| Type: | Package |
| Title: | GC/LC-MS Data Analysis for Environmental Science |
| Version: | 0.8.0 |
| Description: | Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis. |
| URL: | https://github.com/yufree/enviGCMS |
| BugReports: | https://github.com/yufree/enviGCMS/issues |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Suggests: | knitr, testthat, plotly, shiny, rmarkdown, DT, crosstalk |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 3.5) |
| Imports: | Rdisop, BiocParallel, grDevices, graphics, stats, utils, animation (≥ 2.2.3), RColorBrewer, mixtools, data.table, igraph |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-01-14 02:15:57 UTC; yufree |
| Author: | Miao YU  [aut,
cre],
Thanh Wang [ctb] |
| Maintainer: | Miao YU <yufreecas@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-01-14 07:50:02 UTC |
GetIntegration was mainly used for get the integration of certain ion's chromatogram data and plot the data
Description
GetIntegration was mainly used for get the integration of certain ion's chromatogram data and plot the data
Usage
GetIntegration(
data,
rt = c(8.3, 9),
n = 5,
m = 5,
slope = c(2, 2),
baseline = 10,
noslope = TRUE,
smoothit = TRUE,
half = FALSE
)
Arguments
data |
file should be a dataframe with the first column RT and second column intensity of the SIM ions. |
rt |
a rough RT range contained only one peak to get the area |
n |
points in the moving average smooth box, default value is 5 |
m |
numbers of points for regression to get the slope |
slope |
the threshold value for start/stop peak as percentage of max slope |
baseline |
numbers of the points for the baseline of the signal |
noslope |
logical, if using a horizon line to get area or not |
smoothit |
logical, if using an average smooth box or not. If using, n will be used |
half |
logical, if using the left half peak to calculate the area |
Value
integration data such as peak area, peak height, signal and the slope data.
Examples
## Not run:
list <- GetIntegration(data)
## End(Not run)
Get the selected isotopologues at certain MS data
Description
Get the selected isotopologues at certain MS data
Usage
Getisotopologues(formula = "C6H11O6", charge = 1, width = 0.3)
Arguments
formula |
the molecular formula. |
charge |
the charge of that molecular. 1 in EI mode as default |
width |
the width of the peak width on mass spectrum. 0.3 as default for low resolution mass spectrum. |
Examples
## Not run:
# show isotopologues
Getisotopologues(formula = 'C6H11O6', charge = 1, width = 0.3)
## End(Not run)
Just integrate data according to fixed rt and fixed noise area
Description
Just integrate data according to fixed rt and fixed noise area
Usage
Integration(data, rt = c(8.3, 9), brt = c(8.3, 8.4), smoothit = TRUE)
Arguments
data |
file should be a dataframe with the first column RT and second column intensity of the SIM ions. |
rt |
a rough RT range contained only one peak to get the area |
brt |
a rough RT range contained only one peak and enough noises to get the area |
smoothit |
logical, if using an average smooth box or not. If using, n will be used |
Value
area integration data
Examples
## Not run:
area <- Integration(data)
## End(Not run)
define the Mode function
Description
define the Mode function
Usage
Mode(x)
Arguments
x |
vector |
Value
Mode of the vector
Demo data for TBBPA metabolism in Pumpkin
Description
Demo data for TBBPA metabolism in Pumpkin
Usage
data(TBBPA)
Format
A list object with data, mass to charge ratio, retention time and group information. Three pumpkin seeding root samples' peaks list is extracted by xcms online.
References
Hou, X., Yu, M., Liu, A., Wang, X., Li, Y., Liu, J., Schnoor, J.L., Jiang, G., 2019. Glycosylation of Tetrabromobisphenol A in Pumpkin. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.9b02122
Get the MIR and related information from the files
Description
Get the MIR and related information from the files
Usage
batch(file, mz1, mz2)
Arguments
file |
data file, CDF or other format supportted by xcmsRaw |
mz1 |
the lowest mass |
mz2 |
the highest mass |
Value
Molecular isotope ratio
Examples
## Not run:
mr <- batch(data,mz1 = 79, mz2 = 81)
## End(Not run)
Combine two data with similar retention time while different mass range
Description
Combine two data with similar retention time while different mass range
Usage
cbmd(data1, data2, mzstep = 0.1, rtstep = 0.01)
Arguments
data1 |
data file path of lower mass range |
data2 |
data file path of higher mass range |
mzstep |
the m/z step for generating matrix data from raw mass spectral data |
rtstep |
the alignment accuracy of retention time, e.g. 0.01 means the retention times of combined data should be the same at the accuracy 0.01s. Higher rtstep would return less scans for combined data |
Value
matrix with the row as scantime in second and column as m/z
Examples
## Not run:
# mz100_200 and mz201_300 were the path to the raw data
matrix <- getmd(mz100_200,mz201_300)
## End(Not run)
Perform MS/MS dot product annotation for mgf file
Description
Perform MS/MS dot product annotation for mgf file
Usage
dotpanno(file, db = NULL, ppm = 10, prems = 1.1, binstep = 1, consinc = 0.6)
Arguments
file |
mgf file generated from MS/MS data |
db |
database could be list object from 'getMSP' |
ppm |
mass accuracy, default 10 |
prems |
precursor mass range, default 1.1 to include M+H or M-H |
binstep |
bin step for consin similarity |
consinc |
consin similarity cutoff for annotation. Default 0.6. |
Value
list with MSMS annotation results
Deprecated functions in package enviGCMS.
Description
The functions listed below are deprecated and will be defunct in
the near future. When possible, alternative functions with similar
functionality are also mentioned. Help pages for deprecated functions are
available at help("<function>-deprecated").
find line of the regression model for GC-MS
Description
find line of the regression model for GC-MS
Usage
findline(data, threshold = 2, temp = c(100, 320))
Arguments
data |
imported data matrix of GC-MS |
threshold |
the threshold of the response (log based 10) |
temp |
the scale of the oven temperature (constant rate) |
Value
list linear regression model for the matrix
Examples
## Not run:
data(matrix)
findline(matrix)
## End(Not run)
Find lipid class of metabolites base on referenced Kendrick mass defect
Description
Find lipid class of metabolites base on referenced Kendrick mass defect
Usage
findlipid(list, mode = "pos")
Arguments
list |
list with data as peaks list, mz, rt and group information, retention time should be in seconds |
mode |
'pos' for positive mode, 'neg' for negative mode and 'none' for neutral mass, only support [M+H] and [M-H] for each mode |
Value
list list with dataframe with the lipid referenced Kendrick mass defect(RKMD) and logical for class
References
Method for the Identification of Lipid Classes Based on Referenced Kendrick Mass Analysis. Lerno LA, German JB, Lebrilla CB. Anal Chem. 2010 May 15;82(10):4236–45.
Examples
data(list)
RKMD <- findlipid(list)
Screen metabolites by Mass Defect
Description
Screen metabolites by Mass Defect
Usage
findmet(list, mass, mdr = 50)
Arguments
list |
list with data as peaks list, mz, rt and group information, retention time should be in seconds |
mass |
mass to charge ratio of specific compounds |
mdr |
mass defect range, default 50mDa |
Value
list with filtered metabolites mass to charge index of certain compound
Screen organohalogen compounds by retention time, mass defect analysis and isotope relationship modified by literature report. Also support compounds with [M] and [M+2] ratio cutoff.
Description
Screen organohalogen compounds by retention time, mass defect analysis and isotope relationship modified by literature report. Also support compounds with [M] and [M+2] ratio cutoff.
