| proteins {msProcess} | R Documentation |
Class slots:
proteins(masses, counts)
masses |
A positive numeric vector of protein masses in daltons, whose elements should be unique. |
counts |
A positive integer vector of protein counts/abundance,
which should have the same length as masses. |
Usage: FUN(x, y) or x op y
Arith group generic functions,
which include +, -, *,^,
%%, %/%, and /.proteins object or a numeric vector.proteins object or an integer vector.
If both x and y are objects of proteins,
only + and - apply as mixing two protein samples and
taking part of a protein sample away, respectively.
If x is a numeric and y is an objects of proteins,
the masses of y will be modified according to the operation.
If x is an objects of proteins and y is an integer,
the counts of x will be modified according to the operation.
Usage: FUN(x, y)
Compare group generic functions,
which include ==, >, <, !=, <=,
>=, and compare.proteins object or a numeric vector.proteins object or a numeric vector.
If both x and y are objects of proteins,
only == and != apply .
If x is a numeric and y is an objects of proteins,
the masses of y will be compared according to the operation.
If x is an objects of proteins and y is a numeric,
the counts of x will be compared according to the operation.
proteins.Usage: FUN(x)
Summary group generic functions,
which include max, min, range, prod, sum,
any, and all.proteins object.proteins object.
Usage: x[i]
proteins object.proteins object.
Usage: x[i]<-value
proteins object.proteins object or an integer vector.proteins object.
Usage: show(object) or object
proteins object.proteins,
including plot, lines, points, and etc.
This generic function is not meant to be called directly.
Usage: xyCall(x, y, FUN, ..., xexpr, yexpr)
proteins object.x, y, ....x argument to FUN unevaluated.y argument to FUN unevaluated.
Coombes, K.R., Koomen, J.M., Baggerly, K.A., Morris, J.S., Kobayashi, R., ``Understanding the characteristics of mass spectrometry data through the use of simulation," Cancer Informatics, textbf{2005(1)}:41–52, 2005.
## generate two protein samples sam1 <- proteins(masses=c(1, 95, 190), counts=as.integer(c(500, 3000, 10000))) sam2 <- proteins(masses=10000+200*(0:3), counts=as.integer(c(12000, 4000, 2000, 1000))) ## print the synopsis of the protein samples sam1 sam2 ## mix the protein samples sam <- sam1 + sam2 ## visualize the protein mixture plot(sam, type="h")