New version

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: DAtest
 Title: Comparing Differential Abundance methods
-Version: 2.6.0
+Version: 2.6.1
 Authors@R: person("Jakob", "Russel", email = "[email protected]", role = c("aut", "cre"))
 Description: What the title says.
 Depends: R (>= 3.2.5)

R/DA.sam.R

@@ -9,7 +9,7 @@
 #' @import samr
 #' @export
 DA.sam <- function(data, predictor, paired = NULL, fdr.output = 0.05, allResults = FALSE, ...){
-  
+
   # Extract from phyloseq
   if(class(data) == "phyloseq"){
     if(length(predictor) > 1 | length(paired) > 1) stop("When data is a phyloseq object predictor and paired should only contain the name of the variables in sample_data")

R/DA.zzz.R

@@ -34,7 +34,7 @@ DA.zzz <- function(data, predictor, paired = NULL, covars = NULL, p.adj = "fdr",
     count_table <- data
   }
 
-  res <- dafun(count_table, predictor, paired, covars)
+  res <- FUN(count_table, predictor, paired, covars)
 
   # Check output
   if(!all(c("pval","Feature","Method") %in% colnames(res))) stop("The following columns has to be present in output: pval, Feature and Method")

R/zzz.R

@@ -1,6 +1,12 @@
-# Fix samr options problem
 
 .onLoad <- function(libname, pkgname){
-  options(error=NULL)
+  message("DAtest version 2.6.1")
+
+  # Fix samr problem
+  if(.Platform$OS.type == "windows"){
+    if("samr" %in% rownames(installed.packages())){
+      options(error = NULL)
+    }
+  }
 }
 

README.md

@@ -31,11 +31,16 @@ in choosing a method for a specific dataset based on empirical testing.
 
 ### Citation
 
-Some scripts, including the spike-in for estimating AUC, is borrowed
-from: [Thorsen J, Brejnrod A et al. Large-scale benchmarking reveals
-false discoveries and count transformation sensitivity in 16S rRNA gene
-amplicon data analysis methods used in microbiome studies. *Microbiome*
-(2016)](https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-016-0208-8)
+Please cite the following publication if you use the DAtest package:
+
+Russel *et al.* (2017) DAtest: A framework for comparing differential
+abundance and expression methods. *In Prep*
+
+Remember also to cite the method you end up using for your final
+analysis (See [implemented methods](#implemented-methods) for links). If
+there is no associated publication (e.g. for a t-test) you can cite as
+follows: ."we used t-test as implemented in DAtest version x.x.x (Russel
+*et al.* 2017)"
 
 Overview of this tutorial
 -------------------------
@@ -231,6 +236,16 @@ false positives. This can be tuned with the `sig` ("anc") and
 
 P-values for baySeq are defined as 1 - posterior likelihoods.
 
+### *How to choose the effect size:*
+
+The default effect size of 2 (equal to a log2 fold change of 1) might
+not fit for your data. If the best method has an AUC below 0.7 you might
+want to increase the effect size. In contrast, if several methods have
+an AUC close to 1, you might want to decrease the effect size (towards
+1) to better differentiate the methods. It is also possible to test for
+negative associations with effect sizes between 0 and 1, but this has
+not been tested extensively.
+
 How to run real data
 ====================