Description Usage Arguments Details Value Creating Objects Slots Methods Author(s) References See Also Examples
Implements the "Significance Analysis of Microarrays" approach to detecting differentially expressed genes.
1 2 3 4 5 6 7 8 9 10  Sam(data, classes, nPerm=100, verbose=TRUE)
## S4 method for signature 'Sam,missing'
plot(x, y, tracks=NULL, xlab='Expected T Statistics (Empirical)',
ylab='Observed T Statistics', ...)
## S4 method for signature 'Sam'
summary(object, cutoff=1, ...)
## S4 method for signature 'Sam'
selectSignificant(object, cutoff=1, ...)
## S4 method for signature 'Sam'
countSignificant(object, cutoff=1, ...)

data 
Either a data frame or matrix with numeric values or an

classes 
If 
nPerm 
An integer; the number of permutations 
verbose 
A logical flag 
x 
A 
y 
Nothing, since it is supposed to be missing. Changes to the Rd processor require documenting the missing entry. 
tracks 
a numeric vector 
xlab 
Label for the x axis 
ylab 
Label for the y axis 
object 
A 
cutoff 
A numeric value 
... 
The usual extra arguments to generic functions 
The SAM approach to analyzing microarray data was developed by Tusher
and colleagues; their implementation is widely available. This is an
independent implementation based on the description in their original
paper, customized to use the same interface (and thus work with
ExpressionSet
objects) used
by the rest of the ClassComparison package. The fundamental idea
behind SAM is that the observed distribution of rowbyrow twosample
ttests should be compared not to the theoretical null distribution
but to a null distribution estimated by a permutation test. The
Sam
constructor performs the permutation test.
summary
returns an object of class SamSummary
.
selectSignificant
returns a vector of logical values.
countSignificant
returns an integer.
As usual, objects can be created by new
, but better methods are
available in the form of the Sam
function. The inputs to this
function are the same as those used for rowbyrow statistical tests
throughout the ClassComparison package; a detailed description can be
found in the MultiTtest
class.
t.statistics
:numeric vector containing the observed tstatistics.
observed
:numeric vector containing the sorted observed tstatistics.
expected
:numeric vector of the expected distribution of tstatistics based on a permutation test.
sim.data
:numeric matrix containing all the tstatistics from all the permutations.
call
:object of class call
specifying the function
call that was used to create this object.
Compute a summary of the object.
Plot the observed and expected
tstatistics. The tracks
argument causes parallel lines to be
drawn on either side of the quantilequantile central line, at the
specified offsets. Colors in the plot are controlled by the current
values of oompaColor$CENTRAL.LINE
and
oompaColor$CONFIDENCE.CURVE
Compute a vector that selects significant values
Count the number of significant values
Kevin R. Coombes krc@silicovore.com
Tusher VG, Tibshirani R, Chu G.
Significance analysis of microarrays applied to the ionizing radiation
response.
Proc Natl Acad Sci U S A (2001) 98, 51165121.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  showClass("Sam")
ng < 10000
ns < 50
nd < 100
dat < matrix(rnorm(ng*ns), ncol=ns)
dat[1:nd, 1:(ns/2)] < dat[1:nd, 1:(ns/2)] + 2
dat[(nd+1):(2*nd), 1:(ns/2)] < dat[(nd+1):(2*nd), 1:(ns/2)]  2
cla < factor(rep(c('A', 'B'), each=25))
res < Sam(dat, cla)
plot(res)
plot(res, tracks=1:3)
summary(res)
summary(res, cutoff=2)
a < summary(res)
plot(a@significant.calls)
plot(a@significant.calls[1:300])
countSignificant(res, 1)

Loading required package: oompaBase
Class "Sam" [package "ClassComparison"]
Slots:
Name: expected observed t.statistics sim.data call
Class: numeric numeric numeric matrix call
1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . 13 . 14 . 15 . 16 . 17 . 18 . 19 . 20 . 21 . 22 . 23 . 24 . 25 . 26 . 27 . 28 . 29 . 30 . 31 . 32 . 33 . 34 . 35 . 36 . 37 . 38 . 39 . 40 . 41 . 42 . 43 . 44 . 45 . 46 . 47 . 48 . 49 . 50 . 51 . 52 . 53 . 54 . 55 . 56 . 57 . 58 . 59 . 60 . 61 . 62 . 63 . 64 . 65 . 66 . 67 . 68 . 69 . 70 . 71 . 72 . 73 . 74 . 75 . 76 . 77 . 78 . 79 . 80 . 81 . 82 . 83 . 84 . 85 . 86 . 87 . 88 . 89 . 90 . 91 . 92 . 93 . 94 . 95 . 96 . 97 . 98 . 99 . 100 .
Using a cutoff of 1 , we called 209 genes significant with expected FDR = 0.0574 ( 12 )
Using a cutoff of 2 , we called 197 genes significant with expected FDR = 0 ( 0 )
[1] 209
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