Package 'pmd'

Perform correlation directed analysis for peaks list.

Description

Perform correlation directed analysis for peaks list.

Usage

getcda(list, corcutoff = 0.9, rtcutoff = 10, accuracy = 4)

Arguments

list	a list with mzrt profile
corcutoff	cutoff of the correlation coefficient, default NULL
rtcutoff	cutoff of the distances in retention time hierarchical clustering analysis, default 10
accuracy	measured mass or mass to charge ratio in digits, default 4

Value

list with correlation directed analysis results

See Also

getsda,getrda

Examples

data(spmeinvivo)
cluster <- getpseudospectrum(spmeinvivo)
cbp <- enviGCMS::getfilter(cluster,rowindex = cluster$stdmassindex2)
cda <- getcda(cbp)

---

Get reaction chain for specific mass to charge ratio

Description

Get reaction chain for specific mass to charge ratio

Usage

getchain(
  list,
  diff,
  mass,
  digits = 2,
  accuracy = 4,
  rtcutoff = 10,
  corcutoff = 0.6,
  ppm = 25
)

Arguments

list	a list with mzrt profile
diff	paired mass distance(s) of interests
mass	a specific mass for known compound or a vector of masses. You could also input formula for certain compounds
digits	mass or mass to charge ratio accuracy for pmd, default 2
accuracy	measured mass or mass to charge ratio in digits, default 4
rtcutoff	cutoff of the distances in retention time hierarchical clustering analysis, default 10
corcutoff	cutoff of the correlation coefficient, default 0.6
ppm	all the peaks within this mass accuracy as seed mass or formula

Value

a list with mzrt profile and reaction chain dataframe

Examples

data(spmeinvivo)
# check metabolites of C18H39NO
pmd <- getchain(spmeinvivo,diff = c(2.02,14.02,15.99),mass = 286.3101)
# remove the retention time for mass only data
spmeinvivo$rt <- NULL
pmd <- getchain(spmeinvivo,diff = c(2.02,14.02,15.99),mass = 286.3101)

---

Get reaction pathway chains matching an ordered PMD pattern with quantifiers

Description

Searches a feature network for directed paths whose successive mass differences follow a user-specified PMD pattern. Unlike getchain, which extracts the connected component reachable by any PMD in a set, this function matches an ordered sequence of PMDs, with support for wildcards and regex-style quantifiers. This unifies three use cases: fixed reaction sequences (e.g. glycosylation followed by dehydration), homologous series (repeated +CH2, PEG units, etc.), and paths with unknown intermediates (wildcard steps).

Usage

getchainseq(
  list,
  pattern,
  mass = NULL,
  digits = 4,
  rtcutoff = 10,
  corcutoff = 0.6,
  ppm = 25,
  rtdir = "any",
  max_paths = 1e+05,
  allow_cycles = FALSE
)

Arguments

list	a pmd-style list with mz, rt (optional), data
pattern	numeric vector, list of step specs, or DSL string (see parse_pmd_pattern)
mass	optional seed mass(es) or formula(s); only paths starting within ppm of a seed are returned
digits	PMD matching precision, default 4
rtcutoff	RT hierarchical-clustering cutoff for isomer grouping, default 10
corcutoff	correlation cutoff between linked features, default 0.6; pass NULL to disable
ppm	seed mass ppm tolerance, default 25
rtdir	RT direction per edge: "any", "increasing", "decreasing", or a numeric vector (one entry per unrolled edge, values in {-1, 0, 1})
max_paths	safety cap on returned paths, default 1e5
allow_cycles	if FALSE (default), paths may not revisit a node

Details

Pattern grammar (any of):

• Numeric vector: each element is one fixed-PMD step. Example: c(162.0528, -18.0106) = glycosylation followed by one dehydration.

• List of step specs: each step is a list with pmd (numeric, or NA for wildcard), min (default 1), max (default = min; Inf allowed).

• Character string parsed by parse_pmd_pattern.

Signs of PMDs are significant: +162 = mass gain, -162 = mass loss. This separates e.g. glycosylation from deglycosylation.

Value

the input list with two new elements: sdacseq (a data.table of matched paths) and pattern (the normalized pattern). sdacseq columns: n1..nK (node indices; NA-padded for shorter paths in variable-length matches), path_len, mz_1..mz_K, rt_1..rt_K (if RT present), pmd_1..pmd_{K-1} (observed PMDs), and cor_mean.

