average.fdr: Average a two-dimensional local false discovery rate
In OCplus: Operating characteristics plus sample size and local fdr for microarray experiments
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
This function averages two-dimensional local false discovery rates as computed by fdr2d for binned values of the first test statistic and across the values of the second test statistic. The result can easily be plotted and should be comparable to the one-dimensional fdr as provided by fdr1d, provided that the smoothing parameters were chosen suitably.
Usage
1
average.fdr(x, breaks)
Arguments
x
an object returned by fdr2d.
breaks
breaks defining intervals into which the first test statistic is binned; by default the same values that were used by fdr2d.
Details
Assuming that we have smoothed the estimate suitably and have chosen the proportion of non-dffierentially expressed genes suitably, we should get very much the same results from fdr2d as from fdr1d when we average across the logarithmized standard errors, see Examples.
The averaging is done across the estimated values for the actual genes; this corresponds to a weighted mean of the smoothed estimates on a grid, where the weight is proportional to cell frequencies.
Note that it is usuually easier to get a good match in the tails of the curves than in the center, which is okay, as this is where we want to estimate the fdr reliably.
Value
A matrix with two columns tstat and fdr.local.
Author(s)
A. Ploner
References
Ploner A, Calza S, Gusnanto A, Pawitan Y (2005) Multidimensional local false discovery rate for micorarray studies. Submitted Manuscript.
See Also
Examples
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Example output
fdr1d> # We simulate a small example with 5 percent regulated genes and
fdr1d> # a rather large effect size
fdr1d> set.seed(2000)
fdr1d> xdat = matrix(rnorm(50000), nrow=1000)
fdr1d> xdat[1:25, 1:25] = xdat[1:25, 1:25] - 1
fdr1d> xdat[26:50, 1:25] = xdat[26:50, 1:25] + 1
fdr1d> grp = rep(c("Sample A","Sample B"), c(25,25))
fdr1d> # A default run
fdr1d> res1d = fdr1d(xdat, grp)
Starting permutations...
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Smoothing f
Smoothing f0
fdr1d> res1d[1:20,]
tstat fdr.local
1 -3.113975 0.343518819
2 -4.868530 0.005661948
3 -4.487968 0.014988680
4 -3.891546 0.067078413
5 -2.323968 0.794599504
6 -2.491116 0.697740540
7 -2.896670 0.460201903
8 -4.254195 0.026995885
9 -3.490224 0.170529676
10 -3.465717 0.180666869
11 -2.642704 0.605687562
12 -1.532776 0.976587492
13 -3.805811 0.082774181
14 -4.138912 0.035586709
15 -4.999346 0.004067203
16 -3.724595 0.098722065
17 -4.853666 0.005843152
18 -2.607185 0.627256785
19 -3.639713 0.123482696
20 -4.881803 0.005500135
fdr1d> # Looking at the results
fdr1d> summary(res1d)
$Statistic
Min. 1st Qu. Median Mean 3rd Qu. Max.