Usage
findohc(
list,
sf = 78/77.91051,
step = 0.001,
stepsd1 = 0.003,
stepsd2 = 0.005,
mzc = 700,
cutoffint = 1000,
cutoffr = 0.4,
clustercf = 10
)
Arguments
list |
list with data as peaks list, mz, rt and group information, retention time should be in seconds |
sf |
scale factor, default 78/77.91051(Br) |
step |
mass defect step, default 0.001 |
stepsd1 |
mass defect uncertainty for lower mass, default 0.003 |
stepsd2 |
mass defect uncertainty for higher mass, default 0.005 |
mzc |
threshold of lower mass and higher mass, default 700 |
cutoffint |
the cutoff of intensity, default 1000 |
cutoffr |
the cutoff of [M] and [M+2] ratio, default 0.4 |
clustercf |
the cutoff of cluster analysis to separate two different ions groups for retention time, default 10 |
Value
list with filtered organohalogen compounds
References
Identification of Novel Brominated Compounds in Flame Retarded Plastics Containing TBBPA by Combining Isotope Pattern and Mass Defect Cluster Analysis Ana Ballesteros-Gómez, Joaquín Ballesteros, Xavier Ortiz, Willem Jonker, Rick Helmus, Karl J. Jobst, John R. Parsons, and Eric J. Reiner Environmental Science & Technology 2017 51 (3), 1518-1526 DOI: 10.1021/acs.est.6b03294
Find PFCs based on mass defect analysis
Description
Find PFCs based on mass defect analysis
Usage
findpfc(list)
Arguments
list |
list with data as peaks list, mz, rt and group information, retention time should be in seconds |
Value
list list with potential PFCs compounds index
References
Liu, Y.; D’Agostino, L. A.; Qu, G.; Jiang, G.; Martin, J. W. High-Resolution Mass Spectrometry (HRMS) Methods for Nontarget Discovery and Characterization of Poly- and per-Fluoroalkyl Substances (PFASs) in Environmental and Human Samples. TrAC Trends in Analytical Chemistry 2019, 121, 115420.
Examples
data(list)
pfc <- findpfc(list)
read in MSP file as list for ms/ms or ms(EI) annotation
Description
read in MSP file as list for ms/ms or ms(EI) annotation
Usage
getMSP(file)
Arguments
file |
the path to your MSP file |
Value
list a list with MSP information for annotation
get the data of QC compound for a group of data
Description
get the data of QC compound for a group of data
Usage
getQCraw(path, mzrange, rtrange, index = NULL)
Arguments
path |
data path for your QC samples |
mzrange |
mass of the QC compound |
rtrange |
retention time of the QC compound |
index |
index of the files contained QC compounds, default is all of the compounds |
Value
number vector, each number indicate the peak area of that mass and retention time range
Align two peaks vectors by mass to charge ratio and/or retention time
Description
Align two peaks vectors by mass to charge ratio and/or retention time
Usage
getalign(mz1, mz2, rt1 = NULL, rt2 = NULL, ppm = 10, deltart = 10)
Arguments
mz1 |
the mass to charge of reference peaks |
mz2 |
the mass to charge of peaks to be aligned |
rt1 |
retention time of reference peaks |
rt2 |
retention time of peaks to be aligned |
ppm |
mass accuracy, default 10 |
deltart |
retention time shift table, default 10 seconds |
Value
data frame with aligned peaks table
Examples
mz1 <- c(221.1171, 227.1390, 229.1546, 233.1497, 271.0790 )
mz2 <- c(282.279, 281.113, 227.139, 227.139, 302.207)
rt1 <- c(590.8710, 251.3820, 102.9230, 85.8850, 313.8240)
rt2 <- c(787.08, 160.02, 251.76, 251.76, 220.26)
getalign(mz1,mz2,rt1,rt2)
Align mass to charge ratio and/or retention time to remove redundancy
Description
Align mass to charge ratio and/or retention time to remove redundancy
Usage
getalign2(mz, rt, ppm = 5, deltart = 5)
Arguments
mz |
the mass to charge of reference peaks |
rt |
retention time of reference peaks |
ppm |
mass accuracy, default 10 |
deltart |
retention time shift table, default 10 seconds |
Value
index for
Examples
mz <- c(221.1171, 221.1170, 229.1546, 233.1497, 271.0790 )
rt <- c(590.8710, 587.3820, 102.9230, 85.8850, 313.8240)
getalign2(mz,rt)
Get the peak list with blank samples' peaks removed
Description
Get the peak list with blank samples' peaks removed
Usage
getbgremove(
xset,
method = "medret",
intensity = "into",
file = NULL,
rsdcf = 30,
inscf = 1000
)
Arguments
xset |
the xcmsset object with blank and certain group samples' data |
method |
parameter for groupval function |
intensity |
parameter for groupval function |
file |
file name for further annotation, default NULL |
rsdcf |
rsd cutoff for peaks, default 30 |
inscf |
intensity cutoff for peaks, default 1000 |
Value
diff report
Examples
## Not run:
library(faahKO)
cdfpath <- system.file("cdf", package = "faahKO")
xset <- getdata(cdfpath, pmethod = ' ')
getbgremove(xset)
## End(Not run)
Get the report for biological replicates.
Description
Get the report for biological replicates.
Usage
getbiotechrep(
xset,
method = "medret",
intensity = "into",
file = NULL,
rsdcf = 30,
inscf = 1000
)
Arguments
xset |
the xcmsset object which for all of your technique replicates for bio replicated sample in single group |
method |
parameter for groupval function |
intensity |
parameter for groupval function |
file |
file name for further annotation, default NULL |
rsdcf |
rsd cutoff for peaks, default 30 |
inscf |
intensity cutoff for peaks, default 0 |
Value
dataframe with mean, standard deviation and RSD for those technique replicates & biological replicates combined with raw data
Align multiple peaks list to one peak list
Description
Align multiple peaks list to one peak list
Usage
getcompare(..., index = 1, ppm = 5, deltart = 5)
Arguments
... |
peaks list, mzrt objects |
index |
numeric, the index of reference peaks. |
ppm |
pmd mass accuracy, default 5 |
deltart |
retention time shift table, default 10 seconds |
Value
list object with aligned mzrt objects
Convert an list object to csv file.
Description
Convert an list object to csv file.
Usage
getcsv(list, name, mzdigit = 4, rtdigit = 1, type = "o", target = FALSE, ...)
Arguments
list |
list with data as peaks list, mz, rt and group information |
name |
result name for csv and/or eic file, default NULL |
mzdigit |
m/z digits of row names of data frame, default 4 |
rtdigit |
retention time digits of row names of data frame, default 1 |
type |
csv format for further analysis, m means Metaboanalyst, a means xMSannotator, p means Mummichog(NA values are imputed by 'getimputation', and F test is used here to generate stats and p value), o means full information csv (for 'pmd' package), default o. mapo could output all those format files. |
target |
logical, preserve original rowname of data or not for target data, default FALSE. |
... |
other parameters for 'write.table' |
Value
NULL, csv file
References
Li, S.; Park, Y.; Duraisingham, S.; Strobel, F. H.; Khan, N.; Soltow, Q. A.; Jones, D. P.; Pulendran, B. PLOS Computational Biology 2013, 9 (7), e1003123. Xia, J., Sinelnikov, I.V., Han, B., Wishart, D.S., 2015. MetaboAnalyst 3.0—making metabolomics more meaningful. Nucl. Acids Res. 43, W251–W257.
