@@ -1,13 +1,13 @@

 Package: crlmm

 Type: Package

 Title: Genotype Calling via CRLMM Algorithm

-Version: 1.0.52

+Version: 1.0.53

 Date: 2008-12-28

 Author: Rafael A Irizarry

 Maintainer: Benilton S Carvalho <bcarvalh@jhsph.edu>

 Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 6.0.

 License: Artistic-2.0

-Imports: affyio, preprocessCore, utils, stats, genefilter, splines, Biobase

+Imports: affyio, preprocessCore, utils, stats, genefilter, splines, Biobase, mvtnorm

 Suggests: hapmapsnp5, genomewidesnp5Crlmm, genomewidesnp6Crlmm, methods, GGdata, snpMatrix

 Collate: AllClasses.R

          crlmm-methods.R

@@ -8,5 +8,5 @@ importFrom(preprocessCore, normalize.quantiles.use.target)

 importFrom(utils, data, packageDescription, setTxtProgressBar, txtProgressBar)

 importFrom(stats, coef, cov, dnorm, kmeans, lm, mad, median, quantile, sd)

 importFrom(genefilter, rowSds)

-##importFrom(mvtnorm, dmvnorm)

+importFrom(mvtnorm, dmvnorm)

@@ -417,10 +417,12 @@ nonpolymorphic <- function(plateIndex, NP, envir){

 oneBatch <- function(plateIndex, G, A, B, conf, CONF.THR=0.99, MIN.OBS=3, DF.PRIOR, envir, trim, upperTail, bias.adj=FALSE, priorProb, ...){

 	p <- plateIndex

 	plate <- get("plate", envir)

-	AA <- G == 1

-	AB <- G == 2

-	BB <- G == 3

-	Ns <- get("Ns", envir)

+	Ns <- get("Ns", envir)	

+	highConf <- 1-exp(-conf/1000)

+	highConf <- highConf > CONF.THR

+	AA <- G == 1 & highConf

+	AB <- G == 2 & highConf

+	BB <- G == 3 & highConf

 	Ns[, p, "AA"] <- rowSums(AA)

 	Ns[, p, "AB"] <- rowSums(AB)

 	Ns[, p, "BB"] <- rowSums(BB)

@@ -534,21 +536,18 @@ oneBatch <- function(plateIndex, G, A, B, conf, CONF.THR=0.99, MIN.OBS=3, DF.PRI

 	##---------------------------------------------------------------------------

 	## Predict sufficient statistics for unobserved genotypes (plate-specific)

 	##---------------------------------------------------------------------------

-	highConf <- 1-exp(-conf/1000)

-	highConf <- highConf > CONF.THR

-	confInd <- rowMeans(highConf) > CONF.THR

-	NN <- Ns

-	NN[, p, "AA"] <- rowSums(AA & highConf, na.rm=TRUE) ##how many AA were called with high confidence

-	NN[, p, "AB"] <- rowSums(AB & highConf, na.rm=TRUE)

-	NN[, p, "BB"] <- rowSums(BB & highConf, na.rm=TRUE)

-	index.AA <- which(NN[, p, "AA"] >= 3)

-	index.AB <- which(NN[, p, "AB"] >= 3)

-	index.BB <- which(NN[, p, "BB"] >= 3)

+##	NN <- Ns

+##	NN[, p, "AA"] <- rowSums(AA & highConf, na.rm=TRUE) ##how many AA were called with high confidence

+##	NN[, p, "AB"] <- rowSums(AB & highConf, na.rm=TRUE)

+##	NN[, p, "BB"] <- rowSums(BB & highConf, na.rm=TRUE)

+	index.AA <- which(Ns[, p, "AA"] >= 3)

+	index.AB <- which(Ns[, p, "AB"] >= 3)

+	index.BB <- which(Ns[, p, "BB"] >= 3)

 	correct.orderA <- muA.AA[, p] > muA.BB[, p]

 	correct.orderB <- muB.BB[, p] > muB.AA[, p]

 	if(length(index.AB) > 0){

-		nobs <- rowSums(NN[, p, ] >= MIN.OBS) == 3

-	} else nobs <- rowSums(NN[, p, c(1,3)] >= MIN.OBS) == 2

+		nobs <- rowSums(Ns[, p, ] >= MIN.OBS) == 3

+	} else nobs <- rowSums(Ns[, p, c(1,3)] >= MIN.OBS) == 2

 	index.complete <- which(correct.orderA & correct.orderB & nobs) ##be selective here

 	size <- min(5000, length(index.complete))

 	if(size == 5000) index.complete <- sample(index.complete, 5000)

@@ -556,9 +555,9 @@ oneBatch <- function(plateIndex, G, A, B, conf, CONF.THR=0.99, MIN.OBS=3, DF.PRI

 		warning("fewer than 200 snps pass criteria for predicting the sufficient statistics")

 		stop()

 	}

-	index.AA <- which(NN[, p, "AA"] == 0 & (NN[, p, "AB"] >= MIN.OBS & NN[, p, "BB"] >= MIN.OBS))

-	index.AB <- which(NN[, p, "AB"] == 0 & (NN[, p, "AA"] >= MIN.OBS & NN[, p, "BB"] >= MIN.OBS))

-	index.BB <- which(NN[, p, "BB"] == 0 & (NN[, p, "AB"] >= MIN.OBS & NN[, p, "AA"] >= MIN.OBS))

+	index.AA <- which(Ns[, p, "AA"] == 0 & (Ns[, p, "AB"] >= MIN.OBS & Ns[, p, "BB"] >= MIN.OBS))

+	index.AB <- which(Ns[, p, "AB"] == 0 & (Ns[, p, "AA"] >= MIN.OBS & Ns[, p, "BB"] >= MIN.OBS))

+	index.BB <- which(Ns[, p, "BB"] == 0 & (Ns[, p, "AB"] >= MIN.OBS & Ns[, p, "AA"] >= MIN.OBS))

 	if(length(index.AA) > 0){

 		##Predict mean for AA genotypes

 		X.AA <- cbind(1, muA.AB[index.complete, p], muA.BB[index.complete, p],

@@ -605,9 +604,9 @@ oneBatch <- function(plateIndex, G, A, B, conf, CONF.THR=0.99, MIN.OBS=3, DF.PRI

 #	index.AA <- which(NN[, p, "AA"] == 0 & (NN[, p, "AB"] < MIN.OBS | NN[, p, "BB"] < MIN.OBS))

 #	index.AB <- which(NN[, p, "AB"] == 0 & (NN[, p, "AA"] < MIN.OBS | NN[, p, "BB"] < MIN.OBS))

 #	index.BB <- which(NN[, p, "BB"] == 0 & (NN[, p, "AB"] >= MIN.OBS | NN[, p, "AA"] >= MIN.OBS))	

-	noAA <- NN[, p, "AA"] < MIN.OBS

-	noAB <- NN[, p, "AB"] < MIN.OBS

-	noBB <- NN[, p, "BB"] < MIN.OBS

+	noAA <- Ns[, p, "AA"] < MIN.OBS

+	noAB <- Ns[, p, "AB"] < MIN.OBS

+	noBB <- Ns[, p, "BB"] < MIN.OBS

 	##---------------------------------------------------------------------------

 	## Two genotype clusters not observed -- would sequence help? (didn't seem that helpful)

 	## 1 extract index of complete data