Defining the bivariate center to acknowledge background through correlation did not help the signal to noise ratio in the trisomy data. This code is now in .bivariateCenter.
git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/crlmm@58655 bc3139a8-67e5-0310-9ffc-ced21a209358
Rob Scharp authored on 01/10/2011 04:49:31 | ... | ... |
@@ -1,7 +1,7 @@ |
| 1 | 1 |
Package: crlmm |
| 2 | 2 |
Type: Package |
| 3 | 3 |
Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays. |
| 4 |
-Version: 1.11.32 |
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+Version: 1.11.33 |
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| 5 | 5 |
Date: 2010-12-10 |
| 6 | 6 |
Author: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.edu.au>, Ingo Ruczinski <iruczins@jhsph.edu>, Rafael A Irizarry |
| 7 | 7 |
Maintainer: Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU> |
| ... | ... |
@@ -384,6 +384,36 @@ setMethod("calculatePosteriorMean", signature(object="CNSet"),
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| 384 | 384 |
return(pm) |
| 385 | 385 |
}) |
| 386 | 386 |
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| 387 |
+ .bivariateCenter <- function(nu, phi){
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| 388 |
+ ## lexical scope for mus, CA, CB |
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| 389 |
+ if(CA <= 2 & CB <= 2 & (CA+CB) < 4){
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| 390 |
+ mus[,1, ] <- log2(nu[, 1, ] + CA * |
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| 391 |
+ phi[, 1, ]) |
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| 392 |
+ mus[,2, ] <- log2(nu[, 2, ] + CB * |
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| 393 |
+ phi[, 2, ]) |
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| 394 |
+ } else { ## CA > 2
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| 395 |
+ if(CA > 2){
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| 396 |
+ theta <- pi/4*Sigma[,2,] |
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| 397 |
+ shiftA <- CA/4*phi[, 1, ] * cos(theta) |
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| 398 |
+ shiftB <- CA/4*phi[, 1, ] * sin(theta) |
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| 399 |
+ mus[, 1, ] <- log2(nu[, 1, ] + 2 * phi[,1,]+shiftA) |
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| 400 |
+ mus[, 2, ] <- log2(nu[, 2, ] + CB *phi[,2,]+shiftB) |
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+ } |
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+ if(CB > 2){
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+ ## CB > 2 |
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+ theta <- pi/2-pi/4*Sigma[,2,] |
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| 405 |
+ shiftA <- CB/4*phi[, 2, ] * cos(theta) |
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| 406 |
+ shiftB <- CB/4*phi[, 2, ] * sin(theta) |
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| 407 |
+ mus[, 1, ] <- log2(nu[, 1, ] + CA*phi[,1,]+shiftA) |
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| 408 |
+ mus[, 2, ] <- log2(nu[, 2, ]+ 2*phi[,2,]+shiftB) |
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| 409 |
+ } |
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| 410 |
+ if(CA == 2 & CB == 2){
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+ mus[, 1, ] <- log2(nu[, 1, ] + 1/2*CA*phi[,1,]) |
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+ mus[, 2, ] <- log2(nu[, 2, ]+ 1/2*CB*phi[,2,]) |
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+ } |
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+ } |
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+ mus |
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+ } |
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| 387 | 417 |
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| 388 | 418 |
## for a given copy number, return a named list of bivariate normal prediction regions |
| 389 | 419 |
## - elements of list are named by genotype |
| ... | ... |
@@ -428,9 +458,11 @@ setMethod("predictionRegion", signature(object="CNSet", copyNumber="integer"),
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| 428 | 458 |
bnms)) |
| 429 | 459 |
bivariateCenter <- function(nu, phi){
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| 430 | 460 |
## lexical scope for mus, CA, CB |
| 431 |
- mus[,1, ] <- log2(nu[, 1, ] + CA * phi[, 1, ]) |
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| 432 |
- mus[,2, ] <- log2(nu[, 2, ] + CB * phi[, 2, ]) |
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| 433 |
- mus |
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+ mus[,1, ] <- log2(nu[, 1, ] + CA * |
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| 462 |
+ phi[, 1, ]) |
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+ mus[,2, ] <- log2(nu[, 2, ] + CB * |
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+ phi[, 2, ]) |
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+ return(mus) |
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| 434 | 466 |
} |
| 435 | 467 |
np.index <- which(!isSnp(object)) |
| 436 | 468 |
for(i in seq_along(copyNumber)){
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| ... | ... |
@@ -452,14 +484,20 @@ setMethod("predictionRegion", signature(object="CNSet", copyNumber="integer"),
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| 452 | 484 |
Sigma[, 1, ] <- getVar(object, "tau2A.AA") |
| 453 | 485 |
Sigma[np.index, 2, ] <- NA |
| 454 | 486 |
} |
| 455 |
- res[[gt]]$mu <- bivariateCenter(nu, phi) |
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| 487 |
+ res[[gt]]$mu <- bivariateCenter(nu,phi) |
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| 488 |
+ ## adjust correlation |
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+## if(CA == 0 | CB == 0){
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+## Sigma[,2,] <- 0 |
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+## } |
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| 456 | 492 |
res[[gt]]$cov <- Sigma |
| 457 | 493 |
} |
| 458 | 494 |
} |
| 495 |
+ res <- as(res, "PredictionRegion") |
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| 459 | 496 |
return(res) |
| 460 | 497 |
}) |
| 461 | 498 |
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| 462 | 499 |
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+ |
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| 463 | 501 |
setMethod("xyplotcrlmm", signature(x="formula", data="CNSet", predictRegion="list"),
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| 464 | 502 |
function(x, data, predictRegion, ...){
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| 465 | 503 |
fns <- featureNames(data) |
| ... | ... |
@@ -479,7 +517,7 @@ setMethod("xyplotcrlmm", signature(x="formula", data="CNSet", predictRegion="lis
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| 479 | 517 |
x$cov <- x$cov[, , batch.index, drop=FALSE] |
| 480 | 518 |
return(x) |
| 481 | 519 |
}, bns=bns) |
| 482 |
- ##df <- as.data.frame(data) |
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+ ##predictRegion is an argument of ABpanel |
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| 483 | 521 |
xyplot(x, df, predictRegion=predictRegion, ...) |
| 484 | 522 |
}) |
| 485 | 523 |
setMethod("xyplot", signature(x="formula", data="CNSet"),
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