Additional parameters passed to the inner imputation function. |
k |
|
MARGIN |
|
randna |
|
mar |
Imputation method for values missing at random. See
|
mnar |
Imputation method for values missing not at
random. See |
q |
|
sigma |
|
val |
|
fun |
The imputation function to get the default margin from. |
A matrix of same class as x
with dimensions dim(x)
.
There are two types of mechanisms resulting in missing values in LC/MSMS experiments.
Missing values resulting from absence of detection of a feature, despite ions being present at detectable concentrations. For example in the case of ion suppression or as a result from the stochastic, data-dependent nature of the DDA MS acquisition method. These missing value are expected to be randomly distributed in the data and are defined, in statistical terms, as missing at random (MAR) or missing completely at random (MCAR).
Biologically relevant missing values resulting from the absence or the low abundance of ions (i.e. below the limit of detection of the instrument). These missing values are not expected to be randomly distributed in the data and are defined as missing not at random (MNAR).
MNAR features should ideally be imputed with a left-censor method,
such as QRILC
below. Conversely, it is recommended to use hot
deck methods such nearest neighbours, Bayesian missing value
imputation or maximum likelihood methods when values are missing
at random.
We assume that the input matrix x
contains features along the
rows and samples along the columns, as is generally the case in
omics data analysis. When performing imputation, the missing
values are taken as a feature-specific property: feature x is
missing because it is absent (in a sample or group), or because it
was missed during acquisition (not selected during data dependent
acquisition) or data processing (not identified or with an
identification score below a chosen false discovery threshold). As
such, imputation is by default performed at the feature
level. In some cases, such as imputation by zero or a global
minimum value, it doesn't matter. In other cases, it does matter
very much, such as for example when using the minimum value
computed for each margin (i.e. row or column) as in the MinDet
method (see below) - do we want to use the minimum of the sample
or of that feature? KNN is another such example: do we consider
the most similar features to impute a feature with missing values,
or the most similar samples to impute all missing in a sample.
The MARGIN
argument can be used to change the imputation margin
from features/rows (MARGIN = 1
) to samples/columns (MARGIN = 2
).
Different imputations will have different default values, and
changing this parameter can have a major impact on imputation results
and downstream results.
Currently, the following imputation methods are available.
MLE: Maximum likelihood-based imputation method using the EM
algorithm. The impute_mle()
function relies on
norm::imp.norm()
. function. See norm::imp.norm()
for details
and additional parameters. Note that here, ...
are passed to
the norm::em.norm()
function, rather to the actual imputation
function imp.norm
.
bpca: Bayesian missing value imputation are available, as
implemented in the pcaMethods::pca()
function. See
pcaMethods::pca()
for details and additional parameters.
RF: Random Forest imputation, as implemented in the
missForest::missForest
function. See missForest::missForest()
] for
details and additional parameters.
knn: Nearest neighbour averaging, as implemented in the
impute::impute.knn
function. See impute::impute.knn()
] for
details and additional parameters.
QRILC: A missing data imputation method that performs the
imputation of left-censored missing data using random draws from
a truncated distribution with parameters estimated using
quantile regression. The impute_QRILC()
function calls
imputeLCMD::impute.QRILC()
from the imputeLCMD
package.
MinDet: Performs the imputation of left-censored missing data
using a deterministic minimal value approach. Considering a
expression data with n samples and p features, for each
sample, the missing entries are replaced with a minimal value
observed in that sample. The minimal value observed is estimated
as being the q-th quantile (default q = 0.01
) of the observed
values in that sample. The implementation in based on the
imputeLCMD::impute.MinDet()
function.