Examples
## Not run:
data(list)
getcsv(list,name='demo')
## End(Not run)
Get xcmsset object in one step with optimized methods.
Description
Get xcmsset object in one step with optimized methods.
Usage
getdata(
path,
index = FALSE,
BPPARAM = BiocParallel::SnowParam(),
pmethod = "hplcorbitrap",
minfrac = 0.67,
...
)
Arguments
path |
the path to your data |
index |
the index of the files |
BPPARAM |
used for BiocParallel package |
pmethod |
parameters used for different instrumentals such as 'hplcorbitrap', 'uplcorbitrap', 'hplcqtof', 'hplchqtof', 'uplcqtof', 'uplchqtof'. The parameters were from the reference |
minfrac |
minimum fraction of samples necessary in at least one of the sample groups for it to be a valid group, default 0.67 |
... |
arguments for xcmsSet function |
Details
the parameters are extracted from the papers. If you use name other than the name above, you will use the default setting of XCMS. Also I suggest IPO packages or apLCMS packages to get reasonable data for your own instrumental. If you want to summit the results to a paper, remember to include those parameters.
Value
a xcmsset object for that path or selected samples
References
Patti, G. J.; Tautenhahn, R.; Siuzdak, G. Nat. Protocols 2012, 7 (3), 508–516.
See Also
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
xset <- getdata(cdfpath, pmethod = ' ')
## End(Not run)
Get XCMSnExp object in one step from structured folder path for xcms 3.
Description
Get XCMSnExp object in one step from structured folder path for xcms 3.
Usage
getdata2(
path,
index = FALSE,
snames = NULL,
sclass = NULL,
phenoData = NULL,
BPPARAM = BiocParallel::SnowParam(),
mode = "onDisk",
ppp,
rtp,
gpp,
fpp
)
Arguments
path |
the path to your data |
index |
the index of the files |
snames |
sample names. By default the file name without extension is used |
sclass |
sample classes. |
phenoData |
data.frame or NAnnotatedDataFrame defining the sample names and classes and other sample related properties. If not provided, the argument sclass or the subdirectories in which the samples are stored will be used to specify sample grouping. |
BPPARAM |
used for BiocParallel package |
mode |
'inMemory' or 'onDisk' see ‘?MSnbase::readMSData' for details, default ’onDisk' |
ppp |
parameters for peaks picking, e.g. xcms::CentWaveParam() |
rtp |
parameters for retention time correction, e.g. xcms::ObiwarpParam() |
gpp |
parameters for peaks grouping, e.g. xcms::PeakDensityParam() |
fpp |
parameters for peaks filling, e.g. xcms::FillChromPeaksParam(), PeakGroupsParam() |
Details
This is a wrap function for metabolomics data process for xcms 3.
Value
a XCMSnExp object with processed data
See Also
Generate the group level rsd and average intensity based on DoE,
Description
Generate the group level rsd and average intensity based on DoE,
Usage
getdoe(
list,
inscf = 5,
rsdcf = 100,
rsdcft = 30,
imputation = "l",
tr = FALSE,
BPPARAM = BiocParallel::bpparam()
)
Arguments
list |
list with data as peaks list, mz, rt and group information |
inscf |
Log intensity cutoff for peaks across samples. If any peaks show a intensity higher than the cutoff in any samples, this peaks would not be filtered. default 5 |
rsdcf |
the rsd cutoff of all peaks in all group |
rsdcft |
the rsd cutoff of all peaks in technical replicates |
imputation |
parameters for 'getimputation' function method |
tr |
logical. TRUE means dataset with technical replicates at the base level folder |
BPPARAM |
An optional BiocParallelParam instance determining the parallel back-end to be used during evaluation. |
Value
list with group mean, standard deviation, and relative standard deviation for all peaks, and filtered peaks index
See Also
Examples
data(list)
getdoe(list)
Density weighted intensity for one sample
Description
Density weighted intensity for one sample
Usage
getdwtus(peak, n = 512, log = FALSE)
Arguments
peak |
peaks intensity one sample |
n |
the number of equally spaced points at which the density is to be estimated, default 512 |
log |
log transformation |
Value
Density weighted intensity for one sample
Examples
data(list)
getdwtus(list$data[,1])
Get the features from anova, with p value, q value, rsd and power restriction
Description
Get the features from anova, with p value, q value, rsd and power restriction
Usage
getfeaturesanova(
list,
power = 0.8,
pt = 0.05,
qt = 0.05,
n = 3,
ng = 3,
rsdcf = 100,
inscf = 5,
imputation = "l",
index = NULL
)
Arguments
list |
list with data as peaks list, mz, rt and group information (more than two groups) |
power |
defined power |
pt |
p value threshold |
qt |
q value threshold, BH adjust |
n |
sample numbers in one group |
ng |
group numbers |
rsdcf |
the rsd cutoff of all peaks in all group |
inscf |
Log intensity cutoff for peaks across samples. If any peaks show a intensity higher than the cutoff in any samples, this peaks would not be filtered. default 5 |
imputation |
parameters for 'getimputation' function method |
index |
the index of peaks considered, default NULL |
Value
dataframe with peaks fit the setting above
Get the features from t test, with p value, q value, rsd and power restriction
Description
Get the features from t test, with p value, q value, rsd and power restriction
Usage
getfeaturest(list, power = 0.8, pt = 0.05, qt = 0.05, n = 3, imputation = "l")
Arguments
list |
list with data as peaks list, mz, rt and group information (two groups) |
power |
defined power |
pt |
p value threshold |
qt |
q value threshold, BH adjust |
n |
sample numbers in one group |
imputation |
parameters for 'getimputation' function method |
Value
dataframe with peaks fit the setting above
Filter the data based on row and column index
Description
Filter the data based on row and column index
Usage
getfilter(list, rowindex = TRUE, colindex = TRUE, name = NULL, type = "o", ...)
Arguments
list |
list with data as peaks list, mz, rt and group information |
rowindex |
logical, row index to keep |
colindex |
logical, column index to keep |
name |
file name for csv and/or eic file, default NULL |
type |
csv format for further analysis, m means Metaboanalyst, a means xMSannotator, p means Mummichog(NA values are imputed by 'getimputation', and F test is used here to generate stats and p value), o means full information csv (for 'pmd' package), default o. mapo could output all those format files. |
... |
other parameters for 'getcsv' |
Value
list with remain peaks, and filtered peaks index
See Also
Examples
data(list)
li <- getdoe(list)
lif <- getfilter(li,rowindex = li$rsdindex)
Get chemical formula for mass to charge ratio.
Description
Get chemical formula for mass to charge ratio.
Usage
getformula(
mz,
charge = 0,
window = 0.001,
elements = list(C = c(1, 50), H = c(1, 50), N = c(0, 50), O = c(0, 50), P = c(0, 1), S
= c(0, 1))
)
Arguments
mz |
a vector with mass to charge ratio |
charge |
The charge value of the formula, default 0 for autodetect |
window |
The window accuracy in the same units as mass |
elements |
Elements list to take into account. |
Value
list with chemical formula
Get the report for samples with biological and technique replicates in different groups
Description
Get the report for samples with biological and technique replicates in different groups
Usage
getgrouprep(
xset,
file = NULL,
method = "medret",
intensity = "into",
rsdcf = 30,
inscf = 1000
)
Arguments
xset |
the xcmsset object all of samples with technique replicates |
file |
file name for the peaklist to MetaboAnalyst |
method |
parameter for groupval function |
intensity |
parameter for groupval function |
rsdcf |
rsd cutoff for peaks, default 30 |
inscf |
intensity cutoff for peaks, default 1000 |
Value
dataframe with mean, standard deviation and RSD for those technique replicates & biological replicates combined with raw data in different groups if file are defaults NULL.