See Also

getchain, gethomolog, parse_pmd_pattern

Examples

## Not run: 
data(spmeinvivo)

# Fixed sequence: glycosylation then dehydration
r1 <- getchainseq(spmeinvivo, c(162.0528, -18.0106))

# Homologous series: 2+ CH2 extensions
r2 <- getchainseq(spmeinvivo,
                  list(list(pmd = 14.0157, min = 2, max = Inf)))

# DSL string with wildcards and quantifiers
r3 <- getchainseq(spmeinvivo,
                  "162.0528; -18.0106{0,3}; 14.0157+")

# Anchor to a known compound
r4 <- getchainseq(spmeinvivo, c(162.0528, -18.0106), mass = 286.3101)

## End(Not run)

---

Get Pseudo-Spectrum as peaks cluster based on correlation analysis.

Description

Get Pseudo-Spectrum as peaks cluster based on correlation analysis.

Usage

getcorpseudospectrum(list, corcutoff = 0.9, rtcutoff = 10, accuracy = 4)

Arguments

list	a list with peaks intensity
corcutoff	cutoff of the correlation coefficient, default 0.9
rtcutoff	cutoff of the distances in cluster, default 10
accuracy	measured mass or mass to charge ratio in digits, default 4

Value

list with Pseudo-Spectrum index

Examples

data(spmeinvivo)
pseudo <- getcorpseudospectrum(spmeinvivo)

---

Find homologous series by a repeating PMD unit

Description

Convenience wrapper around getchainseq for the common case of detecting homologous series: chains of features linked by repeated applications of a single PMD unit. Useful for finding alkyl chain series (+CH2 = 14.0157), PEG series (+C2H4O = 44.0262), polymeric artifacts, and similar patterns.

Usage

gethomolog(list, unit = 14.0157, min_len = 3L, max_len = Inf, ...)

Arguments

list	a pmd-style list with mz, rt (optional), data
unit	the repeating PMD, default 14.0157 (CH2)
min_len	minimum number of nodes in the series, default 3
max_len	maximum number of nodes, default Inf
...	passed through to getchainseq (e.g. corcutoff, rtdir, mass, ppm)

Value

as getchainseq

See Also

getchainseq

Examples

## Not run: 
data(spmeinvivo)
gethomolog(spmeinvivo, unit = 14.0157, min_len = 4)
gethomolog(spmeinvivo, unit = 44.0262, min_len = 3)

## End(Not run)

---

read in MSP file as list for ms/ms annotation

Description

read in MSP file as list for ms/ms annotation

Usage

getms2pmd(file, digits = 2, icf = 10)

Arguments

file	the path to your MSP file
digits	mass or mass to charge ratio accuracy for pmd, default 2
icf	intensity cutoff, default 10 percentage

Value

list a list with MSP information for MS/MS annotation

---

read in MSP file as list for EI-MS annotation

Description

read in MSP file as list for EI-MS annotation

Usage

getmspmd(file, digits = 2, icf = 10)

Arguments

file	the path to your MSP file
digits	mass or mass to charge ratio accuracy for pmd, default 0
icf	intensity cutoff, default 10 percentage

Value

list a list with MSP information for EI-MS annotation

---

Calculate Normalized Spectral Entropy (NSE)

Description

Calculate Normalized Spectral Entropy (NSE)

Usage

getnse(x)

Arguments

x	Matrix or dataframe with samples in column and features in row

Value

Normalized Spectral Entropy (NSE) between 0 and 1

Examples

data(spmeinvivo)
getnse(spmeinvivo$data)

---

Filter ions/peaks based on retention time hierarchical clustering, paired mass distances(PMD) and PMD frequency analysis.

Description

Filter ions/peaks based on retention time hierarchical clustering, paired mass distances(PMD) and PMD frequency analysis.

Usage

getpaired(
  list,
  rtcutoff = 10,
  ng = NULL,
  digits = 2,
  accuracy = 4,
  corcutoff = NULL
)

Arguments

list	a peaks list with mass to charge, retention time and intensity data
rtcutoff	cutoff of the distances in retention time hierarchical clustering analysis, default 10
ng	cutoff of global PMD's retention time group numbers, If ng = NULL, 20 percent of RT cluster will be used as ng, default NULL.
digits	mass or mass to charge ratio accuracy for pmd, default 2
accuracy	measured mass or mass to charge ratio in digits, default 4
corcutoff	cutoff of the correlation coefficient, 0.6 is suggested, default NULL

Value

list with tentative isotope, multi-chargers, adducts, and neutral loss peaks' index, retention time clusters.