MinProb: Performs the imputation of left-censored missing data
by random draws from a Gaussian distribution centred to a
minimal value. Considering an expression data matrix with n
samples and p features, for each sample, the mean value of the
Gaussian distribution is set to a minimal observed value in that
sample. The minimal value observed is estimated as being the
q-th quantile (default q = 0.01
) of the observed values in
that sample. The standard deviation is estimated as the median
of the feature (or sample) standard deviations. Note that when
estimating the standard deviation of the Gaussian distribution,
only the peptides/proteins which present more than 50\
values are considered. The impute_MinProb()
function calls
imputeLCMD::impute.MinProb()
from the imputeLCMD
package.
min: Replaces the missing values with the smallest non-missing value in the data.
zero: Replaces the missing values with 0.
mixed: A mixed imputation applying two methods (to be defined
by the user as mar
for values missing at random and mnar
for
values missing not at random, see example) on two MCAR/MNAR
subsets of the data (as defined by the user by a randna
logical, of length equal to nrow(object)).
nbavg: Average neighbour imputation for fractions collected along a fractionation/separation gradient, such as sub-cellular fractions. The method assumes that the fraction are ordered along the gradient and is invalid otherwise.
Continuous sets NA
value at the beginning and the end of the
quantitation vectors are set to the lowest observed value in the
data or to a user defined value passed as argument k
. Then,
when a missing value is flanked by two non-missing neighbouring
values, it is imputed by the mean of its direct neighbours.
with: Replaces all missing values with a user-provided value.
none: No imputation is performed and the missing values are left untouched. Implemented in case one wants to only impute value missing at random or not at random with the mixed method.
The imputeMethods()
function returns a vector with valid
imputation method names. Use getImputeMargin()
to get the
default margin for each imputation function.
Laurent Gatto
Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing value estimation methods for DNA microarrays Bioinformatics (2001) 17 (6): 520-525.
Oba et al., A Bayesian missing value estimation method for gene expression profile data, Bioinformatics (2003) 19 (16): 2088-2096.
Cosmin Lazar (2015). imputeLCMD: A collection of methods for left-censored missing data imputation. R package version 2.0. http://CRAN.R-project.org/package=imputeLCMD.
Lazar C, Gatto L, Ferro M, Bruley C, Burger T. Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. J Proteome Res. 2016 Apr 1;15(4):1116-25. doi: 10.1021/acs.jproteome.5b00981. PubMed PMID:26906401.
## test data set.seed(42) m <- matrix(rlnorm(60), 10) dimnames(m) <- list(letters[1:10], LETTERS[1:6]) m[sample(60, 10)] <- NA ## available methods imputeMethods() impute_matrix(m, method = "zero") impute_matrix(m, method = "min") impute_matrix(m, method = "knn") ## same as impute_zero impute_matrix(m, method = "with", val = 0) ## impute with half of the smalles value impute_matrix(m, method = "with", val = min(m, na.rm = TRUE) * 0.5) ## all but third and fourth features' missing values ## are the result of random missing values randna <- rep(TRUE, 10) randna[c(3, 9)] <- FALSE impute_matrix(m, method = "mixed", randna = randna, mar = "knn", mnar = "min") ## user provided (random) imputation function random_imp <- function(x) { m <- mean(x, na.rm = TRUE) sdev <- sd(x, na.rm = TRUE) n <- sum(is.na(x)) x[is.na(x)] <- rnorm(n, mean = m, sd = sdev) x } impute_matrix(m, FUN = random_imp) ## get the default margin getImputeMargin(impute_knn) ## default imputes along features getImputeMargin(impute_mle) ## default imputes along samples getImputeMargin(impute_zero) ## NA: no margin here ## default margin for all MsCoreUtils::impute_* functions sapply(ls("package:MsCoreUtils", pattern = "impute_"), getImputeMargin)
## test data set.seed(42) m <- matrix(rlnorm(60), 10) dimnames(m) <- list(letters[1:10], LETTERS[1:6]) m[sample(60, 10)] <- NA ## available methods imputeMethods() impute_matrix(m, method = "zero") impute_matrix(m, method = "min") impute_matrix(m, method = "knn") ## same as impute_zero impute_matrix(m, method = "with", val = 0) ## impute with half of the smalles value impute_matrix(m, method = "with", val = min(m, na.rm = TRUE) * 0.5) ## all but third and fourth features' missing values ## are the result of random missing values randna <- rep(TRUE, 10) randna[c(3, 9)] <- FALSE impute_matrix(m, method = "mixed", randna = randna, mar = "knn", mnar = "min") ## user provided (random) imputation function random_imp <- function(x) { m <- mean(x, na.rm = TRUE) sdev <- sd(x, na.rm = TRUE) n <- sum(is.na(x)) x[is.na(x)] <- rnorm(n, mean = m, sd = sdev) x } impute_matrix(m, FUN = random_imp) ## get the default margin getImputeMargin(impute_knn) ## default imputes along features getImputeMargin(impute_mle) ## default imputes along samples getImputeMargin(impute_zero) ## NA: no margin here ## default margin for all MsCoreUtils::impute_* functions sapply(ls("package:MsCoreUtils", pattern = "impute_"), getImputeMargin)
These functions are used to check input arguments.
