Update the maintainer information

.Rbuildignore

@@ -1,3 +1,4 @@
 ^\.github$
 ^metadata\.yaml$
 ^LICENSE\.md$
+^\.cicd-env$

DESCRIPTION

@@ -1,10 +1,10 @@
 Package: NanoStringNormCNV
 Type: Package
 Title: Helper Functions to Normalize NanoString CNV Data
-Version: 1.1.2
-Date: 2017-11-07
+Version: 1.1.3
+Date: 2026-01-22
 Author: Cindy Yao, Emilie Lalonde, Dorota Sendorek
-Maintainer: Paul Boutros <PBoutros@mednet.ucla.edu>
+Maintainer: Paul Boutros <pboutros@sbpdiscovery.org>
 Depends: R (>= 3.0.2),lattice (>= 0.20-27), BoutrosLab.plotting.general (>= 3.6.20), mclust (>= 4.4), gdata (>= 2.8.2)
 Imports: futile.logger (>= 1.3.7), testthat, reshape2 (>= 1.4), vsn (>= 3.22.0), NanoStringNorm (>= 1.1.18), vegan
 Suggests: googleVis (>= 0.2.14)

NEWS

@@ -1,3 +1,9 @@
+NanoStringNormCNV 1.1.3 2026-01-22
+
+CHANGE
+* Updated maintainer email
+
+--------------------------------------------------------------------------
 NanoStringNormCNV 1.1.0 2017-05-20
 
 CHANGE
@@ -54,4 +60,5 @@ BUG
 --------------------------------------------------------------------------
 NanoStringNormCNV 1.0.0 2017-03-28
 
-Initial release
+NEW
+* Initial release

R/apply.kd.cna.thresh.R

@@ -1,6 +1,6 @@
 apply.kd.cna.thresh <- function(ratio.data, kd.values, neutral.cn = 2) {
 	if (2 != length(kd.values) & 4 != length(kd.values)) {
 		stop("Must provide either two or four KD values! Please see documentation for details.");
 		}
 
 	# assign header names
@@ -15,7 +15,7 @@
 	na.counts <- apply(
 		X = ratio.data[,which.n, drop = FALSE],
 		MARGIN = 2,
 		FUN = function(f) { all(is.na(f)) }
 		);
 
 	if (any(na.counts)) {
@@ -24,28 +24,28 @@
 		which.cna <- which.cna[which.n];
 		}
 	cna.output <- ratio.data[, which.cna, drop = FALSE];
 
 	# determine the thresholds based on all patients combined
 	# shown to be more stable if only considering small subset of patients
 	if (2 == length(kd.values)) {
 
-		cna.thresh.single <- NanoStringNormCNV:::get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[1]); # het
-		cna.thresh.multi  <- NanoStringNormCNV:::get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[2]); # hom
+		cna.thresh.single <- get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[1]); # het
+		cna.thresh.multi  <- get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[2]); # hom
 		thresh <- c(cna.thresh.multi[1], cna.thresh.single, cna.thresh.multi[2]);
 
 	} else if (4 == length(kd.values)) {
 
 		thresh <- vector(length = 4);
-		thresh[1] <- NanoStringNormCNV:::get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[1])[[1]]; # hom del
-		thresh[2] <- NanoStringNormCNV:::get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[2])[[1]]; # het del
-		thresh[4] <- NanoStringNormCNV:::get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[4])[[2]]; # hom gain
-		thresh[3] <- NanoStringNormCNV:::get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[3])[[2]]; # het gain
+		thresh[1] <- get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[1])[[1]]; # hom del
+		thresh[2] <- get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[2])[[1]]; # het del
+		thresh[4] <- get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[4])[[2]]; # hom gain
+		thresh[3] <- get.cna.thresholds(ratios = unlist(cna.output), percent = kd.values[3])[[2]]; # het gain
 
 		}
 
 	# loop over each sample to call CNA states
 	for (col.ind in 1:ncol(cna.output)) {
-		cna.output[, col.ind] <- NanoStringNormCNV:::tumour.normal.ratio.to.cn.state(
+		cna.output[, col.ind] <- tumour.normal.ratio.to.cn.state(
 			ratios = cna.output[, col.ind],
 			thresholds = thresh
 			);

R/call.cnas.with.matched.normals.R

@@ -20,7 +20,7 @@ call.cnas.with.matched.normals <- function(
 			}
 
 		# identify and process XY probes separately
-		xy.processed.data <- NanoStringNormCNV:::process.xy.probes(
+		xy.processed.data <- process.xy.probes(
 			normalized.data = normalized.data,
 			sex.info = phenodata[, c("SampleID", "Sex")]
 			);
@@ -76,7 +76,7 @@ call.cnas.with.matched.normals <- function(
 			c("SampleID", "Cartridge")
 			];
 