See Also

getstd,getsda,plotpaired

Examples

data(spmeinvivo)
pmd <- getpaired(spmeinvivo)

---

Get pmd for specific reaction

Description

Get pmd for specific reaction

Usage

getpmd(list, pmd, rtcutoff = 10, corcutoff = NULL, digits = 2, accuracy = 4)

Arguments

list	a list with mzrt profile
pmd	a specific paired mass distance or a vector of pmds
rtcutoff	cutoff of the distances in retention time hierarchical clustering analysis, default 10
corcutoff	cutoff of the correlation coefficient, default NULL
digits	mass or mass to charge ratio accuracy for pmd, default 2
accuracy	measured mass or mass to charge ratio in digits, default 4

Value

list with paired peaks for specific pmd or pmds.

See Also

getpaired,getstd,getsda,getrda

Examples

data(spmeinvivo)
pmd <- getpmd(spmeinvivo,pmd=15.99)

---

Get pmd details for specific reaction after the removal of isotopouge.

Description

Get pmd details for specific reaction after the removal of isotopouge.

Usage

getpmddf(mz, group = NULL, pmd = NULL, digits = 2, mdrange = c(0.25, 0.9))

Arguments

mz	a vector of mass to charge ratio.
group	mass to charge ratio group from either retention time or mass spectrometry imaging segmentation.
pmd	a specific paired mass distance or a vector of pmds
digits	mass or mass to charge ratio accuracy for pmd, default 2.
mdrange	mass defect range to ignore. Default c(0.25,0.9) to retain the possible reaction related paired mass.

Value

dataframe with paired peaks for specific pmd or pmds. When group is provided, a column named net will be generated to show if certain pmd will be local(within the same group) or global(across the groups)

See Also

getpaired,getstd,getsda,getrda

Examples

data(spmeinvivo)
pmddf <- getpmddf(spmeinvivo$mz,pmd=15.99)

---

Link pos mode peak list with neg mode peak list by pmd.

Description

Link pos mode peak list with neg mode peak list by pmd.

Usage

getposneg(pos, neg, pmd = 2.02, digits = 2)

Arguments

pos	a list with mzrt profile collected from positive mode.
neg	a list with mzrt profile collected from negative mode.
pmd	numeric or numeric vector
digits	mass or mass to charge ratio accuracy for pmd, default 2

Value

dataframe with filtered positive and negative peak list

---

Get Pseudo-Spectrum as peaks cluster based on correlation analysis.

Description

Get Pseudo-Spectrum as peaks cluster based on correlation analysis.

Usage

getpseudospectrum(
  list,
  corcutoff = NULL,
  rtcutoff = 10,
  accuracy = 4,
  ng = NULL,
  digits = 2
)

Arguments

list	A list containing peak intensities, m/z values, and retention times. Must include elements: mz, rt, data.
corcutoff	Cutoff value for correlation coefficient (default: 0.9).
rtcutoff	Cutoff value for retention time clustering (default: 10).
accuracy	Number of decimal places for m/z rounding (default: 4).
ng	cutoff of global PMD's retention time group numbers, If ng = NULL, 20 percent of RT cluster will be used as ng, default NULL.
digits	mass or mass to charge ratio accuracy for pmd, default 2

Value

A list with pseudo-spectrum clustering results.

Examples

data(spmeinvivo)
pseudo <- getpseudospectrum(spmeinvivo)

---

Perform structure/reaction directed analysis for mass only.

Description

Perform structure/reaction directed analysis for mass only.

Usage

getrda(
  mz,
  pmd = NULL,
  freqcutoff = 10,
  digits = 3,
  top = 20,
  formula = NULL,
  mdrange = c(0.25, 0.9),
  verbose = FALSE
)

Arguments

mz	numeric vector for independent mass or mass to charge ratio. Mass to charge ratio from GlobalStd algorithm is suggested. Isomers would be excluded automated
pmd	a specific paired mass distance or a vector of pmds, default NULL
freqcutoff	pmd frequency cutoff for structures or reactions, default 10
digits	mass or mass to charge ratio accuracy for pmd, default 3
top	top n pmd frequency cutoff when the freqcutoff is too small for large data set
formula	vector for formula when you don't have mass or mass to charge ratio data
mdrange	mass defect range to ignore. Default c(0.25,0.9) to retain the possible reaction related paired mass
verbose	logic, if TURE, return will be llist with paired mass distances table. Default FALSE.