isPeaksMatrix(x)
isPeaksMatrix(x)
x |
object to test. |
isPeaksMatrix
: test for a numeric
matrix with two columns named "mz" and
"intensity". The "mz" column has to be sorted increasingly.
logical(1)
, TRUE
if checks are successful otherwise FALSE
.
Sebastian Gibb
Other helper functions for developers:
between()
,
rbindFill()
,
validPeaksMatrix()
,
vapply1c()
,
which.first()
isPeaksMatrix(1:2) isPeaksMatrix(cbind(mz = 2:1, intensity = 1:2)) isPeaksMatrix(cbind(mz = 1:2, intensity = 1:2))
isPeaksMatrix(1:2) isPeaksMatrix(cbind(mz = 2:1, intensity = 1:2)) isPeaksMatrix(cbind(mz = 1:2, intensity = 1:2))
This function finds local maxima in a numeric vector. A local maximum is
defined as maximum in a window of the current index +/- hws
.
localMaxima(x, hws = 1L)
localMaxima(x, hws = 1L)
x |
|
hws |
|
A logical
of the same length as x
that is TRUE
for each local
maxima.
Sebastian Gibb
Other extreme value functions:
.peakRegionMask()
,
refineCentroids()
,
valleys()
x <- c(1:5, 4:1, 1:10, 9:1, 1:5, 4:1) localMaxima(x) localMaxima(x, hws = 10)
x <- c(1:5, 4:1, 1:10, 9:1, 1:5, 4:1) localMaxima(x) localMaxima(x, hws = 10)
maxi
determines the maximum or mass spectrometry intensity values, e.g.
from a spectrum or chromatogram. In contrast to the base R max()
function
this function returns NA_real_
if all intensity values are NA
or if
length(x)
is 0 (the base R max
function returns -Inf
in these cases).
maxi(x)
maxi(x)
x |
|
numeric(1)
representing the maximum of values in x
. Returns
always a numeric
(double) even if x
is an integer.
Johannes Rainer, Sebastian Gibb
x <- c(3.2, 34.4, 1.3, NA) maxi(x) ## Compared to base R max: max(x) max(x, na.rm = TRUE) max(numeric(), na.rm = TRUE) maxi(numeric()) max(c(NA, NA), na.rm = TRUE) maxi(c(NA, NA))
x <- c(3.2, 34.4, 1.3, NA) maxi(x) ## Compared to base R max: max(x) max(x, na.rm = TRUE) max(numeric(), na.rm = TRUE) maxi(numeric()) max(c(NA, NA), na.rm = TRUE) maxi(c(NA, NA))
Fits an additive model (two way decomposition) using Tukey's median
polish procedure using stats::medpolish()
.
medianPolish(x, verbose = FALSE, ...)
medianPolish(x, verbose = FALSE, ...)
x |
A |
verbose |
Default is |
... |
Additional arguments passed to |
A numeric
vector of length identical to ncol(x)
.
Laurent Gatto
Other Quantitative feature aggregation:
aggregate()
,
colCounts()
,
robustSummary()
x <- matrix(rnorm(30), nrow = 3) medianPolish(x)
x <- matrix(rnorm(30), nrow = 3) medianPolish(x)
This functions estimate the noise in the data.
noise(x, y, method = c("MAD", "SuperSmoother"), ...)
noise(x, y, method = c("MAD", "SuperSmoother"), ...)
x |
|
y |
|
method |
|
... |
further arguments passed to |
A numeric
of the same length as x
with the estimated noise.
