From 250f29b71fc7727e7422bcd05276a64a505340ff Mon Sep 17 00:00:00 2001
From: LTLA <infinite.monkeys.with.keyboards@gmail.com>
Date: Thu, 25 Apr 2019 17:38:18 -0700
Subject: [PATCH] Enforce use of TMM to bypass changes in calcNormFactors
 defaults. Relaxed normalization test.

---
 DESCRIPTION                | 4 ++--
 R/normFactors.R            | 4 ++--
 man/normOffsets.Rd         | 4 +++-
 tests/testthat/test-norm.R | 2 +-
 4 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index b78c21f..3c39664 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: csaw
-Version: 1.17.8
-Date: 2019-03-13
+Version: 1.17.9
+Date: 2019-04-25
 Title: ChIP-Seq Analysis with Windows
 Authors@R: c(person("Aaron", "Lun", role=c("aut", "cre"), email =
         "infinite.monkeys.with.keyboards@gmail.com"),
diff --git a/R/normFactors.R b/R/normFactors.R
index 35410fe..05bf859 100644
--- a/R/normFactors.R
+++ b/R/normFactors.R
@@ -1,7 +1,7 @@
 #' @export
 #' @importFrom edgeR calcNormFactors
 #' @importFrom SummarizedExperiment assay
-normFactors <- function(object, weighted=FALSE, ..., assay.id="counts", se.out=TRUE) 
+normFactors <- function(object, method="TMM", weighted=FALSE, ..., assay.id="counts", se.out=TRUE) 
 # This provides a wrapper to perform TMM normalization with non-standard
 # library sizes (e.g. due to filtering) and weighting turned off.
 # Alternatively, it can do a form a fast loess-like normalization which uses
@@ -17,7 +17,7 @@ normFactors <- function(object, weighted=FALSE, ..., assay.id="counts", se.out=T
     }
 
 	out <- calcNormFactors(assay(object, i=assay.id, withDimnames=FALSE), 
-        lib.size=lib.sizes, doWeighting=weighted, ...)
+        lib.size=lib.sizes, doWeighting=weighted, method=method, ...)
 
 	# Choosing to put these values in a different location, if requested. 
     if (!is.logical(se.out)) { 
diff --git a/man/normOffsets.Rd b/man/normOffsets.Rd
index c40c758..1701ca0 100644
--- a/man/normOffsets.Rd
+++ b/man/normOffsets.Rd
@@ -9,12 +9,14 @@
 \usage{
 normOffsets(object, type=NULL, ..., assay.id="counts", se.out=TRUE)
 
-normFactors(object, weighted=FALSE, ..., assay.id="counts", se.out=TRUE)
+normFactors(object, method="TMM", weighted=FALSE, ..., 
+    assay.id="counts", se.out=TRUE)
 }
 
 \arguments{
   \item{object}{A SummarizedExperiment object containing a count matrix.}
   \item{type}{Deprecated; a character string indicating what type of normalization is to be performed.}
+  \item{method}{String specifying the type of scaling normalization method to use.}
   \item{weighted}{A logical scalar indicating whether precision weights should be used for TMM normalization.}
   \item{...}{Other arguments to be passed to \code{\link{calcNormFactors}} for \code{type="scaling"}, or \code{\link{loessFit}} for \code{type="loess"}.}
   \item{assay.id}{An integer scalar or string specifying the assay values to use for normalization.}
diff --git a/tests/testthat/test-norm.R b/tests/testthat/test-norm.R
index 0181f31..0798012 100644
--- a/tests/testthat/test-norm.R
+++ b/tests/testthat/test-norm.R
@@ -47,7 +47,7 @@ test_that("testing what happens with undersampling", {
     data <- SummarizedExperiment(list(counts=counts))
     data$totals <- c(sum(mu1), sum(mu2))
     truth <- sqrt(c(1/undersamp, undersamp)) 
-    expect_true(all(abs(normFactors(data)$norm.factors - truth) < 0.05))
+    expect_true(all(abs(normFactors(data)$norm.factors - truth) < 0.1))
 
     # Testing what happens with weighting, after adding some high-abundance DB regions. 
     # The errors should be larger than the unweighted version.