Initialise exporters to raw, qiime and mothur

DESCRIPTION

@@ -78,7 +78,6 @@ Imports:
     MASS,
     MatrixGenerics,
     methods,
-    ecodive,
     rlang,
     S4Vectors,
     scater,
@@ -94,6 +93,7 @@ Suggests:
     BiocStyle,
     biomformat,
     dada2,
+    ecodive,
     knitr,
     mediation,
     miaTime,

NAMESPACE

@@ -57,6 +57,9 @@ export(estimateDominance)
 export(estimateEvenness)
 export(estimateFaith)
 export(estimateRichness)
+export(exportMothur)
+export(exportQIIME2)
+export(exportRaw)
 export(full_join)
 export(getAbundanceClass)
 export(getAbundant)
@@ -413,6 +416,8 @@ importFrom(ape,is.binary)
 importFrom(ape,is.rooted)
 importFrom(ape,read.tree)
 importFrom(ape,reorder.phylo)
+importFrom(ape,write.FASTA)
+importFrom(ape,write.tree)
 importFrom(bluster,clusterRows)
 importFrom(decontam,isContaminant)
 importFrom(decontam,isNotContaminant)
@@ -430,8 +435,6 @@ importFrom(dplyr,select)
 importFrom(dplyr,summarise)
 importFrom(dplyr,sym)
 importFrom(dplyr,tally)
-importFrom(ecodive,unweighted_unifrac)
-importFrom(ecodive,weighted_unifrac)
 importFrom(rlang,":=")
 importFrom(rlang,sym)
 importFrom(scater,calculateMDS)

NEWS

@@ -190,5 +190,6 @@ Changes in version 1.19.x
 Changes in version 1.21.x
 + Fixed explained percentages on PCA axes for jointRPCA and hardened
   rank-deficient SVD handling in joint-RPCA (1.21.1, 2026-05-12)
-* Generalise agglomerateByModule to non-binary numerical modules (1.21.2, 2026-05-31)
-* Modify joint-RPCA to match with Gemelli implementation (1.21.3, 2026-06-16)
++ Generalise agglomerateByModule to non-binary numerical modules (1.21.2, 2026-05-31)
++ Modify joint-RPCA to match with Gemelli implementation (1.21.3, 2026-06-16)
++ Implement Striped Unifrac algorithm (1.21.3, 2026-06-12)

R/AllGenerics.R

@@ -105,6 +105,21 @@ setGeneric("convertToBIOM", signature = c("x"),
 setGeneric("convertToPhyloseq", signature = c("x"), function(x, ...)
     standardGeneric("convertToPhyloseq"))
 
+#' @rdname export-methods
+#' @export
+setGeneric("exportRaw", signature = c("x"), function(x, ...)
+    standardGeneric("exportRaw"))
+
+#' @rdname export-methods
+#' @export
+setGeneric("exportQIIME2", signature = c("x"), function(x, ...)
+    standardGeneric("exportQIIME2"))
+
+#' @rdname export-methods
+#' @export
+setGeneric("exportMothur", signature = c("x"), function(x, ...)
+    standardGeneric("exportMothur"))
+
 #' @rdname isContaminant
 #' @export
 setGeneric("addContaminantQC", signature = c("x"),

R/RcppExports.R

@@ -9,3 +9,7 @@
     .Call(`_mia_apply_transformation_difference_or_division`, mat, method)
 }
 
+.unifrac_cpp <- function(assay, rowTree, weighted) {
+    .Call(`_mia_unifrac_cpp`, assay, rowTree, weighted)
+}
+