-		raw.ratios <- NanoStringNormCNV:::call.copy.number.values(
+		raw.ratios <- call.copy.number.values(
 			normalized.data = input.data,
 			reference = ref,
 			thresh.method = 'none',
@@ -89,7 +89,7 @@ call.cnas.with.matched.normals <- function(
 
 		# calculate tumour:normal ratios (for male sex chrom probes, if any)
 		if (!is.null(sex.probes) && tmr %in% has.ref.XY && length(use.genes.XY) > 0) {
-			raw.ratios.xy <- NanoStringNormCNV:::call.copy.number.values(
+			raw.ratios.xy <- call.copy.number.values(
 				normalized.data = input.data.XY,
 				reference = ref,
 				thresh.method = 'none',
@@ -106,7 +106,7 @@ call.cnas.with.matched.normals <- function(
 			thresh <- c(0.4, 1.5, 2.5, 3.5);
 
 			# call CNAs in tumours (for autosome and female sex chrom probes)
-			round.ratios <- NanoStringNormCNV:::call.copy.number.values(
+			round.ratios <- call.copy.number.values(
 				normalized.data = input.data,
 				reference = ref,
 				per.chip = per.chip,
@@ -120,7 +120,7 @@ call.cnas.with.matched.normals <- function(
 
 			# call CNAs in tumours (for male sex chrom probes)
 			if (!is.null(sex.probes) && tmr %in% has.ref.XY && length(use.genes.XY) > 0) {
-				round.ratios.xy <- NanoStringNormCNV:::call.copy.number.values(
+				round.ratios.xy <- call.copy.number.values(
 					normalized.data = input.data.XY,
 					reference = ref,
 					per.chip = per.chip,
@@ -143,7 +143,7 @@ call.cnas.with.matched.normals <- function(
 				}
 
 			# call CNAs in tumours (for autosome and female sex chrom probes)
-			round.ratios <- NanoStringNormCNV:::call.copy.number.values(
+			round.ratios <- call.copy.number.values(
 				normalized.data = input.data,
 				reference = ref,
 				per.chip = per.chip,
@@ -158,7 +158,7 @@ call.cnas.with.matched.normals <- function(
 
 			# call CNAs in tumours (for male sex chrom probes)
 			if (!is.null(sex.probes) && tmr %in% has.ref.XY && length(use.genes.XY) > 0) {
-				round.ratios.xy <- NanoStringNormCNV:::call.copy.number.values(
+				round.ratios.xy <- call.copy.number.values(
 					normalized.data = input.data.XY,
 					reference = ref,
 					per.chip = per.chip,

R/call.cnas.with.pooled.normals.R

@@ -23,7 +23,7 @@ call.cnas.with.pooled.normals <- function(
 			}
 
 		# identify and process XY probes separately
-		xy.processed.data <- NanoStringNormCNV:::process.xy.probes(
+		xy.processed.data <- process.xy.probes(
 			normalized.data = normalized.data,
 			sex.info = phenodata[, c("SampleID", "Sex")]
 			);
@@ -42,7 +42,7 @@ call.cnas.with.pooled.normals <- function(
 	use.genes <- which(normalized.data$CodeClass %in% use.codeclass);
 
 	# calculate tumour:normal ratios (for autosome and female sex chrom probes)
-	cna.raw <- NanoStringNormCNV:::call.copy.number.values(
+	cna.raw <- call.copy.number.values(
 		normalized.data = normalized.data[use.genes,],
 		reference = phenodata$SampleID[is.ref],
 		per.chip = per.chip,
@@ -54,7 +54,7 @@ call.cnas.with.pooled.normals <- function(
 
 	# calculate tumour:normal ratios (for male sex chrom probes)
 	if (!is.null(sex.probes) && ncol(normalized.data.XY) > 0 && length(use.genes.XY) > 0 && length(is.ref.XY) > 0) {
-		cna.raw.XY <- NanoStringNormCNV:::call.copy.number.values(
+		cna.raw.XY <- call.copy.number.values(
 			normalized.data = normalized.data.XY[use.genes.XY,],
 			reference = phenodata[phenodata$Sex %in% 'M',]$SampleID[is.ref.XY],
 			per.chip = per.chip,
@@ -71,7 +71,7 @@ call.cnas.with.pooled.normals <- function(
 		}
 