Sebastian Gibb
Other noise estimation and smoothing functions:
smooth()
x <- 1:20 y <- c(1:10, 10:1) noise(x, y) noise(x, y, method = "SuperSmoother", span = 1 / 3)
x <- 1:20 y <- c(1:10, 10:1) noise(x, y) noise(x, y, method = "SuperSmoother", span = 1 / 3)
Function to normalise a matrix of quantitative omics data. The
nature of the normalisation is controlled by the method
argument, described below.
normalizeMethods() normalize_matrix(x, method, ...)
normalizeMethods() normalize_matrix(x, method, ...)
x |
A matrix or an |
method |
|
... |
Additional parameters passed to the inner normalisation function. |
The method
parameter can be one of "sum"
, "max"
, "center.mean"
,
"center.median"
, "div.mean"
, "div.median"
, "diff.median"
,
"quantiles
", "quantiles.robust
" or "vsn"
. The normalizeMethods()
function returns a vector of available normalisation methods.
For "sum"
and "max"
, each feature's intensity is divided by the
maximum or the sum of the feature respectively. These two methods are
applied along the features (rows).
"center.mean"
and "center.median"
center the respective sample
(column) intensities by subtracting the respective column means or
medians. "div.mean"
and "div.median"
divide by the column means or
medians.
"diff.median"
centers all samples (columns) so that they all match the
grand median by subtracting the respective columns medians differences to
the grand median.
Using "quantiles"
or "quantiles.robust"
applies (robust) quantile
normalisation, as implemented in preprocessCore::normalize.quantiles()
and preprocessCore::normalize.quantiles.robust()
. "vsn"
uses the
vsn::vsn2()
function. Note that the latter also glog-transforms the
intensities. See respective manuals for more details and function
arguments.
A matrix of same class as x
with dimensions dim(x)
.
Laurent Gatto
The scale()
function that centers (like center.mean
above) and
scales.
normalizeMethods() ## test data set.seed(42) m <- matrix(rlnorm(60), 10) normalize_matrix(m, method = "sum") normalize_matrix(m, method = "max") normalize_matrix(m, method = "quantiles") normalize_matrix(m, method = "center.mean")
normalizeMethods() ## test data set.seed(42) m <- matrix(rlnorm(60), 10) normalize_matrix(m, method = "sum") normalize_matrix(m, method = "max") normalize_matrix(m, method = "quantiles") normalize_matrix(m, method = "center.mean")
ppm
is a small helper function to determine the parts-per-million for a
user-provided value and ppm.
ppm(x, ppm)
ppm(x, ppm)
x |
|
ppm |
|
numeric
: parts-per-million of x
(always a positive value).
Sebastian Gibb
ppm(c(1000, 2000), 5) ppm(c(-300, 200), 5)
ppm(c(1000, 2000), 5) ppm(c(-300, 200), 5)
This function combines instances of matrix
, data.frame
or DataFrame
objects into a single instance adding eventually missing columns (filling
them with NA
s).
rbindFill(...)
rbindFill(...)
... |
2 or more: |
Depending on the input a single matrix
, data.frame
or
DataFrame
.
rbindFill
might not work if one of the columns contains S4 classes.
Johannes Rainer, Sebastian Gibb
Other helper functions for developers:
between()
,
isPeaksMatrix()
,
validPeaksMatrix()
,
vapply1c()
,
which.first()
## Combine matrices a <- matrix(1:9, nrow = 3, ncol = 3) colnames(a) <- c("a", "b", "c") b <- matrix(1:12, nrow = 3, ncol = 4) colnames(b) <- c("b", "a", "d", "e") rbindFill(a, b) rbindFill(b, a, b)
## Combine matrices a <- matrix(1:9, nrow = 3, ncol = 3) colnames(a) <- c("a", "b", "c") b <- matrix(1:12, nrow = 3, ncol = 4) colnames(b) <- c("b", "a", "d", "e") rbindFill(a, b) rbindFill(b, a, b)
This function refines the centroided values of a peak by weighting the y values in the neighbourhood that belong most likely to the same peak.
