veupathUtils -> mbioUtils

.github/workflows/R-CMD-check.yaml

@@ -39,6 +39,12 @@ jobs:
           http-user-agent: ${{ matrix.config.http-user-agent }}
           use-public-rspm: true
 
+      - name: Install Maaslin2 from GitHub
+        run: |
+          if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
+          remotes::install_github("biobakery/Maaslin2")
+        shell: Rscript {0}
+
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: any::rcmdcheck

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: microbiomeComputations
 Title: Compute Results for Microbiome Analyses
-Version: 5.1.6
+Version: 5.1.7
 Authors@R: 
     c(person(given = "Danielle",
            family = "Callan",
@@ -20,14 +20,16 @@ Imports:
     purrr,
     Rcpp (>= 0.11.0),
     S4Vectors,
-    SpiecEasi,
+    SpiecEasi (>= 1.0.7),
     stringi,
     vegan,
-    veupathUtils (>= 2.7.0)
+    mbioUtils (>= 0.1.0)
 Depends:
     R (>= 2.10)
 Remotes:
-    VEuPathDB/veupathUtils
+    microbiomeDB/mbioUtils,
+    zdk123/SpiecEasi@v1.0.7
+biocViews:
 LinkingTo: Rcpp
 URL: https://github.com/microbiomeDB/microbiomeComputations, https://microbiomedb.github.io/microbiomeComputations/
 BugReports: https://github.com/microbiomeDB/microbiomeComputations/issues
@@ -36,7 +38,7 @@ License: Apache License (>= 2)
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.3
 Suggests: 
     testthat (>= 3.0.0)
 Config/testthat/edition: 3

NAMESPACE

@@ -18,12 +18,14 @@ exportClasses(DifferentialAbundanceResult)
 exportMethods(selfCorrelation)
 import(DESeq2)
 import(data.table)
+import(mbioUtils)
 import(methods)
-import(veupathUtils)
 importFrom(Maaslin2,Maaslin2)
 importFrom(Rcpp,sourceCpp)
 importFrom(S4Vectors,SimpleList)
 importFrom(ape,pcoa)
+importFrom(mbioUtils,CollectionWithMetadata)
+importFrom(mbioUtils,SampleMetadata)
 importFrom(methods,new)
 importFrom(methods,slot)
 importFrom(methods,validObject)
@@ -38,6 +40,4 @@ importFrom(stringi,stri_replace_all_fixed)
 importFrom(stringi,stri_trans_totitle)
 importFrom(vegan,diversity)
 importFrom(vegan,vegdist)
-importFrom(veupathUtils,CollectionWithMetadata)
-importFrom(veupathUtils,SampleMetadata)
 useDynLib(microbiomeComputations)

R/class-AbundanceData.R

@@ -37,7 +37,7 @@ check_abundance_data <- function(object) {
 #' @slot removeEmptyRecords A logical indicating whether empty (all NA/ zero) samples should be removed.
 #' @name AbundanceData-class
 #' @rdname AbundanceData-class
-#' @importFrom veupathUtils SampleMetadata
-#' @importFrom veupathUtils CollectionWithMetadata
+#' @importFrom mbioUtils SampleMetadata
+#' @importFrom mbioUtils CollectionWithMetadata
 #' @export 
 AbundanceData <- setClass("AbundanceData", contains = "CollectionWithMetadata", validity = check_abundance_data)

R/method-alphaDiv.R

@@ -8,7 +8,7 @@
 #' @return ComputeResult object
 #' @importFrom vegan diversity
 #' @importFrom stringi stri_trans_totitle
-#' @import veupathUtils
+#' @import mbioUtils
 #' @import data.table
 #' @importFrom methods new slot validObject
 #' @importFrom stats as.dist as.formula median quantile var
@@ -29,16 +29,16 @@ setMethod("alphaDiv", signature("AbundanceData"), function(data, method = c('sha
     naToZero <- data@imputeZero
 
     # Initialize and check inputs
-    method <- veupathUtils::matchArg(method)
-    verbose <- veupathUtils::matchArg(verbose)
+    method <- mbioUtils::matchArg(method)
+    verbose <- mbioUtils::matchArg(verbose)
 
     # Check that incoming df meets requirements
     if (!'data.table' %in% class(df)) {
       data.table::setDT(df)
     }
 
     computeMessage <- ''
-    veupathUtils::logWithTime(paste("Received df table with", nrow(df), "samples and", (ncol(df)-1), "taxa."), verbose)
+    mbioUtils::logWithTime(paste("Received df table with", nrow(df), "samples and", (ncol(df)-1), "taxa."), verbose)
 