 	# call CNAs in normals (for autosome and female sex chrom probes)
-	cna.normals.unadj <- NanoStringNormCNV:::call.copy.number.values(
+	cna.normals.unadj <- call.copy.number.values(
 		normalized.data = normalized.data[, c(1:3, (is.ref + 3))],
 		reference = phenodata$SampleID[is.ref],
 		per.chip = FALSE,
@@ -105,7 +105,7 @@ call.cnas.with.pooled.normals <- function(
 			}
 
 		# call CNAs in tumours (for autosome and female sex chrom probes)
-		cna.rounded <- NanoStringNormCNV:::call.copy.number.values(
+		cna.rounded <- call.copy.number.values(
 			normalized.data = normalized.data[use.genes,],
 			reference = phenodata$SampleID[is.ref],
 			per.chip = per.chip,
@@ -117,7 +117,7 @@ call.cnas.with.pooled.normals <- function(
 
 		# call CNAs in tumours (for male sex chrom probes)
 		if (!is.null(sex.probes) && ncol(normalized.data.XY) > 0 && length(use.genes.XY) > 0 && length(is.ref.XY) > 0) {
-			cna.rounded.XY <- NanoStringNormCNV:::call.copy.number.values(
+			cna.rounded.XY <- call.copy.number.values(
 				normalized.data = normalized.data.XY[use.genes.XY,],
 				reference = phenodata[phenodata$Sex %in% 'M',]$SampleID[is.ref.XY],
 				per.chip = per.chip,
@@ -143,7 +143,7 @@ call.cnas.with.pooled.normals <- function(
 			}
 
 		# call CNAs in tumours (for autosome and female sex chrom probes)
-		cna.rounded <- NanoStringNormCNV:::call.copy.number.values(
+		cna.rounded <- call.copy.number.values(
 			normalized.data = normalized.data[use.genes,],
 			reference = phenodata$SampleID[is.ref],
 			per.chip = per.chip,
@@ -156,7 +156,7 @@ call.cnas.with.pooled.normals <- function(
 
 		# call CNAs in tumours (for male sex chrom probes)
 		if (!is.null(sex.probes) && ncol(normalized.data.XY) > 0 && length(use.genes.XY) > 0 && length(is.ref.XY) > 0) {
-			cna.rounded.XY <- NanoStringNormCNV:::call.copy.number.values(
+			cna.rounded.XY <- call.copy.number.values(
 				normalized.data = normalized.data.XY[use.genes.XY,],
 				reference = phenodata[phenodata$Sex %in% 'M',]$SampleID[is.ref.XY],
 				per.chip = per.chip,
@@ -178,7 +178,7 @@ call.cnas.with.pooled.normals <- function(
 
 	# call CNAs in normals (for male sex chrom probes)
 	if (!is.null(sex.probes) && ncol(normalized.data.XY) > 0 && length(use.genes.XY) > 0 && length(is.ref.XY) > 0) {
-		cna.normals.unadj.XY <- NanoStringNormCNV:::call.copy.number.values(
+		cna.normals.unadj.XY <- call.copy.number.values(
 			normalized.data = normalized.data.XY[, c(1:3, (is.ref.XY + 3))],
 			reference = phenodata[phenodata$Sex %in% 'M',]$SampleID[is.ref.XY],
 			per.chip = FALSE,

R/call.copy.number.values.R

@@ -32,7 +32,7 @@ call.copy.number.values <- function(
 		}
 
 	# get tumour:normal ratios
-	out.cna <- NanoStringNormCNV:::get.tumour.normal.ratio(
+	out.cna <- get.tumour.normal.ratio(
 		normalized.data = normalized.data,
 		reference = reference,
 		chip.info = chip.info,
@@ -52,15 +52,15 @@ call.copy.number.values <- function(
 	if (thresh.method == 'round') {
 
 		# segment using set thresholds
-		out.cna.final <- NanoStringNormCNV:::apply.ns.cna.thresh(
+		out.cna.final <- apply.ns.cna.thresh(
 			ratio.data = out.cna,
 			cna.thresh = cna.thresh
 			);
 
 	} else if (thresh.method == 'KD') {
 
 		# segment using thresholds obtained through kernel density approach
-		out.cna.final <- NanoStringNormCNV:::apply.kd.cna.thresh(
+		out.cna.final <- apply.kd.cna.thresh(
 			ratio.data = out.cna,
 			kd.values = kd.values,
 			neutral.cn = multi.factor

R/evaluate.replicates.R

@@ -43,15 +43,15 @@ evaluate.replicates <- function(phenodata, normalized.data = NULL, cna.rounded =
 