Value

logical matrix with row as the same order of mz or formula and column as high frequency pmd group when verbose is FALSE

See Also

getsda

Examples

data(spmeinvivo)
pmd <- getpaired(spmeinvivo)
std <- getstd(pmd)
sda <- getrda(spmeinvivo$mz[std$stdmassindex])
sda <- getrda(spmeinvivo$mz, pmd = c(2.016,15.995,18.011,14.016))

---

Get quantitative paired peaks list for specific reaction/pmd

Description

Get quantitative paired peaks list for specific reaction/pmd

Usage

getreact(
  list,
  pmd,
  rtcutoff = 10,
  digits = 2,
  accuracy = 4,
  cvcutoff = 30,
  outlier = FALSE,
  method = "static",
  ...
)

Arguments

list	a list with mzrt profile and data
pmd	a specific paired mass distances
rtcutoff	cutoff of the distances in retention time hierarchical clustering analysis, default 10
digits	mass or mass to charge ratio accuracy for pmd, default 2
accuracy	measured mass or mass to charge ratio in digits, default 4
cvcutoff	ratio or intensity cv cutoff for quantitative paired peaks, default 30
outlier	logical, if true, outlier of ratio will be removed, default False.
method	quantification method can be 'static' or 'dynamic'. See details.
...	other parameters for getpmd

Details

PMD based reaction quantification methods have two options: 'static' will only consider the stable mass pairs across samples and such reactions will be limited by the enzyme or other factors than substrates. 'dynamic' will consider the unstable paired masses by normalization the relatively unstable peak with stable peak between paired masses and such reactions will be limited by one or both peaks in the paired masses.

Value

list with quantitative paired peaks.

See Also

getpaired,getstd,getsda,getrda,getpmd,

Examples

data(spmeinvivo)
pmd <- getreact(spmeinvivo,pmd=15.99)

---

Perform structure/reaction directed analysis for peaks list.

Description

Perform structure/reaction directed analysis for peaks list.

Usage

getsda(
  list,
  rtcutoff = 10,
  corcutoff = NULL,
  digits = 2,
  accuracy = 4,
  freqcutoff = NULL
)

Arguments

list	a list with mzrt profile
rtcutoff	cutoff of the distances in retention time hierarchical clustering analysis, default 10
corcutoff	cutoff of the correlation coefficient, default NULL
digits	mass or mass to charge ratio accuracy for pmd, default 2
accuracy	measured mass or mass to charge ratio in digits, default 4
freqcutoff	pmd frequency cutoff for structures or reactions, default NULL. This cutoff will be found by PMD network analysis when it is NULL.

Value

list with tentative isotope, adducts, and neutral loss peaks' index, retention time clusters.

See Also

getpaired,getstd,plotpaired

Examples

data(spmeinvivo)
pmd <- getpaired(spmeinvivo)
std <- getstd(pmd)
sda <- getsda(std)

---

Evaluate the similarity of two matrices or dataframes

Description

Evaluate the similarity of two matrices or dataframes

Usage

getsim(x, y, dim = NULL)

Arguments

x	Matrix or dataframe with samples in column and features in row (Original data)
y	Matrix or dataframe with samples in column and features in row (Reduced data)
dim	number of retained dimensions for PCASF. Defaults to all.

Value

A numeric vector containing PCASF, RV coefficient, and Mantel test r (Pearson and Spearman).

Examples

data(spmeinvivo)
re <- globalstd(spmeinvivo)
x <- spmeinvivo$data
y <- spmeinvivo$data[re$stdmassindex, ]
getsim(x, y)

---

Identify standard ions through retention time clustering and PMD relationships

Description

Identify standard ions through retention time clustering and PMD relationships

Usage

getstd(list, digits = 2, accuracy = 4)

Arguments

list	A list object from getpaired() containing paired features
digits	Rounding digits for mass differences
accuracy	Mass accuracy for standard ion identification

Value

List with added standard ion indices and metadata

See Also

getpaired,getsda,plotstd

Examples

data(spmeinvivo)
pmd <- getpaired(spmeinvivo)
std <- getstd(pmd)

---

Get multiple injections index for selected retention time

Description

Get multiple injections index for selected retention time

Usage

gettarget(rt, drt = 10, n = 6)

Arguments

rt	retention time vector for peaks in seconds
drt	retention time drift for targeted analysis in seconds, default 10.
n	max ions numbers within retention time drift windows