-5.247257 -0.647769 -0.003438 0.015693 0.736234 5.125711
$fdr
fdr
statistic (0,0.05] (0.05,0.1] (0.1,0.2] (0.2,1] (1,Inf]
t<0 8 2 6 480 0
t>=0 11 3 3 487 0
$p0
$p0$Value
[1] 0.8879223
$p0$Estimated
[1] TRUE
fdr1d> plot(res1d)
fdr1d> res1d[res1d$fdr<0.05, ]
tstat fdr.local
2 -4.868530 0.005661948
3 -4.487968 0.014988680
8 -4.254195 0.026995885
14 -4.138912 0.035586709
15 -4.999346 0.004067203
17 -4.853666 0.005843152
20 -4.881803 0.005500135
21 -4.273400 0.025780390
22 -5.247257 0.002150341
23 -4.550108 0.012704082
24 -5.002789 0.004025231
28 4.027545 0.035205867
30 4.274270 0.018487088
32 3.913886 0.047579527
37 4.810875 0.004376113
38 5.125711 0.001711363
39 4.883544 0.003480536
41 4.404355 0.013438726
522 4.017566 0.036292167
fdr1d> # Averaging estimates the global FDR for a set of genes
fdr1d> ndx = abs(res1d$tstat) > 3
fdr1d> mean(res1d$fdr[ndx])
[1] 0.08513626
fdr1d> # Extra information
fdr1d> class(res1d)
[1] "fdr1d.result" "fdr.result" "data.frame"
fdr1d> attr(res1d,"param")
$p0
[1] 0.8879223
$p0.est
[1] TRUE
$fdr
[1] 0.002150341 0.003679441 0.006313912 0.010908497 0.018853690 0.032531021
[7] 0.056260686 0.095823210 0.158862377 0.248254978 0.357555584 0.474579801
[13] 0.598819090 0.730049134 0.852474483 0.928910674 0.961551113 0.977649650
[19] 0.972012961 0.963954436 0.944227575 0.925816181 0.879585482 0.875265249
[25] 0.881280571 0.936401140 0.976673462 1.000000000 0.995431165 0.965258323
[31] 0.930756968 0.906137071 0.900032160 0.843371879 0.769103282 0.673382987
[37] 0.559230197 0.442600785 0.334090209 0.232379026 0.150794383 0.094266063
[43] 0.056810882 0.033284476 0.018990523 0.010603980 0.005822593 0.003159320
[49] 0.001711363
$xbreaks
[1] -5.35530881 -5.13920530 -4.92310180 -4.70699829 -4.49089478 -4.27479127
[7] -4.05868776 -3.84258426 -3.62648075 -3.41037724 -3.19427373 -2.97817022
[13] -2.76206672 -2.54596321 -2.32985970 -2.11375619 -1.89765268 -1.68154918
[19] -1.46544567 -1.24934216 -1.03323865 -0.81713514 -0.60103164 -0.38492813
[25] -0.16882462 0.04727889 0.26338240 0.47948590 0.69558941 0.91169292
[31] 1.12779643 1.34389994 1.56000344 1.77610695 1.99221046 2.20831397
[37] 2.42441748 2.64052098 2.85662449 3.07272800 3.28883151 3.50493502
[43] 3.72103852 3.93714203 4.15324554 4.36934905 4.58545256 4.80155606
[49] 5.01765957 5.23376308
Starting permutations...
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OCplus documentation built on Nov. 8, 2020, 5:20 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Author(s) References See Also Examples
Description
This function averages two-dimensional local false discovery rates as computed by fdr2d for binned values of the first test statistic and across the values of the second test statistic. The result can easily be plotted and should be comparable to the one-dimensional fdr as provided by fdr1d, provided that the smoothing parameters were chosen suitably.
Usage
1 | average.fdr(x, breaks)
|
Arguments
x |
an object returned by |
breaks |
breaks defining intervals into which the first test statistic is binned; by default the same values that were used by |
Details
Assuming that we have smoothed the estimate suitably and have chosen the proportion of non-dffierentially expressed genes suitably, we should get very much the same results from fdr2d as from fdr1d when we average across the logarithmized standard errors, see Examples.
The averaging is done across the estimated values for the actual genes; this corresponds to a weighted mean of the smoothed estimates on a grid, where the weight is proportional to cell frequencies.
Note that it is usuually easier to get a good match in the tails of the curves than in the center, which is okay, as this is where we want to estimate the fdr reliably.
Value
A matrix with two columns tstat and fdr.local.
Author(s)
A. Ploner
References
Ploner A, Calza S, Gusnanto A, Pawitan Y (2005) Multidimensional local false discovery rate for micorarray studies. Submitted Manuscript.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 |
Example output
fdr1d> # We simulate a small example with 5 percent regulated genes and
fdr1d> # a rather large effect size
fdr1d> set.seed(2000)
fdr1d> xdat = matrix(rnorm(50000), nrow=1000)
fdr1d> xdat[1:25, 1:25] = xdat[1:25, 1:25] - 1
fdr1d> xdat[26:50, 1:25] = xdat[26:50, 1:25] + 1
fdr1d> grp = rep(c("Sample A","Sample B"), c(25,25))
fdr1d> # A default run
fdr1d> res1d = fdr1d(xdat, grp)
Starting permutations...