 	# calculate count variance
 	if (!is.null(count.reps)) {
-		var.matrix <- NanoStringNormCNV:::calculate.replicate.variance(
+		var.matrix <- calculate.replicate.variance(
 			normalized.data.reps = count.reps,
 			phenodata.reps = pheno.reps.count
 			);
 		}
 
 	# calculate CNA concordance
 	if (!is.null(cna.reps)) {
-		conc.matrix <- NanoStringNormCNV:::calculate.replicate.concordance(
+		conc.matrix <- calculate.replicate.concordance(
 			cna.rounded.reps = cna.reps,
 			phenodata.reps = pheno.reps.cna
 			);

R/make.cna.heatmap.R

@@ -32,7 +32,7 @@ make.cna.heatmap <- function(nano.cnas, fname.stem = NULL, covs.rows = NULL, cov
 				}
 
 			# create covariates
-			cov.objs <- NanoStringNormCNV:::generate.plot.covariates(
+			cov.objs <- generate.plot.covariates(
 				plotting.data = nano.cnas,
 				sample.covariates = c.row,
 				gene.covariates = c.col
@@ -50,7 +50,7 @@ make.cna.heatmap <- function(nano.cnas, fname.stem = NULL, covs.rows = NULL, cov
 				}
 
 			cov.list <- cov.list[!(names(cov.list) %in% c('SampleID', 'Name'))];
-			covs.legend <- NanoStringNormCNV:::generate.plot.legend(cov.info = cov.list);
+			covs.legend <- generate.plot.legend(cov.info = cov.list);
 		} else {
 			covs.legend <- NULL;
 			}

R/make.counts.heatmap.R

@@ -34,7 +34,7 @@ make.counts.heatmap <- function(nano.counts, fname.stem = NULL, covs.rows = NULL
 
 	if (!is.null(c.col) | !is.null(c.row)) {
 		# covariates
-		cov.objs <- NanoStringNormCNV:::generate.plot.covariates(
+		cov.objs <- generate.plot.covariates(
 			plotting.data = nano.counts,
 			sample.covariates = c.row,
 			gene.covariates = c.col
@@ -52,7 +52,7 @@ make.counts.heatmap <- function(nano.counts, fname.stem = NULL, covs.rows = NULL
 			}
 
 		cov.list <- cov.list[!(names(cov.list) %in% c('SampleID', 'Name'))];
-		covs.legend <- NanoStringNormCNV:::generate.plot.legend(cov.info = cov.list);
+		covs.legend <- generate.plot.legend(cov.info = cov.list);
 	} else {
 		covs.legend <- NULL;
 		}

R/make.positive.control.plot.R

@@ -12,15 +12,15 @@ make.positive.control.plot <- function(correlations, covs = NULL, print.x.labels
 
 	if (!is.null(covs) & ncol(covs) > 1) {
 		# covariates
-		cov.objs <- NanoStringNormCNV:::generate.plot.covariates(
+		cov.objs <- generate.plot.covariates(
 			plotting.data = correlations,
 			sample.covariates = covs
 			);
 		cov.obj <- cov.objs[['sample']];
 
 		# legend
 		covs <- covs[, names(covs) != 'SampleID'];
-		covs.legend <- NanoStringNormCNV:::generate.plot.legend(cov.info = as.list(covs));
+		covs.legend <- generate.plot.legend(cov.info = as.list(covs));
 
 		clust.dim <- 'columns';
 	} else {

R/make.sample.correlations.heatmap.R

@@ -12,15 +12,15 @@ make.sample.correlations.heatmap <- function(nano.counts, cor.method = 'pearson'
 			}
 
 		# covariates
-		cov.objs <- NanoStringNormCNV:::generate.plot.covariates(
+		cov.objs <- generate.plot.covariates(
 			plotting.data = nano.counts,
 			sample.covariates = covs
 			);
 		cov.obj <- cov.objs[['sample']];
 
 		# legend
 		covs <- covs[, names(covs) != 'SampleID', drop = FALSE];
-		covs.legend <- NanoStringNormCNV:::generate.plot.legend(cov.info = as.list(covs));
+		covs.legend <- generate.plot.legend(cov.info = as.list(covs));
 	} else {
 		covs.legend <- NULL;
 		cov.obj <- NULL;

R/restriction.fragmentation.qc.R

@@ -49,7 +49,7 @@ restriction.fragmentation.qc <- function(raw.data){
         }
 
 	# plot the ratios
-	NanoStringNormCNV:::make.restriction.fragmentation.plot(
+	make.restriction.fragmentation.plot(
 		restr.data = nano.restr.avg,
 		low.ratio.samples = which.low
 		);