refineCentroids(x, y, p, k = 2L, threshold = 0.33, descending = FALSE)
refineCentroids(x, y, p, k = 2L, threshold = 0.33, descending = FALSE)
x |
|
y |
|
p |
|
k |
|
threshold |
|
descending |
|
For descending = FALSE
the function looks for the k
nearest neighbouring
data points and use their x
for weighted mean with their corresponding y
values as weights for calculation of the new peak centroid. If k
are chosen
too large it could result in skewed peak centroids, see example below.
If descending = TRUE
is used the k
should be general larger because it is
trimmed automatically to the nearest valleys on both sides of the peak so the
problem with skewed centroids is rare.
Sebastian Gibb, Johannes Rainer
Other extreme value functions:
.peakRegionMask()
,
localMaxima()
,
valleys()
ints <- c(5, 8, 12, 7, 4, 9, 15, 16, 11, 8, 3, 2, 3, 9, 12, 14, 13, 8, 3) mzs <- seq_along(ints) plot(mzs, ints, type = "h") pidx <- as.integer(c(3, 8, 16)) points(mzs[pidx], ints[pidx], pch = 16) ## Use the weighted average considering the adjacent mz mzs1 <- refineCentroids(mzs, ints, pidx, k = 2L, descending = FALSE, threshold = 0) mzs2 <- refineCentroids(mzs, ints, pidx, k = 5L, descending = FALSE, threshold = 0) mzs3 <- refineCentroids(mzs, ints, pidx, k = 5L, descending = TRUE, threshold = 0) points(mzs1, ints[pidx], col = "red", type = "h") ## please recognize the artificial moved centroids of the first peak caused ## by a too large k, here points(mzs2, ints[pidx], col = "blue", type = "h") points(mzs3, ints[pidx], col = "green", type = "h") legend("topright", legend = paste0("k = ", c(2, 5, 5), ", descending =", c("FALSE", "FALSE", "TRUE")), col = c("red", "blue", "green"), lwd = 1)
ints <- c(5, 8, 12, 7, 4, 9, 15, 16, 11, 8, 3, 2, 3, 9, 12, 14, 13, 8, 3) mzs <- seq_along(ints) plot(mzs, ints, type = "h") pidx <- as.integer(c(3, 8, 16)) points(mzs[pidx], ints[pidx], pch = 16) ## Use the weighted average considering the adjacent mz mzs1 <- refineCentroids(mzs, ints, pidx, k = 2L, descending = FALSE, threshold = 0) mzs2 <- refineCentroids(mzs, ints, pidx, k = 5L, descending = FALSE, threshold = 0) mzs3 <- refineCentroids(mzs, ints, pidx, k = 5L, descending = TRUE, threshold = 0) points(mzs1, ints[pidx], col = "red", type = "h") ## please recognize the artificial moved centroids of the first peak caused ## by a too large k, here points(mzs2, ints[pidx], col = "blue", type = "h") points(mzs3, ints[pidx], col = "green", type = "h") legend("topright", legend = paste0("k = ", c(2, 5, 5), ", descending =", c("FALSE", "FALSE", "TRUE")), col = c("red", "blue", "green"), lwd = 1)
rla
calculates the relative log abundances (RLA, see reference) on a
numeric
vector. rowRla
performs row-wise RLA calculations on a numeric
matrix
.
rla( x, f = rep_len(1, length(x)), transform = c("log2", "log10", "identity"), na.rm = TRUE ) rowRla(x, f = rep_len(1, ncol(x)), transform = c("log2", "log10", "identity"))
rla( x, f = rep_len(1, length(x)), transform = c("log2", "log10", "identity"), na.rm = TRUE ) rowRla(x, f = rep_len(1, ncol(x)), transform = c("log2", "log10", "identity"))
x |
|
f |
|
transform |
|
na.rm |
|
The RLA is defined as the (log2) abundance of an analyte relative
to the median across all abundances of that analyte in samples of the
same group. The grouping of values can be defined with parameter f
.
numeric
with the relative log abundances (in log2 scale) with the
same length than x
(for rla
) or matrix
with the same dimensions
than x
(for rowRla
).