Value

index for each injection

Examples

data(spmeinvivo)
pmd <- getpaired(spmeinvivo)
std <- getstd(pmd)
index <- gettarget(std$rt[std$stdmassindex])
table(index)

---

GlobalStd algorithm with structure/reaction directed analysis

Description

GlobalStd algorithm with structure/reaction directed analysis

Usage

globalstd(
  list,
  rtcutoff = 10,
  ng = NULL,
  corcutoff = NULL,
  digits = 2,
  accuracy = 4,
  freqcutoff = NULL,
  sda = FALSE
)

Arguments

list	a peaks list with mass to charge, retention time and intensity data
rtcutoff	cutoff of the distances in cluster, default 10
ng	cutoff of global PMD's retention time group numbers, If ng = NULL, 20 percent of RT cluster will be used as ng, default NULL.
corcutoff	cutoff of the correlation coefficient, default NULL
digits	mass or mass to charge ratio accuracy for pmd, default 2
accuracy	measured mass or mass to charge ratio in digits, default 4
freqcutoff	pmd frequency cutoff for structures or reactions, default NULL. This cutoff will be found by PMD network analysis when it is NULL.
sda	logical, option to perform structure/reaction directed analysis, default FALSE.

Value

list with GlobalStd algorithm processed data.

See Also

getpaired,getstd,getsda,plotstd,plotstdsda,plotstdrt

Examples

data(spmeinvivo)
re <- globalstd(spmeinvivo)

---

A dataframe containing HMDB with unique accurate mass pmd with three digits frequency larger than 1 and accuracy percentage larger than 0.9.

Description

A dataframe containing HMDB with unique accurate mass pmd with three digits frequency larger than 1 and accuracy percentage larger than 0.9.

Usage

data(hmdb)

Format

A dataframe with atoms numbers of C, H, O, N, P, S

percentage

accuracy of atom numbers prediction

pmd2

pmd with two digits

pmd

pmd with three digits

---

A dataframe containing reaction related accurate mass pmd and related reaction formula with KEGG ID

Description

A dataframe containing reaction related accurate mass pmd and related reaction formula with KEGG ID

Usage

data(keggrall)

Format

A dataframe with KEGG reaction, their realted pmd and atoms numbers of C, H, O, N, P, S

ID

KEGG reaction ID

pmd

pmd with three digits

---

mass spectrometry contaminants database for PMD check

Description

mass spectrometry contaminants database for PMD check

Usage

data(MaConDa)

Format

A data frame from doi:10.1093/bioinformatics/bts527 with 308 rows and 5 variables:

id

MaConDa ID

name

contaminants

formula

contaminants fomula

exact_mass

exact mass of contaminants

type_of_contaminant

type of contaminant

---

A dataframe containing multiple reaction database ID and their related accurate mass pmd and related reactions

Description

A dataframe containing multiple reaction database ID and their related accurate mass pmd and related reactions

Usage

data(omics)

Format

A dataframe with reaction and their realted pmd

KEGG

KEGG reaction ID

RHEA_ID

RHEA_ID

DIRECTION

reaction direction

MASTER_ID

master reaction RHEA ID

ec

ec reaction ID

ecocyc

ecocyc reaction ID

macie

macie reaction ID

metacyc

metacyc reaction ID

reactome

reactome reaction ID

compounds

reaction related compounds

pmd

pmd with two digits

pmd2

pmd with three digits

---

Parse a PMD pattern string into a list of step specs

Description

Converts a compact string grammar into the list-of-steps format accepted by getchainseq. Steps are separated by semicolons. Each step is a numeric PMD (or * for wildcard) optionally followed by a quantifier:

• + one or more (min=1, max=Inf)

• * zero or more (min=0, max=Inf)

• ? zero or one (min=0, max=1)

• {n} exactly n

• {n,m} n to m times

• {n,} at least n times

Whitespace is ignored.

Usage

parse_pmd_pattern(s)

Arguments

s	a single character string, e.g. "162.0528; -18.0106{0,3}; 14.0157+"

Value

a list of step specs suitable for getchainseq(pattern = ...)

Examples

parse_pmd_pattern("162.0528; -18.0106{0,3}")
parse_pmd_pattern("*; 14.0157+")

---

Compare matrices using PCA similarity factor

Description

Compare matrices using PCA similarity factor

Usage

pcasf(x, y, dim = NULL)

Arguments

x	Matrix with sample in column and features in row
y	Matrix is compared to x.
dim	number of retained dimensions in the comparison. Defaults to all.