     # Compute alpha diversity
     if (identical(method, 'shannon') | identical(method, 'simpson')){
@@ -58,41 +58,41 @@ setMethod("alphaDiv", signature("AbundanceData"), function(data, method = c('sha
     result@ancestorIdColumns <- ancestorIdColumns
 
     # Handle errors or return positive computeMessage
-    if (veupathUtils::is.error(alphaDivDT)) {
-      veupathUtils::logWithTime(paste('Alpha diversity computation FAILED with parameters, method =', method), verbose)
+    if (mbioUtils::is.error(alphaDivDT)) {
+      mbioUtils::logWithTime(paste('Alpha diversity computation FAILED with parameters, method =', method), verbose)
       stop() 
     } else {
       computeMessage <- paste('Computed', method, 'alpha diversity.')
-      veupathUtils::logWithTime(paste(method, 'alpha diversity computation complete.'), verbose)
+      mbioUtils::logWithTime(paste(method, 'alpha diversity computation complete.'), verbose)
     }
 
     # Assemble data table
     dt <- data.table::as.data.table(df[, ..allIdColumns])
     dt$alphaDiversity <- alphaDivDT
 
-    entity <- veupathUtils::strSplit(recordIdColumn, ".", 4, 1)
+    entity <- mbioUtils::strSplit(recordIdColumn, ".", 4, 1)
     result@computationDetails <- computeMessage
     result@parameters <- paste('method =', method)
     result@recordIdColumn <- recordIdColumn
     result@ancestorIdColumns <- ancestorIdColumns
 
-    computedVariableMetadata <- veupathUtils::VariableMetadata(
-                 variableClass = veupathUtils::VariableClass(value = "computed"),
-                 variableSpec = veupathUtils::VariableSpec(variableId = names(dt[, -..allIdColumns]), entityId = entity),
-                 plotReference = veupathUtils::PlotReference(value = "yAxis"),
+    computedVariableMetadata <- mbioUtils::VariableMetadata(
+                 variableClass = mbioUtils::VariableClass(value = "computed"),
+                 variableSpec = mbioUtils::VariableSpec(variableId = names(dt[, -..allIdColumns]), entityId = entity),
+                 plotReference = mbioUtils::PlotReference(value = "yAxis"),
                  displayName = computedVarLabel,
                  displayRangeMin = 0,
                  displayRangeMax = max(max(dt$alphaDiversity, na.rm = TRUE),1),
-                 dataType = veupathUtils::DataType(value = "NUMBER"),
-                 dataShape = veupathUtils::DataShape(value = "CONTINUOUS")
+                 dataType = mbioUtils::DataType(value = "NUMBER"),
+                 dataShape = mbioUtils::DataShape(value = "CONTINUOUS")
       )
 
-    result@computedVariableMetadata <- veupathUtils::VariableMetadataList(S4Vectors::SimpleList(computedVariableMetadata))
-    names(dt) <- veupathUtils::stripEntityIdFromColumnHeader(names(dt))
+    result@computedVariableMetadata <- mbioUtils::VariableMetadataList(S4Vectors::SimpleList(computedVariableMetadata))
+    names(dt) <- mbioUtils::stripEntityIdFromColumnHeader(names(dt))
     result@data <- dt 
 
     validObject(result)
-    veupathUtils::logWithTime(paste('Alpha diversity computation completed with parameters method=', method), verbose)
+    mbioUtils::logWithTime(paste('Alpha diversity computation completed with parameters method=', method), verbose)
 
     return(result)
 })

R/method-betaDiv.R

@@ -11,7 +11,7 @@
 #' @importFrom vegan vegdist
 #' @importFrom ape pcoa
 #' @importFrom stringi stri_replace_all_fixed
-#' @import veupathUtils
+#' @import mbioUtils
 #' @import data.table
 #' @useDynLib microbiomeComputations
 #' @rdname betaDiv-methods
@@ -31,16 +31,16 @@ setMethod("betaDiv", signature("AbundanceData"), function(data, method = c('bray
     allIdColumns <- c(recordIdColumn, ancestorIdColumns)
 
     # Initialize and check inputs
-    method <- veupathUtils::matchArg(method)
-    verbose <- veupathUtils::matchArg(verbose)
+    method <- mbioUtils::matchArg(method)
+    verbose <- mbioUtils::matchArg(verbose)
 
     # Check that incoming df meets requirements - consider moving to a validateOTU function or similar
     if (!'data.table' %in% class(df)) {
       data.table::setDT(df)
     }
 
     computeMessage <- ''
-    veupathUtils::logWithTime(paste("Received df table with", nrow(df), "samples and", (ncol(df)-1), "taxa."), verbose)
+    mbioUtils::logWithTime(paste("Received df table with", nrow(df), "samples and", (ncol(df)-1), "taxa."), verbose)
 