Johannes Rainer
De Livera AM, Dias DA, De Souza D, Rupasinghe T, Pyke J, Tull D, Roessner U, McConville M, Speed TP. Normalizing and integrating metabolomics data. Anal Chem 2012 Dec 18;84(24):10768-76.
x <- c(3, 4, 5, 1, 2, 3, 7, 8, 9) grp <- c(1, 1, 1, 2, 2, 2, 3, 3, 3) rla(x, grp) x <- rbind(c(324, 4542, 3422, 3232, 5432, 6535, 3321, 1121), c(12, 3341, 3034, 6540, 34, 4532, 56, 1221)) grp <- c("a", "b", "b", "b", "a", "b", "a", "b") ## row-wise RLA values rowRla(x, grp)
x <- c(3, 4, 5, 1, 2, 3, 7, 8, 9) grp <- c(1, 1, 1, 2, 2, 2, 3, 3, 3) rla(x, grp) x <- rbind(c(324, 4542, 3422, 3232, 5432, 6535, 3321, 1121), c(12, 3341, 3034, 6540, 34, 4532, 56, 1221)) grp <- c("a", "b", "b", "b", "a", "b", "a", "b") ## row-wise RLA values rowRla(x, grp)
This function calculates the robust summarisation for each feature
(protein). Note that the function assumes that the intensities in
input e
are already log-transformed.
robustSummary(x, ...)
robustSummary(x, ...)
x |
A feature by sample |
... |
Additional arguments passed to |
numeric()
vector of length ncol(x)
with robust
summarised values.
Adriaan Sticker, Sebastian Gibb and Laurent Gatto
Other Quantitative feature aggregation:
aggregate()
,
colCounts()
,
medianPolish()
x <- matrix(rnorm(30), nrow = 3) colnames(x) <- letters[1:10] rownames(x) <- LETTERS[1:3] robustSummary(x)
x <- matrix(rnorm(30), nrow = 3) colnames(x) <- letters[1:10] rownames(x) <- LETTERS[1:3] robustSummary(x)
These vectorised functions convert retention times from a numeric
in seconds to/from a character as "mm:ss". rt2character()
performs the numeric to character conversion while rt2numeric()
performs the character to numeric conversion. formatRt()
does
one of the other depending on the input type.
rt2numeric(rt) rt2character(rt) formatRt(rt)
rt2numeric(rt) rt2character(rt) formatRt(rt)
rt |
A vector of retention times of length > 1. Either a
|
A reformatted retention time.
Laurent Gatto
## rt2numeric rt2numeric("25:24") rt2numeric(c("25:24", "25:25", "25:26")) ## rt2character rt2character(1524) rt2character(1) rt2character(1:10) ## formatRt formatRt(1524) formatRt(1) formatRt(1:10) formatRt("25:24") formatRt(c("25:24", "25:25", "25:26"))
## rt2numeric rt2numeric("25:24") rt2numeric(c("25:24", "25:25", "25:26")) ## rt2character rt2character(1524) rt2character(1) rt2character(1:10) ## formatRt formatRt(1524) formatRt(1) formatRt(1:10) formatRt("25:24") formatRt(c("25:24", "25:25", "25:26"))
This function smoothes a numeric vector.
smooth(x, cf) coefMA(hws) coefWMA(hws) coefSG(hws, k = 3L)
smooth(x, cf) coefMA(hws) coefWMA(hws) coefSG(hws, k = 3L)
x |
|
cf |
|
hws |
|
k |
|
For the Savitzky-Golay-Filter the hws
should be smaller than
FWHM of the peaks (full width at half maximum; please find details in
Bromba and Ziegler 1981).