Impute the peaks list data
Description
Impute the peaks list data
Usage
getimputation(list, method = "l")
Arguments
list |
list with data as peaks list, mz, rt and group information |
method |
'r' means remove, 'l' means use half the minimum of the values across the peaks list, 'mean' means mean of the values across the samples, 'median' means median of the values across the samples, '0' means 0, '1' means 1. Default 'l'. |
Value
list with imputed peaks
See Also
Examples
data(list)
getimputation(list)
Get the exact mass of the isotopologues from a chemical formula or reaction's isotope patterns with the highest abundances
Description
Get the exact mass of the isotopologues from a chemical formula or reaction's isotope patterns with the highest abundances
Usage
getmass(data)
Arguments
data |
a chemical formula or reaction e.g. 'Cl-H', 'C2H4' |
Value
numerical vector
Get mass defect with certain scaled factor
Description
Get mass defect with certain scaled factor
Usage
getmassdefect(mass, sf)
Arguments
mass |
vector of mass |
sf |
scaled factors |
Value
dataframe with mass, scaled mass and scaled mass defect
See Also
Examples
mass <- c(100.1022,245.2122,267.3144,400.1222,707.2294)
sf <- 0.9988
mf <- getmassdefect(mass,sf)
Import data and return the annotated matrix for GC/LC-MS by m/z range and retention time
Description
Import data and return the annotated matrix for GC/LC-MS by m/z range and retention time
Usage
getmd(data, mzstep = 0.1, mzrange = FALSE, rtrange = FALSE)
Arguments
data |
file type which xcmsRaw could handle |
mzstep |
the m/z step for generating matrix data from raw mass spectral data |
mzrange |
vector range of the m/z, default all |
rtrange |
vector range of the retention time, default all |
Value
matrix with the row as increasing m/z second and column as increasing scantime
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
matrix <- getmd(cdffiles[1])
## End(Not run)
Get the high order unit based Mass Defect
Description
Get the high order unit based Mass Defect
Usage
getmdh(mz, cus = c("CH2,H2"), method = "round")
Arguments
mz |
numeric vector for exact mass |
cus |
chemical formula or reaction |
method |
you could use 'round', 'floor' or 'ceiling' |
Value
high order Mass Defect with details
Examples
## Not run:
getmdh(getmass('C2H4')))
## End(Not run)
Get the raw Mass Defect
Description
Get the raw Mass Defect
Usage
getmdr(mz)
Arguments
mz |
numeric vector for exact mass |
Value
raw Mass Defect
Examples
getmdr(28.0313)
Get the mzrt profile and group information for batch correction and plot as a list directly from path with default setting
Description
Get the mzrt profile and group information for batch correction and plot as a list directly from path with default setting
Usage
getmr(
path,
index = FALSE,
BPPARAM = BiocParallel::SnowParam(),
pmethod = "hplcorbitrap",
minfrac = 0.67,
...
)
Arguments
path |
the path to your data |
index |
the index of the files |
BPPARAM |
used for BiocParallel package |
pmethod |
parameters used for different instrumentals such as 'hplcorbitrap', 'uplcorbitrap', 'hplcqtof', 'hplchqtof', 'uplcqtof', 'uplchqtof'. The parameters were from the references |
minfrac |
minimum fraction of samples necessary in at least one of the sample groups for it to be a valid group, default 0.67 |
... |
arguments for xcmsSet function |
Value
list with rtmz profile and group infomation
See Also
getdata,getupload, getmzrt, getdoe
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
list <- getmr(cdfpath, pmethod = ' ')
## End(Not run)
Annotation of MS1 data by compounds database by predefined paired mass distance
Description
Annotation of MS1 data by compounds database by predefined paired mass distance
Usage
getms1anno(pmd, mz, ppm = 10, db = NULL)
Arguments
pmd |
adducts formula or paired mass distance for ions |
mz |
unknown mass to charge ratios vector |
ppm |
mass accuracy |
db |
compounds database as dataframe. Two required columns are name and monoisotopic molecular weight with column names of name and mass |
Value
list or data frame
Get the mzrt profile and group information as a mzrt list and/or save them as csv or rds for further analysis.
Description
Get the mzrt profile and group information as a mzrt list and/or save them as csv or rds for further analysis.
Usage
getmzrt(
xset,
name = NULL,
mzdigit = 4,
rtdigit = 1,
method = "medret",
value = "into",
eic = FALSE,
type = "o"
)
Arguments
xset |
xcmsSet/XCMSnExp objects |
name |
file name for csv and/or eic file, default NULL |
mzdigit |
m/z digits of row names of data frame, default 4 |
rtdigit |
retention time digits of row names of data frame, default 1 |
method |
parameter for groupval or featureDefinitions function, default medret |
value |
parameter for groupval or featureDefinitions function, default into |
eic |
logical, save xcmsSet and xcmsEIC objects for further investigation with the same name of files, you will need raw files in the same directory as defined in xcmsSet to extract the EIC based on the binned data. You could use ‘plot' to plot EIC for specific peaks. For example, 'plot(xcmsEIC,xcmsSet,groupidx = ’M123.4567T278.9')' could show the EIC for certain peaks with m/z 206 and retention time 2789. default F |
type |
csv format for further analysis, m means Metaboanalyst, a means xMSannotator, p means Mummichog(NA values are imputed by 'getimputation', and F test is used here to generate stats and p value), o means full information csv (for 'pmd' package), default o. mapo could output all those format files. |
Value
mzrt object, a list with mzrt profile and group information
References
Smith, C.A., Want, E.J., O’Maille, G., Abagyan, R., Siuzdak, G., 2006. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal. Chem. 78, 779–787.
See Also
getdata,getdata2, getdoe, getcsv, getfilter
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
xset <- getdata(cdfpath, pmethod = ' ')
getmzrt(xset, name = 'demo', type = 'mapo')
## End(Not run)
Get the mzrt profile and group information for batch correction and plot as a list for xcms 3 object
Description
Get the mzrt profile and group information for batch correction and plot as a list for xcms 3 object
Usage
getmzrt2(xset, name = NULL)
Arguments
xset |
a XCMSnExp object with processed data |
name |
file name for csv file, default NULL |
Value
list with rtmz profile and group information
See Also
getdata2,getupload2, getmzrt, getdoe,getmzrtcsv
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
xset <- getdata2(cdfpath,
ppp = xcms::MatchedFilterParam(),
rtp = xcms::ObiwarpParam(),
gpp = xcms::PeakDensityParam())
getmzrt2(xset)
## End(Not run)
Covert the peaks list csv file into list
Description
Covert the peaks list csv file into list
Usage
getmzrtcsv(path)
Arguments
path |
the path to your csv file |
Value
list with rtmz profile and group information as the first row
See Also
Get the overlap peaks by mass and retention time range
Description
Get the overlap peaks by mass and retention time range
Usage
getoverlappeak(list1, list2)
Arguments
list1 |
list with data as peaks list, mz, rt, mzrange, rtrange and group information to be overlapped |
list2 |
list with data as peaks list, mz, rt, mzrange, rtrange and group information to overlap |
Value
logical index for list 1's peaks
See Also
Merge positive and negative mode data
Description
Merge positive and negative mode data
Usage
getpn(pos, neg, ppm = 5, pmd = 2.02, digits = 2, cutoff = 0.9)
Arguments
pos |
a list with mzrt profile collected from positive mode. The sample order should match the negative mode. |
neg |
a list with mzrt profile collected from negative mode.The sample order should match the positive mode. |
ppm |
pmd mass accuracy, default 5 |
pmd |
numeric or numeric vector |
digits |
mass or mass to charge ratio accuracy for pmd, default 2 |
cutoff |
correlation coefficients, default 0.9 |
Value
mzrt object with group information from pos mode
Get the index with power restriction for certain study with BH adjusted p-value and certain power.
