@@ -1,6 +1,6 @@

 Package: metagenomeSeq

 Title: Statistical analysis for sparse high-throughput sequencing

-Version: 1.5.29

+Version: 1.5.30

 Date: 2013-12-13

 Author: Joseph Nathaniel Paulson, Mihai Pop,  Hector Corrada Bravo

 Maintainer: Joseph N. Paulson <jpaulson@umiacs.umd.edu>

@@ -41,8 +41,10 @@ Collate:

     'doEStep.R'

     'exportMat.R'

     'exportStats.R'

-    'fitZig.R'

+    'fitDO.R'

+    'fitMeta.R'

     'fitPA.R'

+    'fitZig.R'

     'filterData.R'

     'getEpsilon.R'

     'getCountDensity.R'

@@ -22,6 +22,8 @@ cumNormStatFast,

 expSummary,

 exportMat,

 exportStats,

+fitDO,

+fitMeta,

 fitPA,

 fitZig,

 filterData,

@@ -2,6 +2,7 @@ version 1.5.xx (2013-11-12)

 	+ Incorporating biome format support with the biom2MRexperiment and load_biome function.

 	+ Added filterData, aggregateByTaxonomy(aggTax), calculateEffectiveSamples, functions.

 	+ Renamed MRfisher to fitPA (presence-absence fisher test).

+	+ Added fitDO (Discovery odds ratio test) and fitMeta (original metastats).

 	+ Added match.call() info to fitZig output

 version 1.2.xx (2013-08-20)

 	+ Our paper got accepted and is available!

@@ -10,6 +10,7 @@

 #' @param lvl featureData column name from the MRexperiment object.

 #' @param alternate Use the rowname for undefined OTUs instead of aggregating to "no_match".

 #' @param norm Whether to aggregate normalized counts or not.

+#' @param log Whether or not to log2 transform the counts - if MRexperiment object.

 #' @param aggfun Aggregation function.

 #' @return An aggregated count matrix.

 #' @aliases aggTax

@@ -20,9 +21,9 @@

 #' # not run

 #' # aggregateByTaxonomy(mouseData,lvl="genus",norm=TRUE,aggfun=colMedians)

 #' 

-aggregateByTaxonomy<-function(obj,lvl,alternate=FALSE,norm=TRUE,aggfun = colSums){

+aggregateByTaxonomy<-function(obj,lvl,alternate=FALSE,norm=TRUE,log=FALSE,aggfun = colSums){

 	if(class(obj)=="MRexperiment"){

-		mat = MRcounts(obj,norm=norm,log=FALSE)

+		mat = MRcounts(obj,norm=norm,log=log)

     } else {

 		stop("Object needs to be a MRexperiment object. If it's a matrix, see aggregateM.")

     }

@@ -48,6 +49,6 @@ aggregateByTaxonomy<-function(obj,lvl,alternate=FALSE,norm=TRUE,aggfun = colSums

 }

 #' @rdname aggregateByTaxonomy

 #' @export

-aggTax<-function(obj,lvl,alternate=FALSE,norm=TRUE,aggfun = colSums){

-    aggregateByTaxonomy(obj,lvl,alternate=alternate,norm=norm,aggfun = aggfun)

+aggTax<-function(obj,lvl,alternate=FALSE,norm=TRUE,log=FALSE,aggfun = colSums){

+    aggregateByTaxonomy(obj,lvl,alternate=alternate,norm=norm,log=log,aggfun = aggfun)

 }

\ No newline at end of file

@@ -29,15 +29,18 @@ fitPA<-function(obj,cl,thres=0){

     }

     nrows= nrow(x);

 	if(is.null(rownames(x))){rownames(x)=1:nrows}

-    

+

+    nClass1 = sum(cl==unique(cl)[1])

+    nClass2 = sum(cl==unique(cl)[2])

+

     res = sapply(1:nrows,function(i){

         tbl = table(1-x[i,],cl)

         if(sum(dim(tbl))!=4){

             tbl = array(0,dim=c(2,2));

             tbl[1,1] = sum(x[i,cl==unique(cl)[1]])

             tbl[1,2] = sum(x[i,cl==unique(cl)[2]])

-            tbl[2,1] = sum(cl==unique(cl)[1])-tbl[1,1]

-            tbl[2,2] = sum(cl==unique(cl)[2])-tbl[1,2]

+            tbl[2,1] = nClass1-tbl[1,1]

+            tbl[2,2] = nClass2-tbl[1,2]

         }

         ft <- fisher.test(tbl, workspace = 8e6, alternative = "two.sided", conf.int = T)

         list(p=ft$p.value,o=ft$estimate,cl=ft$conf.int[1],cu=ft$conf.int[2])

@@ -8,10 +8,11 @@ MRexperiment and a particular featureData column (i.e. 'genus') will aggregate c

 to the desired level using the aggfun function (default colSums). Possible aggfun alternatives

 include colMeans and colMedians.}

 \usage{

-aggregateByTaxonomy(obj, lvl, alternate = FALSE, norm = TRUE,

+aggregateByTaxonomy(obj, lvl, alternate = FALSE, norm = TRUE, log = FALSE,

   aggfun = colSums)

-aggTax(obj, lvl, alternate = FALSE, norm = TRUE, aggfun = colSums)

+aggTax(obj, lvl, alternate = FALSE, norm = TRUE, log = FALSE,

+  aggfun = colSums)

 }

 \arguments{

   \item{obj}{A MRexperiment object.}

@@ -25,6 +26,9 @@ aggTax(obj, lvl, alternate = FALSE, norm = TRUE, aggfun = colSums)

   \item{norm}{Whether to aggregate normalized counts or

   not.}

+  \item{log}{Whether or not to log2 transform the counts -

+  if MRexperiment object.}

+

   \item{aggfun}{Aggregation function.}

 }

 \value{