R/addAlpha.R

@@ -244,9 +244,9 @@
 #' evenly the abundances of different species are distributed. The following
 #' evenness indices are provided:
 #'
-#' By default, four indices are returned, each taking into account different 
-#' aspects: richness (the number of observed unique features), 
-#' dominance (Berger-Parker), information (Shannon), and phylogenetics (Faith) 
+#' By default, four indices are returned, each taking into account different
+#' aspects: richness (the number of observed unique features),
+#' dominance (Berger-Parker), information (Shannon), and phylogenetics (Faith)
 #' (Cassol et al., 2025).
 #'
 #' The available evenness indices include the following (all in lowercase):
@@ -360,6 +360,11 @@
 #' in characterizing microbiomes.
 #' _Genome Res._ 31(11):2131-2137. doi: 10.1101/gr.275777.121
 #'
+#' Armstrong G. et al. (2021)
+#' Efficient computation of Faith's phylogenetic diversity with applications
+#' in characterizing microbiomes.
+#' _Genome Res._ 31(11):2131-2137. doi: 10.1101/gr.275777.121
+#'
 #' Beisel J-N. et al. (2003)
 #' A Comparative Analysis of Diversity Index Sensitivity.
 #' _Internal Rev. Hydrobiol._ 88(1):3-15.
@@ -447,9 +452,9 @@
 #' A tribute to Claude Shannon (1916 –2001) and a plea for more rigorous use of
 #' species richness, species diversity and the ‘Shannon–Wiener’ Index.
 #' _Alpha Ecology & Biogeography_ 12, 177–197.
-#' 
-#' Cassol, I., Ibañez, M. & Bustamante, J.P. (2025) 
-#' Key features and guidelines for the application of microbial alpha diversity 
+#'
+#' Cassol, I., Ibañez, M. & Bustamante, J.P. (2025)
+#' Key features and guidelines for the application of microbial alpha diversity
 #' metrics. _Sci Rep_ 15, 622. doi:10.1038/s41598-024-77864-y
 #'
 #' @seealso

R/addDissimilarity.R

@@ -91,9 +91,8 @@
 #' all the abundances of features are equal between two samples, and 0 means
 #' that samples have completely different relative abundances.
 #'
-#' Unifrac is calculated with \code{
-#' \link[ecodive:unweighted_unifrac]{ecodive:unweighted_unifrac()}}
-#' or \code{\link[ecodive:weighted_unifrac]{ecodive:weighted_unifrac()}}.
+#' Unifrac is calculated with a C++ implementation of the Striped Unifrac
+#' algorithm (McDonald et al. 2021).
 #'
 #' If rarefaction is enabled, \code{\link[vegan:avgdist]{vegan:avgdist()}} is
 #' utilized.
@@ -131,6 +130,11 @@
 #'
 #' Lozupone C, Knight R. ``Unifrac: a new phylogenetic method for comparing
 #' microbial communities.'' Appl Environ Microbiol. 2005 71 (12):8228-35.
+#' 
+#' McDonald D, Vázquez-Baeza Y, Koslicki D, McClelland J, Reeve N, Xu Z,
+#' Gonzalez A, Knight R. ``Striped UniFrac: enabling microbiome analysis at
+#' unprecedented scale.'' Nat Methods. 2018 15 (11):847-848.
+#' doi: 10.1038/s41592-018-0187-8.
 #'
 #' For JSD dissimilarity:
 #' Jensen-Shannon Divergence and Hilbert space embedding.

R/calculateUnifrac.R

@@ -1,8 +1,6 @@
 #' @importFrom ape drop.tip
-#' @importFrom ecodive weighted_unifrac
-#' @importFrom ecodive unweighted_unifrac
 .get_unifrac <- function(
-        x, tree, weighted = FALSE, node.label = nodeLab, nodeLab = NULL, ...){
+        x, tree, new = FALSE, weighted = FALSE, node.label = nodeLab, nodeLab = NULL, ...){
     # Transpose the matrix so that the orientation is the same as in other
     # dissimilatity methods
     x <- t(x)
@@ -72,9 +70,8 @@
     # multiple rows are linked to single tip.
     x <- .merge_assay_by_rows(x, node.label, ...)
 
-    # Calculate unifrac. Use implementation from ecodive package
-    FUN <- if( weighted ) weighted_unifrac else unweighted_unifrac
-    res <- FUN(t(x), tree = tree)
+    # Calculate unifrac with C++ algorithm
+    res <- .unifrac_cpp(x, tree, weighted)
     return(res)
 }