Description
Get the index with power restriction for certain study with BH adjusted p-value and certain power.
Usage
getpower(list, pt = 0.05, qt = 0.05, powert = 0.8, imputation = "l")
Arguments
list |
list with data as peaks list, mz, rt and group information |
pt |
p value threshold, default 0.05 |
qt |
q value threshold, BH adjust, default 0.05 |
powert |
power cutoff, default 0.8 |
imputation |
parameters for 'getimputation' function method |
Value
list with current power and sample numbers for each peaks
See Also
Examples
data(list)
getpower(list)
Compute pooled QC linear index according to run order
Description
Compute pooled QC linear index according to run order
Usage
getpqsi(data, order, n = 5)
Arguments
data |
peaks intensity list with row as peaks and column as samples |
order |
run order of pooled QC samples |
n |
samples numbers used for linear regression |
Value
vector for the peaks proportion with significant changes in linear regression after FDR control.
Get a mzrt list and/or save mz and rt range as csv file.
Description
Get a mzrt list and/or save mz and rt range as csv file.
Usage
getrangecsv(list, name, ...)
Arguments
list |
list with data as peaks list, mz, rt and group information |
name |
result name for csv and/or eic file, default NULL |
... |
other parameters for 'write.table' |
Value
NULL, csv file
Perform peaks list alignment and return features table
Description
Perform peaks list alignment and return features table
Usage
getretcor(list, ts = 1, ppm = 10, deltart = 5, FUN)
Arguments
list |
each element should be a data.frame with mz, rt and ins as m/z, retention time in seconds and intensity of certain peaks. |
ts |
template sample index in the list, default 1 |
ppm |
mass accuracy, default 10 |
deltart |
retention time shift table, default 5 seconds |
FUN |
function to deal with multiple aligned peaks from one sample |
Value
mzrt object without group information
Get the Relative Mass Defect
Description
Get the Relative Mass Defect
Usage
getrmd(mz)
Arguments
mz |
numeric vector for exact mass |
Value
Relative Mass Defect
Examples
getrmd(28.0313)
output the similarity of two dataset
Description
output the similarity of two dataset
Usage
getsim(xset1, xset2)
Arguments
xset1 |
the first dataset |
xset2 |
the second dateset |
Value
similarity on retention time and rsd
Get the report for technique replicates.
Description
Get the report for technique replicates.
Usage
gettechrep(
xset,
method = "medret",
intensity = "into",
file = NULL,
rsdcf = 30,
inscf = 1000
)
Arguments
xset |
the xcmsset object which for all of your technique replicates for one sample |
method |
parameter for groupval function |
intensity |
parameter for groupval function |
file |
file name for further annotation, default NULL |
rsdcf |
rsd cutoff for peaks, default 30 |
inscf |
intensity cutoff for peaks, default 1000 |
Value
dataframe with mean, standard deviation and RSD for those technique replicates combined with raw data
Get the time series or two factor DoE report for samples with biological and technique replicates in different groups
Description
Get the time series or two factor DoE report for samples with biological and technique replicates in different groups
Usage
gettimegrouprep(
xset,
file = NULL,
method = "medret",
intensity = "into",
rsdcf = 30,
inscf = 1000
)
Arguments
xset |
the xcmsset object all of samples with technique replicates in time series or two factor DoE |
file |
file name for the peaklist to MetaboAnalyst |
method |
parameter for groupval function |
intensity |
parameter for groupval function |
rsdcf |
rsd cutoff for peaks, default 30 |
inscf |
intensity cutoff for peaks, default 1000 |
Value
dataframe with time series or two factor DoE mean, standard deviation and RSD for those technique replicates & biological replicates combined with raw data in different groups if file are defaults NULL.
Get the csv files from xcmsset/XCMSnExp/list object
Description
Get the csv files from xcmsset/XCMSnExp/list object
Usage
getupload(
xset,
method = "medret",
value = "into",
name = "Peaklist",
type = "m",
mzdigit = 4,
rtdigit = 1
)
Arguments
xset |
the xcmsset/XCMSnExp/list object which you want to submitted to Metaboanalyst |
method |
parameter for groupval function |
value |
parameter for groupval function |
name |
file name |
type |
m means Metaboanalyst, a means xMSannotator, o means full information csv |
mzdigit |
m/z digits of row names of data frame |
rtdigit |
retention time digits of row names of data frame |
Value
dataframe with data needed for Metaboanalyst/xMSannotator/pmd if your want to perform local analysis.
See Also
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
xset <- getdata(cdfpath, pmethod = ' ')
getupload(xset)
## End(Not run)
Get the csv files to be submitted to Metaboanalyst
Description
Get the csv files to be submitted to Metaboanalyst
Usage
getupload2(xset, value = "into", name = "Peaklist")
Arguments
xset |
a XCMSnExp object with processed data which you want to submitted to Metaboanalyst |
value |
value for 'xcms::featureValues' |
name |
file name |
Value
dataframe with data needed for Metaboanalyst if your want to perform local analysis.
See Also
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
xset <- getdata2(cdfpath)
getupload2(xset)
## End(Not run)
Get the csv files to be submitted to Metaboanalyst
Description
Get the csv files to be submitted to Metaboanalyst
Usage
getupload3(list, name = "Peaklist")
Arguments
list |
list with data as peaks list, mz, rt and group information |
name |
file name |
Value
dataframe with data needed for Metaboanalyst if your want to perform local analysis.
See Also
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
xset <- getdata2(cdfpath,
ppp = xcms::MatchedFilterParam(),
rtp = xcms::ObiwarpParam(),
gpp = xcms::PeakDensityParam())
xset <- enviGCMS::getmzrt2(xset)
getupload3(xset)
## End(Not run)
plot scatter plot for rt-mz profile and output gif file for multiple groups
Description
plot scatter plot for rt-mz profile and output gif file for multiple groups
Usage
gifmr(
list,
ms = c(100, 500),
rsdcf = 30,
inscf = 5,
imputation = "i",
name = "test",
...
)
Arguments
list |
list with data as peaks list, mz, rt and group information |
ms |
the mass range to plot the data |
rsdcf |
the rsd cutoff of all peaks in all group |
inscf |
Log intensity cutoff for peaks across samples. If any peaks show a intensity higher than the cutoff in any samples, this peaks would not be filtered. default 5 |
imputation |
parameters for 'getimputation' function method |
name |
file name for gif file, default test |
... |
parameters for 'plot' function |
Value
gif file
Examples
## Not run:
data(list)
gifmr(list)
## End(Not run)
Demo data
Description
Demo data
Usage
data(list)
Format
A list object with data, mass to charge ratio, retention time and group information. The list is generated from faahKO package.
filter data by average moving box
Description
filter data by average moving box
Usage
ma(x, n)
Arguments
x |
a vector |
n |
A number to identify the size of the moving box. |
Value
The filtered data
Examples
ma(rnorm(1000),5)
Demo raw data matrix
Description
Demo raw data matrix
Usage
data(matrix)
Format
A matrix object from raw mass spectrometry data. The list is generated from faahKO package.