In general the hws
for the (weighted) moving average (coefMA
/coefWMA
)
has to bemuch smaller than for the Savitzky-Golay-Filter to conserve the
peak shape.
smooth
: A numeric
of the same length as x
.
coefMA
: A matrix
with coefficients for a simple moving average.
coefWMA
: A matrix
with coefficients for a weighted moving average.
coefSG
: A matrix
with Savitzky-Golay-Filter coefficients.
coefMA()
: Simple Moving Average
This function calculates the coefficients for a simple moving average.
coefWMA()
: Weighted Moving Average
This function calculates the coefficients for a weighted moving average with
weights depending on the distance from the center calculated as
1/2^abs(-hws:hws)
with the sum of all weigths normalized to 1.
coefSG()
: Savitzky-Golay-Filter
This function calculates the Savitzky-Golay-Coefficients. The additional
argument k
controls the order of the used polynomial. If k
is set to zero
it yield a simple moving average.
The hws
depends on the used method ((weighted) moving
average/Savitzky-Golay).
Sebastian Gibb, Sigurdur Smarason (weighted moving average)
A. Savitzky and M. J. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639.
M. U. Bromba and H. Ziegler. 1981. Application hints for Savitzky-Golay digital smoothing filters. Analytical Chemistry, 53(11), 1583-1586.
Implementation based on: Steinier, J., Termonia, Y., & Deltour, J. (1972). Comments on Smoothing and differentiation of data by simplified least square procedure. Analytical Chemistry, 44(11), 1906-1909.
Other noise estimation and smoothing functions:
noise()
x <- c(1:10, 9:1) plot(x, type = "b", pch = 20) cf <- list(MovingAverage = coefMA(2), WeightedMovingAverage = coefWMA(2), SavitzkyGolay = coefSG(2)) for (i in seq_along(cf)) { lines(smooth(x, cf[[i]]), col = i + 1, pch = 20, type = "b") } legend("bottom", legend = c("x", names(cf)), pch = 20, col = seq_len(length(cf) + 1))
x <- c(1:10, 9:1) plot(x, type = "b", pch = 20) cf <- list(MovingAverage = coefMA(2), WeightedMovingAverage = coefWMA(2), SavitzkyGolay = coefSG(2)) for (i in seq_along(cf)) { lines(smooth(x, cf[[i]]), col = i + 1, pch = 20, type = "b") } legend("bottom", legend = c("x", names(cf)), pch = 20, col = seq_len(length(cf) + 1))
sumi
sums mass spectrometry intensity values, e.g. from a spectrum or
chromatogram. In contrast to the base R sum()
function this function
returns NA_real_
if all intensity values are NA
or if length(x)
is 0.
sumi(x)
sumi(x)
x |
|
numeric(1)
representing the sum of values in x
. Always returns
a numeric (double) even if x
is an integer.
Johannes Rainer
x <- c(3.2, 34.4, 1.3, NA) sumi(x) ## Compared to base R sum: sum(x) sum(x, na.rm = TRUE) sum(numeric(), na.rm = TRUE) sumi(numeric()) sum(c(NA, NA), na.rm = TRUE) sumi(c(NA, NA))
x <- c(3.2, 34.4, 1.3, NA) sumi(x) ## Compared to base R sum: sum(x) sum(x, na.rm = TRUE) sum(numeric(), na.rm = TRUE) sumi(numeric()) sum(c(NA, NA), na.rm = TRUE) sumi(c(NA, NA))
These functions are used to validate input arguments. In general they are
just wrapper around their corresponding is*
function with an error message.
validPeaksMatrix(x)
validPeaksMatrix(x)
x |
object to test. |
validPeaksMatrix
: see isPeaksMatrix
.
logical(1)
, TRUE
if validation are successful otherwise an error
is thrown.
Sebastian Gibb
Other helper functions for developers:
between()
,
isPeaksMatrix()
,
rbindFill()
,
vapply1c()
,
which.first()
try(validPeaksMatrix(1:2)) validPeaksMatrix(cbind(mz = 1:2, intensity = 1:2))
try(validPeaksMatrix(1:2)) validPeaksMatrix(cbind(mz = 1:2, intensity = 1:2))
This function finds the valleys around peaks.
valleys(x, p)
valleys(x, p)
x |
|
p |
|
A matrix
with three columns representing the index of the left
valley, the peak centroid, and the right valley.