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Smoothing f
Smoothing f0
fdr1d> res1d[1:20,]
tstat fdr.local
1 -3.113975 0.343518819
2 -4.868530 0.005661948
3 -4.487968 0.014988680
4 -3.891546 0.067078413
5 -2.323968 0.794599504
6 -2.491116 0.697740540
7 -2.896670 0.460201903
8 -4.254195 0.026995885
9 -3.490224 0.170529676
10 -3.465717 0.180666869
11 -2.642704 0.605687562
12 -1.532776 0.976587492
13 -3.805811 0.082774181
14 -4.138912 0.035586709
15 -4.999346 0.004067203
16 -3.724595 0.098722065
17 -4.853666 0.005843152
18 -2.607185 0.627256785
19 -3.639713 0.123482696
20 -4.881803 0.005500135
fdr1d> # Looking at the results
fdr1d> summary(res1d)
$Statistic
Min. 1st Qu. Median Mean 3rd Qu. Max.
-5.247257 -0.647769 -0.003438 0.015693 0.736234 5.125711
$fdr
fdr
statistic (0,0.05] (0.05,0.1] (0.1,0.2] (0.2,1] (1,Inf]
t<0 8 2 6 480 0
t>=0 11 3 3 487 0
$p0
$p0$Value
[1] 0.8879223
$p0$Estimated
[1] TRUE
fdr1d> plot(res1d)
fdr1d> res1d[res1d$fdr<0.05, ]
tstat fdr.local
2 -4.868530 0.005661948
3 -4.487968 0.014988680
8 -4.254195 0.026995885
14 -4.138912 0.035586709
15 -4.999346 0.004067203
17 -4.853666 0.005843152
20 -4.881803 0.005500135
21 -4.273400 0.025780390
22 -5.247257 0.002150341
23 -4.550108 0.012704082
24 -5.002789 0.004025231
28 4.027545 0.035205867
30 4.274270 0.018487088
32 3.913886 0.047579527
37 4.810875 0.004376113
38 5.125711 0.001711363
39 4.883544 0.003480536
41 4.404355 0.013438726
522 4.017566 0.036292167
fdr1d> # Averaging estimates the global FDR for a set of genes
fdr1d> ndx = abs(res1d$tstat) > 3
fdr1d> mean(res1d$fdr[ndx])
[1] 0.08513626
fdr1d> # Extra information
fdr1d> class(res1d)
[1] "fdr1d.result" "fdr.result" "data.frame"
fdr1d> attr(res1d,"param")
$p0
[1] 0.8879223
$p0.est
[1] TRUE
$fdr
[1] 0.002150341 0.003679441 0.006313912 0.010908497 0.018853690 0.032531021
[7] 0.056260686 0.095823210 0.158862377 0.248254978 0.357555584 0.474579801
[13] 0.598819090 0.730049134 0.852474483 0.928910674 0.961551113 0.977649650
[19] 0.972012961 0.963954436 0.944227575 0.925816181 0.879585482 0.875265249
[25] 0.881280571 0.936401140 0.976673462 1.000000000 0.995431165 0.965258323
[31] 0.930756968 0.906137071 0.900032160 0.843371879 0.769103282 0.673382987
[37] 0.559230197 0.442600785 0.334090209 0.232379026 0.150794383 0.094266063
[43] 0.056810882 0.033284476 0.018990523 0.010603980 0.005822593 0.003159320
[49] 0.001711363
$xbreaks
[1] -5.35530881 -5.13920530 -4.92310180 -4.70699829 -4.49089478 -4.27479127
[7] -4.05868776 -3.84258426 -3.62648075 -3.41037724 -3.19427373 -2.97817022
[13] -2.76206672 -2.54596321 -2.32985970 -2.11375619 -1.89765268 -1.68154918
[19] -1.46544567 -1.24934216 -1.03323865 -0.81713514 -0.60103164 -0.38492813
[25] -0.16882462 0.04727889 0.26338240 0.47948590 0.69558941 0.91169292
[31] 1.12779643 1.34389994 1.56000344 1.77610695 1.99221046 2.20831397
[37] 2.42441748 2.64052098 2.85662449 3.07272800 3.28883151 3.50493502
[43] 3.72103852 3.93714203 4.15324554 4.36934905 4.58545256 4.80155606
[49] 5.01765957 5.23376308
Starting permutations...
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