Show MS/MS pmd annotation result
Description
Show MS/MS pmd annotation result
Usage
plotanno(anno, ...)
Arguments
anno |
list from MSMS anno function |
... |
other parameter for plot function |
plot the calibration curve with error bar, r squared and equation.
Description
plot the calibration curve with error bar, r squared and equation.
Usage
plotcc(x, y, upper, lower = upper, ...)
Arguments
x |
concentration |
y |
response |
upper |
upper error bar |
lower |
lower error bar |
... |
parameters for 'plot' function |
Examples
## Not run:
plotcc(x,y,upper)
## End(Not run)
plot the density for multiple samples
Description
plot the density for multiple samples
Usage
plotden(data, lv, index = NULL, name = NULL, lwd = 1, ...)
Arguments
data |
data row as peaks and column as samples |
lv |
group information |
index |
index for selected peaks |
name |
name on the figure for samples |
lwd |
the line width for density plot, default 1 |
... |
parameters for 'plot' function |
Examples
data(list)
plotden(list$data, lv = as.character(list$group$sample_group),ylim = c(0,1))
plot density weighted intensity for multiple samples
Description
plot density weighted intensity for multiple samples
Usage
plotdwtus(list, n = 512, ...)
Arguments
list |
list with data as peaks list, mz, rt and group information |
n |
the number of equally spaced points at which the density is to be estimated, default 512 |
... |
parameters for 'plot' function |
Value
Density weighted intensity for multiple samples
Examples
data(list)
plotdwtus(list)
plot EIC and boxplot for all peaks and return diffreport
Description
plot EIC and boxplot for all peaks and return diffreport
Usage
plote(xset, name = "test", test = "t", nonpara = "n", ...)
Arguments
xset |
xcmsset object |
name |
filebase of the sub dir |
test |
't' means two-sample welch t-test, 't.equalvar' means two-sample welch t-test with equal variance, 'wilcoxon' means rank sum wilcoxon test, 'f' means F-test, 'pairt' means paired t test, 'blockf' means Two-way analysis of variance, default 't' |
nonpara |
'y' means using nonparametric ranked data, 'n' means original data |
... |
other parameters for 'diffreport' |
Value
diffreport and pdf figure for EIC and boxplot
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
xset <- getdata(cdfpath, pmethod = ' ')
plote(xset)
## End(Not run)
Plot the response group of GC-MS
Description
Plot the response group of GC-MS
Usage
plotgroup(data, threshold = 2)
Arguments
data |
imported data matrix of GC-MS |
threshold |
the threshold of the response (log based 10) to separate the group |
Value
list linear regression model for the data matrix
Examples
## Not run:
data(matrix)
plotgroup(matrix)
## End(Not run)
plot the density of the GC-MS data with EM algorithm to separate the data into two log normal distribution.
Description
plot the density of the GC-MS data with EM algorithm to separate the data into two log normal distribution.
Usage
plothist(data)
Arguments
data |
imported data matrix of GC-MS |
Examples
## Not run:
# generate a matrix from raw data with row as m/z and column as retention time
plothist(matrix)
## End(Not run)
Plot the heatmap of mzrt profiles
Description
Plot the heatmap of mzrt profiles
Usage
plothm(data, lv, index = NULL)
Arguments
data |
data row as peaks and column as samples |
lv |
group information |
index |
index for selected peaks |
Examples
data(list)
plothm(list$data, lv = as.factor(list$group$sample_group))
plot the information of integration
Description
plot the information of integration
Usage
plotint(list, name = NULL)
Arguments
list |
list from getinteagtion |
name |
the title of the plot |
Examples
## Not run:
list <- getinteagtion(rawdata)
plotint(list)
## End(Not run)
plot the slope information of integration
Description
plot the slope information of integration
Usage
plotintslope(list, name = NULL)
Arguments
list |
list from getintegration |
name |
the title of the plot |
Examples
## Not run:
list <- getinteragtion(rawdata)
plotintslope(list)
## End(Not run)
plot the kendrick mass defect diagram
Description
plot the kendrick mass defect diagram
Usage
plotkms(data, cutoff = 1000)
Arguments
data |
vector with the name m/z |
cutoff |
remove the low intensity |
See Also
Examples
## Not run:
mz <- c(10000,5000,20000,100,40000)
names(mz) <- c(100.1022,245.2122,267.3144,400.1222,707.2294)
plotkms(mz)
## End(Not run)
plot the scatter plot for peaks list with threshold
Description
plot the scatter plot for peaks list with threshold
Usage
plotmr(
list,
rt = NULL,
ms = NULL,
inscf = 5,
rsdcf = 30,
imputation = "l",
...
)
Arguments
list |
list with data as peaks list, mz, rt and group information |
rt |
vector range of the retention time |
ms |
vector vector range of the m/z |
inscf |
Log intensity cutoff for peaks across samples. If any peaks show a intensity higher than the cutoff in any samples, this peaks would not be filtered. default 5 |
rsdcf |
the rsd cutoff of all peaks in all group, default 30 |
imputation |
parameters for 'getimputation' function method |
... |
parameters for 'plot' function |
Value
data fit the cutoff
Examples
data(list)
plotmr(list)
plot the diff scatter plot for peaks list with threshold between two groups
Description
plot the diff scatter plot for peaks list with threshold between two groups
Usage
plotmrc(list, ms = c(100, 800), inscf = 5, rsdcf = 30, imputation = "l", ...)
Arguments
list |
list with data as peaks list, mz, rt and group information |
ms |
the mass range to plot the data |
inscf |
Log intensity cutoff for peaks across samples. If any peaks show a intensity higher than the cutoff in any samples, this peaks would not be filtered. default 5 |
rsdcf |
the rsd cutoff of all peaks in all group |
imputation |
parameters for 'getimputation' function method |
... |
parameters for 'plot' function |
Examples
data(list)
plotmrc(list)
plot GC/LC-MS data as a heatmap with TIC
Description
plot GC/LC-MS data as a heatmap with TIC
Usage
plotms(data, log = FALSE)
Arguments
data |
imported data matrix of GC-MS |
log |
transform the intensity into log based 10 |
Value
heatmap
Examples
## Not run:
png('test.png')
plotms(matrix)
dev.off()
## End(Not run)
Plot EIC of certain m/z and return dataframe for integration
Description
Plot EIC of certain m/z and return dataframe for integration
Usage
plotmsrt(data, ms, rt, n = FALSE)
Arguments
data |
imported data matrix of GC-MS |
ms |
m/z to be extracted |
rt |
vector range of the retention time |
n |
logical smooth or not |
Value
dataframe with with the first column RT and second column intensity of the SIM ions.
Examples
## Not run:
matrix <- getmd(rawdata)
plotmsrt(matrix,rt = c(500,1000),ms = 300)
## End(Not run)
plot GC/LC-MS data as scatter plot
Description
plot GC/LC-MS data as scatter plot
Usage
plotmz(data, inscf = 3.5, ...)