Value

Ratio of projected variance to total variance

Author(s)

Edgar Zanella Alvarenga

References

Singhal, A. and Seborg, D. E. (2005), Clustering multivariate time-series data. J. Chemometrics, 19: 427-438. doi: 10.1002/cem.945

Examples

c1 <- matrix(rnorm(16),nrow=4)
c2 <- matrix(rnorm(16),nrow=4)
pcasf(c1, c2)

---

plot PMD KEGG network for certain compounds and output network average distance and degree

Description

plot PMD KEGG network for certain compounds and output network average distance and degree

Usage

plotcn(formula, name, pmd)

Arguments

formula	Chemical formula
name	Compound name
pmd	specific paired mass distances

Examples

plotcn('C6H12O6','Glucose',c(2.016,14.016,15.995))

---

Plot the mass pairs and high frequency mass distances

Description

Plot the mass pairs and high frequency mass distances

Usage

plotpaired(list, index = NULL, digits = 2, ...)

Arguments

list	a list from getpaired function
index	index for PMD value
digits	mass or mass to charge ratio accuracy for pmd, default 2
...	other parameters for plot function

See Also

getpaired, globalstd

Examples

data(spmeinvivo)
pmd <- getpaired(spmeinvivo)
plotpaired(pmd)

---

Plot the retention time group

Description

Plot the retention time group

Usage

plotrtg(list, ...)

Arguments

list	a list from getpaired function
...	other parameters for plot function

See Also

getpaired, globalstd

Examples

data(spmeinvivo)
pmd <- getpaired(spmeinvivo)
plotrtg(pmd)

---

Plot the specific structure directed analysis(SDA) groups

Description

Plot the specific structure directed analysis(SDA) groups

Usage

plotsda(list, ...)

Arguments

list	a list from getpmd function
...	other parameters for plot function

See Also

getstd, globalstd,plotstd,plotpaired,plotstdrt

Examples

data(spmeinvivo)
re <- getpmd(spmeinvivo,pmd=78.9)
plotsda(re)

---

Plot the std mass from GlobalStd algorithm

Description

Plot the std mass from GlobalStd algorithm

Usage

plotstd(list)

Arguments

list	a list from getstd function

See Also

getstd, globalstd

Examples

data(spmeinvivo)
pmd <- getpaired(spmeinvivo)
std <- getstd(pmd)
plotstd(std)

---

Plot the std mass from GlobalStd algorithm in certain retention time groups

Description

Plot the std mass from GlobalStd algorithm in certain retention time groups

Usage

plotstdrt(list, rtcluster, ...)

Arguments

list	a list from getstd function
rtcluster	retention time group index
...	other parameters for plot function

See Also

getstd, globalstd,plotstd,plotpaired,plotstdsda

Examples

data(spmeinvivo)
pmd <- getpaired(spmeinvivo)
std <- getstd(pmd)
plotstdrt(std,rtcluster = 6)

---

Plot the std mass from GlobalStd algorithm in structure directed analysis(SDA) groups

Description

Plot the std mass from GlobalStd algorithm in structure directed analysis(SDA) groups

Usage

plotstdsda(list, index = NULL, ...)

Arguments

list	a list from getsda function
index	index for PMD value
...	other parameters for plot function

See Also

getstd, globalstd,plotstd,plotpaired,plotstdrt

Examples

data(spmeinvivo)
re <- globalstd(spmeinvivo, sda=TRUE)
plotstdsda(re)

---

Shiny application for PMD analysis

Description

Shiny application for PMD analysis

Usage

runPMD()

---

Shiny application for PMD network analysis

Description

Shiny application for PMD network analysis

Usage

runPMDnet()

---

A dataset containing common Paired mass distances of substructure, ions replacements, and reaction

Description

A dataset containing common Paired mass distances of substructure, ions replacements, and reaction

Usage

data(sda)

Format

A data frame with 146 rows and 4 variables:

PMD

Paired mass distances

origin

potential sources

Ref.

references

mode

b for biological reaction and e for environmental reaction

---

A peaks list dataset containing 9 samples from 3 fish with triplicates samples for each fish from LC-MS.

Description

A peaks list dataset containing 9 samples from 3 fish with triplicates samples for each fish from LC-MS.

Usage

data(spmeinvivo)

Format

A list with 4 variables from 1459 LC-MS peaks:

mz

mass to charge ratios

rt

retention time

data

intensity matrix

group

group information

---