Common mass spectrometry and statistical tools.
install.packages('MSbox')
examples:
E_iso('C') # element symbol, case insensitive
E_iso('Carbon') # element full name, case insensitive
E_iso('carBon') # element full name, case insensitive
If the queried information is not found for a compounds, it will assign "unknown" to that compound information:
describe('malic acid') # get formula by default
describe(c('malic acid', 'citric acid', 'tartaric acid'), representation = "smiles") # get smiles
It accepts two types of inputs:
(1) Standards elemental composition, for instance, C7H6O
, C7H6Na
. Here each element is distinguished by Capital letters, i.e., sodium should be written as Na, not NA, na or nA here. Since there is only one sodium in the formula C7H7Na
, you don't have to write 1 after Na
.
(2) User friendly elemental composition, for instance,c7H6O1
, C7H6NA1
. Here each element is distinguished by the number of the element, therefore, i.e, sodium can be written as Na
, NA
or na
. But the number the sodium element should be clearlly stated in the formula even if there is only one sodium.
example for Standards elemental composition:
mass("C7H6O", caseSensitive = T)
mass(c("C7H6O", "C7H6Na"), caseSensitive = T) # vector input
example for User friendly elemental composition:
mass("c7h6O1")
mass(c("c7h6o1", "C7H6NA1")) # vector input
It accepts two types of inputs:
(1) Standards elemental composition, for instance, C7H6O
, C7H6Na
. Here each element is distinguished by Capital letters, i.e., sodium should be written as Na, not NA, na or nA here. Since there is only one sodium in the formula C7H7Na
, you don't have to write 1 after Na
.
(2) User friendly elemental composition, for instance,c7H6O1
, C7H6NA1
. Here each element is distinguished by the number of the element, therefore, i.e, sodium can be written as Na
, NA
or na
. But the number the sodium element should be clearlly stated in the formula even if there is only one sodium.
example for Standards elemental composition
mz("C7H7O", 1, caseSensitive = T) # [M+H]+, positive ion mode, charge z = 1
mz("C7H5O", -1, caseSensitive = T) # [M-H]-, negative ion mode, charge z = 1
mz(c("C7H6O", "C7H6Na"), 1, caseSensitive = T) # vector input
example for User friendly elemental composition:
mz("c7h7O1") # [M+H]+, positive ion mode, charge z = 1
mz("c7H5o", -1) # [M-H]-, negative ion mode, charge z = 1
mz(c("c7h6o1", "C7H6NA1"), 1) # vector input
example:
ppm(155.03383, 155.03388) # standard way
ppm(155.03383, .03388) # lazy input when the integer parts of m and t are the same
ppm(155.03383, .03388, lazy = F) # lazy input disabled
ppm(155.03384, mz('C7H7O4', z = 1)) # with ion formula
example
Iso_mass(F = 'C7H6O4', iso = '[13]C2[2]H3') # Two 13C and three 2H are labled. Case insensitive.
example
Iso_mz(F = 'C7H6O4', iso = '[13]C2[2]H3', z = 1) # Two 13C and three 2H are labled. Case insensitive.
examples
contam(33.0335, ppm = 10, mode = '+')
contam(44.998, ppm = 10, mode = '-')
examples
adduct('C1H4', mode = '-') # case insensitive
adduct('C1H4', mode = '+') # case insensitive
what(1034.556, mode = "+", ppm = 3) # single m/z value in HMDB database (default)
what(1034.556, mode = "+", ppm = 3, useDB = "KEGG") # single m/z value in KEGG database
what(c(133.014, 191.020), ppm = 10, mode = '-') # batch search
searchDB(DF, DB, ppm = 5, RT = 0.2, useRT = T) # with RT searching
searchDB(DF, DB, ppm = 5, RT = 0.2, useRT = F) # without RT searching, default
Find the samples names which contain the max ion intensity/peak area for each mass feature
Calculate coefficient of variation (CV) for each mass feature among different sample group
Calculate fold change (FC) for each mass feature among different sample group
Calculate p-value for each mass feature among different sample group
Calculate calculate VIP value for each mass feature among different sample group
You can use Dostat()
function to get above statistical analysis.
# sample data
dat <- matrix(runif(2 * 300), ncol = 2, nrow = 300)
rownames(dat) <- 1:dim(dat)[1]
myGroup <- rep_len(LETTERS[1:3], 300)
# statistics
myResult <- doStat(dat, Group = myGroup)
# sample data
dat <- matrix(runif(2*300), ncol = 2, nrow = 300)
Group <- rep_len(LETTERS[1:3], 300)
# PCA
viewPCA(dat, Group = Group, interactive = T) # you can turn on/off interactive plot using interactive = T/F
# sample data
dat <- matrix(runif(100*9), ncol = 100, nrow = 27)
myGroup <- rep_len(LETTERS[1:3], 27)
myBatch <- rep(1:3, each = 9, times = 1)
mySeq <- c(1:27)
# view TIC
viewTIC(dat, Group = myGroup, Batch = myBatch, resultBy = "Batch")
Normalization methods include: (1) LBME: linear baseline normalization based on mean values; (2) LBMD: linear baseline normalization based on median values; (3) PQN: probabilistic quotient normalization; (4) QT: quantile normalization; (5) TIC: total ion current normalization.
dat <- matrix(runif(100*9), ncol = 100, nrow = 10)
out <- doNormalization(dat, method = "PQN" )
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