Arguments
data |
imported data matrix of GC-MS |
inscf |
Log intensity cutoff for peaks, default 3.5 |
... |
parameters for 'plot' function |
Value
scatter plot
Examples
## Not run:
data(matrix)
png('test.png')
plotmz(matrix)
dev.off()
## End(Not run)
plot the PCA for multiple samples
Description
plot the PCA for multiple samples
Usage
plotpca(
data,
lv = NULL,
index = NULL,
center = TRUE,
scale = TRUE,
xrange = NULL,
yrange = NULL,
pch = NULL,
...
)
Arguments
data |
data row as peaks and column as samples |
lv |
group information |
index |
index for selected peaks |
center |
parameters for PCA |
scale |
parameters for scale |
xrange |
x axis range for return samples, default NULL |
yrange |
y axis range for return samples, default NULL |
pch |
default pch would be the first character of group information or samples name |
... |
other parameters for 'plot' function |
Value
if xrange and yrange are not NULL, return file name of all selected samples on 2D score plot
Examples
data(list)
plotpca(list$data, lv = as.character(list$group$sample_group))
plot intensity of peaks across samples or samples across peaks
Description
plot intensity of peaks across samples or samples across peaks
Usage
plotpeak(data, lv = NULL, indexx = NULL, indexy = NULL, ...)
Arguments
data |
matrix |
lv |
factor vector for the column |
indexx |
index for matrix row |
indexy |
index for matrix column |
... |
parameters for 'title' function |
Value
parallel coordinates plot
Examples
data(list)
# selected peaks across samples
plotpeak(t(list$data), lv = as.factor(c(rep(1,5),rep(2,nrow(list$data)-5))),1:10,1:10)
# selected samples across peaks
plotpeak(list$data, lv = as.factor(list$group$sample_group),1:10,1:10)
plot ridgeline density plot
Description
plot ridgeline density plot
Usage
plotridge(data, lv = NULL, indexx = NULL, indexy = NULL, ...)
Arguments
data |
matrix |
lv |
factor vector for the column |
indexx |
index for matrix row |
indexy |
index for matrix column |
... |
parameters for 'title' function |
Value
ridgeline density plot
Examples
data(list)
plotridge(t(list$data),indexy=c(1:10),xlab = 'Intensity',ylab = 'peaks')
plotridge(log(list$data),as.factor(list$group$sample_group),xlab = 'Intensity',ylab = 'peaks')
Relative Log Abundance Ridge (RLAR) plots for samples or peaks
Description
Relative Log Abundance Ridge (RLAR) plots for samples or peaks
Usage
plotridges(data, lv, type = "g")
Arguments
data |
data row as peaks and column as samples |
lv |
factor vector for the group information of samples |
type |
'g' means group median based, other means all samples median based. |
Value
Relative Log Abundance Ridge(RLA) plots
Examples
data(list)
plotridges(list$data, as.factor(list$group$sample_group))
Relative Log Abundance (RLA) plots
Description
Relative Log Abundance (RLA) plots
Usage
plotrla(data, lv, type = "g", ...)
Arguments
data |
data row as peaks and column as samples |
lv |
factor vector for the group information |
type |
'g' means group median based, other means all samples median based. |
... |
parameters for boxplot |
Value
Relative Log Abundance (RLA) plots
Examples
data(list)
plotrla(list$data, as.factor(list$group$sample_group))
plot the rsd influences of data in different groups
Description
plot the rsd influences of data in different groups
Usage
plotrsd(list, ms = c(100, 800), inscf = 5, rsdcf = 100, imputation = "l", ...)
Arguments
list |
list with data as peaks list, mz, rt and group information |
ms |
the mass range to plot the data |
inscf |
Log intensity cutoff for peaks across samples. If any peaks show a intensity higher than the cutoff in any samples, this peaks would not be filtered. default 5 |
rsdcf |
the rsd cutoff of all peaks in all group |
imputation |
parameters for 'getimputation' function method |
... |
other parameters for 'plot' function |
Examples
data(list)
plotrsd(list)
Plot mass spectrum of certain retention time and return mass spectrum vector (MSP file) for NIST search
Description
Plot mass spectrum of certain retention time and return mass spectrum vector (MSP file) for NIST search
Usage
plotrtms(data, rt, ms, msp = FALSE)
Arguments
data |
imported data matrix of GC-MS |
rt |
vector range of the retention time |
ms |
vector range of the m/z |
msp |
logical, return MSP files or not, default False |
Value
plot, vector and MSP files for NIST search
Examples
## Not run:
plotrtms(matrix,rt = c(500,1000),ms = c(300,500))
## End(Not run)
plot 1-d density for multiple samples
Description
plot 1-d density for multiple samples
Usage
plotrug(data, lv = NULL, indexx = NULL, indexy = NULL, ...)
Arguments
data |
matrix |
lv |
factor vector for the column |
indexx |
index for matrix row |
indexy |
index for matrix column |
... |
parameters for 'title' function |
Examples
data(list)
plotrug(list$data)
plotrug(log(list$data), lv = as.factor(list$group$sample_group))
Plot the intensity distribution of GC-MS
Description
Plot the intensity distribution of GC-MS
Usage
plotsms(meanmatrix, rsdmatrix)
Arguments
meanmatrix |
mean data matrix of GC-MS(n=5) |
rsdmatrix |
standard deviation matrix of GC-MS(n=5) |
Examples
## Not run:
plotsms(meanmatrix,rsdmatrix)
## End(Not run)
Plot the background of data
Description
Plot the background of data
Usage
plotsub(data)
Arguments
data |
imported data matrix of GC-MS |
Examples
## Not run:
plotsub(matrix)
## End(Not run)
plot GC-MS data as a heatmap for constant speed of temperature rising
Description
plot GC-MS data as a heatmap for constant speed of temperature rising
Usage
plott(data, log = FALSE, temp = c(100, 320))
Arguments
data |
imported data matrix of GC-MS |
log |
transform the intensity into log based 10 |
temp |
temperature range for constant speed |
Value
heatmap
Examples
## Not run:
plott(matrix)
## End(Not run)
Plot Total Ion Chromatogram (TIC)
Description
Plot Total Ion Chromatogram (TIC)
Usage
plottic(data, n = FALSE)
Arguments
data |
imported data matrix of GC-MS |
n |
logical smooth or not |
Value
plot
Examples
## Not run:
plottic(matrix)
## End(Not run)
Get the MIR from the file
Description
Get the MIR from the file
Usage
qbatch(file, mz1, mz2, rt = c(8.65, 8.74), brt = c(8.74, 8.85))
Arguments
file |
data file, CDF or other format supportted by xcmsRaw |
mz1 |
the lowest mass |
mz2 |
the highest mass |
rt |
a rough RT range contained only one peak to get the area |
brt |
a rough RT range contained only one peak and enough noises to get the area |
Value
arearatio
Examples
## Not run:
arearatio <- qbatch(datafile)
## End(Not run)
Shiny application for interactive mass defect plots analysis
Description
Shiny application for interactive mass defect plots analysis
Usage
runMDPlot()
Shiny application for Short-Chain Chlorinated Paraffins analysis
Description
Shiny application for Short-Chain Chlorinated Paraffins analysis
Usage
runsccp()
Short-Chain Chlorinated Paraffins(SCCPs) peaks information for quantitative analysis
Description
A dataset containing the ions, formula, Cl
Usage
data(sccp)
Format
A data frame with 24 rows and 8 variables:
- Cln
Chlorine atom numbers
- Cn
Carbon atom numbers
