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, 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")