The detection of the valleys is based on localMaxima
. It returns the
first occurence of a local maximum (in this specific case the minimum).
For plateaus, e.g. c(0, 0, 0, 1:3, 2:1, 0)
this results in a wrongly
reported left valley index of 1
(instead of 3
, see the example section as
well). In real data this should not be a real problem.
x[x == min(x)] <- Inf
could be used before running valleys
to circumvent
this specific problem but it is not really tested and could cause different
problems.
Sebastian Gibb
Other extreme value functions:
.peakRegionMask()
,
localMaxima()
,
refineCentroids()
ints <- c(5, 8, 12, 7, 4, 9, 15, 16, 11, 8, 3, 2, 3, 2, 9, 12, 14, 13, 8, 3) mzs <- seq_along(ints) peaks <- which(localMaxima(ints, hws = 3)) cols <- seq_along(peaks) + 1 plot(mzs, ints, type = "h", ylim = c(0, 16)) points(mzs[peaks], ints[peaks], col = cols, pch = 20) v <- valleys(ints, peaks) segments(mzs[v[, "left"]], 0, mzs[v[, "right"]], col = cols, lwd = 2) ## Known limitations for plateaus y <- c(0, 0, 0, 0, 0, 1:5, 4:1, 0) valleys(y, 10L) # left should be 5 here but is 1 ## a possible workaround that may cause other problems y[min(y) == y] <- Inf valleys(y, 10L)
ints <- c(5, 8, 12, 7, 4, 9, 15, 16, 11, 8, 3, 2, 3, 2, 9, 12, 14, 13, 8, 3) mzs <- seq_along(ints) peaks <- which(localMaxima(ints, hws = 3)) cols <- seq_along(peaks) + 1 plot(mzs, ints, type = "h", ylim = c(0, 16)) points(mzs[peaks], ints[peaks], col = cols, pch = 20) v <- valleys(ints, peaks) segments(mzs[v[, "left"]], 0, mzs[v[, "right"]], col = cols, lwd = 2) ## Known limitations for plateaus y <- c(0, 0, 0, 0, 0, 1:5, 4:1, 0) valleys(y, 10L) # left should be 5 here but is 1 ## a possible workaround that may cause other problems y[min(y) == y] <- Inf valleys(y, 10L)
These functions are short wrappers around typical vapply
calls for easier
development.
vapply1c(X, FUN, ..., USE.NAMES = FALSE) vapply1d(X, FUN, ..., USE.NAMES = FALSE) vapply1l(X, FUN, ..., USE.NAMES = FALSE)
vapply1c(X, FUN, ..., USE.NAMES = FALSE) vapply1d(X, FUN, ..., USE.NAMES = FALSE) vapply1l(X, FUN, ..., USE.NAMES = FALSE)
X |
a vector (atomic or |
FUN |
the |
... |
optional arguments to |
USE.NAMES |
|
vapply1c
returns a vector of character
s of length X
.
vapply1d
returns a vector of double
s of length X
.
vapply1l
returns a vector of logical
s of length X
.
Sebastian Gibb
Other helper functions for developers:
between()
,
isPeaksMatrix()
,
rbindFill()
,
validPeaksMatrix()
,
which.first()
l <- list(a=1:3, b=4:6) vapply1d(l, sum)
l <- list(a=1:3, b=4:6) vapply1d(l, sum)
Determines the location, i.e., index of the first or last TRUE
value in a logical vector.
which.first(x) which.last(x)
which.first(x) which.last(x)
x |
|
integer
, index of the first/last TRUE
value. integer(0)
if no TRUE
(everything FALSE
or NA
)
was found.
Sebastian Gibb
Other helper functions for developers:
between()
,
isPeaksMatrix()
,
rbindFill()
,
validPeaksMatrix()
,
vapply1c()
l <- 2 <= 1:3 which.first(l) which.last(l)
l <- 2 <= 1:3 which.first(l) which.last(l)