- formula
molecular formula
- Hn
hydrogen atom numbers
- ions
[M-Cl]- ions
- mz
m/z for the isotopologues with highest intensity
- intensity
abundance of the isotopologues with highest intensity
- Clp
Chlorine contents
Get the differences of two GC/LC-MS data
Description
Get the differences of two GC/LC-MS data
Usage
submd(data1, data2, mzstep = 0.1, rtstep = 0.01)
Arguments
data1 |
data file path of first data |
data2 |
data file path of second data |
mzstep |
the m/z step for generating matrix data from raw mass spectral data |
rtstep |
the alignment accuracy of retention time, e.g. 0.01 means the retention times of combined data should be the same at the accuracy 0.01s. Higher rtstep would return less scans for combined data |
Value
list four matrix with the row as scantime in second and column as m/z, the first matrix refer to data 1, the second matrix refer to data 2, the third matrix refer to data1 - data2 while the fourth refer to data2 - data1, minus values are imputed by 0
Examples
## Not run:
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
matrix <- submd(cdffiles[1],cdffiles[7])
## End(Not run)
Plot the influences of DoE and Batch effects on each peaks
Description
Plot the influences of DoE and Batch effects on each peaks
Usage
svabatch(df, dfsv, dfanova)
Arguments
df |
data output from 'svacor' function |
dfsv |
data output from 'svaplot' function for corrected data |
dfanova |
data output from 'svaplot' function for raw data |
Value
influences plot
See Also
Examples
## Not run:
library(faahKO)
cdfpath <- system.file("cdf", package = "faahKO")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
xset <- xcmsSet(cdffiles)
xset <- group(xset)
xset2 <- retcor(xset, family = "symmetric", plottype = "mdevden")
xset2 <- group(xset2, bw = 10)
xset3 <- fillPeaks(xset2)
df <- svacor(xset3)
dfsv <- svaplot(xset3)
dfanova <- svaplot(xset3, pqvalues = "anova")
svabatch(df,dfsv,dfanova)
## End(Not run)
Surrogate variable analysis(SVA) to correct the unknown batch effects
Description
Surrogate variable analysis(SVA) to correct the unknown batch effects
Usage
svacor(xset, lv = NULL, method = "medret", intensity = "into")
Arguments
xset |
xcmsset object |
lv |
group information |
method |
parameter for groupval function |
intensity |
parameter for groupval function |
Details
this is used for reviesed version of SVA to correct the unknown batch effects
Value
list object with various components such raw data, corrected data, signal part, random errors part, batch part, p-values, q-values, mass, rt, Posterior Probabilities of Surrogate variables and Posterior Probabilities of Mod. If no surrogate variable found, corresponding part would miss.
See Also
Examples
## Not run:
library(faahKO)
cdfpath <- system.file("cdf", package = "faahKO")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
xset <- xcmsSet(cdffiles)
xset <- group(xset)
xset2 <- retcor(xset, family = "symmetric", plottype = "mdevden")
xset2 <- group(xset2, bw = 10)
xset3 <- fillPeaks(xset2)
df <- svacor(xset3)
## End(Not run)
Filter the data with p value and q value
Description
Filter the data with p value and q value
Usage
svadata(list, pqvalues = "sv", pt = 0.05, qt = 0.05)
Arguments
list |
results from svacor function |
pqvalues |
method for ANOVA or SVA |
pt |
threshold for p value, default is 0.05 |
qt |
threshold for q value, default is 0.05 |
Value
data, corrected data, mz and retention for filerted data
Examples
## Not run:
library(faahKO)
cdfpath <- system.file("cdf", package = "faahKO")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
xset <- xcmsSet(cdffiles)
xset <- group(xset)
xset2 <- retcor(xset, family = "symmetric", plottype = "mdevden")
xset2 <- group(xset2, bw = 10)
xset3 <- fillPeaks(xset2)
df <- svacor(xset3)
svadata(df)
## End(Not run)
Principal component analysis(PCA) for SVA corrected data and raw data
Description
Principal component analysis(PCA) for SVA corrected data and raw data
Usage
svapca(list, center = TRUE, scale = TRUE, lv = NULL)
Arguments
list |
results from svacor function |
center |
parameters for PCA |
scale |
parameters for scale |
lv |
group information |
Value
plot
See Also
Examples
## Not run:
library(faahKO)
cdfpath <- system.file("cdf", package = "faahKO")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
xset <- xcmsSet(cdffiles)
xset <- group(xset)
xset2 <- retcor(xset, family = "symmetric", plottype = "mdevden")
xset2 <- group(xset2, bw = 10)
xset3 <- fillPeaks(xset2)
df <- svacor(xset3)
svapca(df)
## End(Not run)
Filter the data with p value and q value and show them
Description
Filter the data with p value and q value and show them
Usage
svaplot(list, pqvalues = "sv", pt = 0.05, qt = 0.05, lv = NULL, index = NULL)
Arguments
list |
results from svacor function |
pqvalues |
method for ANOVA or SVA |
pt |
threshold for p value, default is 0.05 |
qt |
threshold for q value, default is 0.05 |
lv |
group information |
index |
index for selected peaks |
Value
heatmap for the data
See Also
Examples
## Not run:
library(faahKO)
cdfpath <- system.file("cdf", package = "faahKO")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
xset <- xcmsSet(cdffiles)
xset <- group(xset)
xset2 <- retcor(xset, family = "symmetric", plottype = "mdevden")
xset2 <- group(xset2, bw = 10)
xset3 <- fillPeaks(xset2)
df <- svacor(xset3)
svaplot(df)
## End(Not run)
Get the corrected data after SVA for metabolanalyst
Description
Get the corrected data after SVA for metabolanalyst
Usage
svaupload(xset, lv = NULL)
Arguments
xset |
xcmsset object |
lv |
group information |
Value
csv files for both raw and corrected data for metaboanalyst if SVA could be applied
Examples
## Not run:
library(faahKO)
cdfpath <- system.file("cdf", package = "faahKO")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
xset <- xcmsSet(cdffiles)
xset <- group(xset)
xset2 <- retcor(xset, family = "symmetric", plottype = "mdevden")
xset2 <- group(xset2, bw = 10)
xset3 <- fillPeaks(xset2)
svaupload(xset3)
## End(Not run)
Write MSP file for NIST search
Description
Write MSP file for NIST search
Usage
writeMSP(list, name = "unknown", sep = FALSE)
Arguments
list |
a list with spectra information |
name |
name of the compounds |
sep |
numeric or logical the numbers of spectra in each file and FALSE to include all of the spectra in one msp file |
Value
none a MSP file will be created.
Examples
## Not run:
ins <- c(10000,20000,10000,30000,5000)
mz <- c(101,143,189,221,234)
writeMSP(list(list(spectra = cbind.data.frame(mz,ins))), name = 'test')
## End(Not run)
Perform MS/MS X rank annotation for mgf file
Description
Perform MS/MS X rank annotation for mgf file
Usage
xrankanno(file, db = NULL, ppm = 10, prems = 1.1, intc = 0.1, quantile = 0.75)
Arguments
file |
mgf file generated from MS/MS data |
db |
database could be list object from 'getms2pmd' |
ppm |
mass accuracy, default 10 |
prems |
precursor mass range, default 1.1 to include M+H or M-H |
intc |
intensity cutoff for peaks. Default 0.1 |
quantile |
X rank quantiles cutoff for annotation. Default 0.75. |
Value
list with MSMS annotation results