     # Compute beta diversity using given dissimilarity method
     if (identical(method, 'bray') | identical(method, 'jaccard')) {
@@ -62,11 +62,11 @@ setMethod("betaDiv", signature("AbundanceData"), function(data, method = c('bray
     result@ancestorIdColumns <- ancestorIdColumns
 
     # Handle errors or return positive computeMessage
-    if (veupathUtils::is.error(dist)) {
-      veupathUtils::logWithTime(paste('Beta diversity computation FAILED with parameters method=', method, ', k=', k), verbose)
+    if (mbioUtils::is.error(dist)) {
+      mbioUtils::logWithTime(paste('Beta diversity computation FAILED with parameters method=', method, ', k=', k), verbose)
       stop() 
     } else {
-      veupathUtils::logWithTime("Computed dissimilarity matrix.", verbose)
+      mbioUtils::logWithTime("Computed dissimilarity matrix.", verbose)
       computeMessage <- paste(method, "dissimilarity matrix computation successful.")
     }
 
@@ -80,7 +80,7 @@ setMethod("betaDiv", signature("AbundanceData"), function(data, method = c('bray
 
     dt <- cbind(dt, df[, ..allIdColumns])
     data.table::setcolorder(dt, allIdColumns)
-    veupathUtils::logWithTime("Finished ordination step.", verbose)
+    mbioUtils::logWithTime("Finished ordination step.", verbose)
 
     # Extract percent variance
     eigenvecs <- pcoa$values$Relative_eig
@@ -91,38 +91,38 @@ setMethod("betaDiv", signature("AbundanceData"), function(data, method = c('bray
     # For now returning data and percentVar for how much is in the plot.
     percentVar <- percentVar[1:k]
 
-    entity <- veupathUtils::strSplit(recordIdColumn,".", 4, 1)
+    entity <- mbioUtils::strSplit(recordIdColumn,".", 4, 1)
     result@computationDetails <- paste(computeMessage, ', pcoaVariance =', percentVar)
     result@parameters <- paste('method =', method)   
 
     axesNames <- names(dt[, -..allIdColumns])
     displayNames <- paste0(axesNames, " ", sprintf(percentVar,fmt = '%#.1f'), "%")
 
     makeVariableMetadataObject <- function(displayName) {
-      axisName <- veupathUtils::strSplit(displayName, " ")
+      axisName <- mbioUtils::strSplit(displayName, " ")
       #bit hacky, see if you can think of something better
       plotRef <- ifelse(grepl('Axis1', displayName, fixed=T), 'xAxis', 'yAxis')
 
-      veupathUtils::VariableMetadata(
-                 variableClass = veupathUtils::VariableClass(value = "computed"),
-                 variableSpec = veupathUtils::VariableSpec(variableId = axisName, entityId = entity),
-                 plotReference = veupathUtils::PlotReference(value = plotRef),
+      mbioUtils::VariableMetadata(
+                 variableClass = mbioUtils::VariableClass(value = "computed"),
+                 variableSpec = mbioUtils::VariableSpec(variableId = axisName, entityId = entity),
+                 plotReference = mbioUtils::PlotReference(value = plotRef),
                  displayName = displayName,
                  displayRangeMin = min(dt[[axisName]]),
                  displayRangeMax = max(dt[[axisName]]),
-                 dataType = veupathUtils::DataType(value = "NUMBER"),
-                 dataShape = veupathUtils::DataShape(value = "CONTINUOUS")
+                 dataType = mbioUtils::DataType(value = "NUMBER"),
+                 dataShape = mbioUtils::DataShape(value = "CONTINUOUS")
       )
     }
 
-    computedVariableMetadata <- veupathUtils::VariableMetadataList(lapply(displayNames, makeVariableMetadataObject))
+    computedVariableMetadata <- mbioUtils::VariableMetadataList(lapply(displayNames, makeVariableMetadataObject))
 
     result@computedVariableMetadata <- computedVariableMetadata
-    names(dt) <- veupathUtils::stripEntityIdFromColumnHeader(names(dt))
+    names(dt) <- mbioUtils::stripEntityIdFromColumnHeader(names(dt))
     result@data <- dt
 
     validObject(result)
-    veupathUtils::logWithTime(paste('Beta diversity computation completed with parameters recordIdColumn=', recordIdColumn, ', method =', method, ', k =', k, ', verbose =', verbose), verbose)
+    mbioUtils::logWithTime(paste('Beta diversity computation completed with parameters recordIdColumn=', recordIdColumn, ', method =', method, ', k =', k, ', verbose =', verbose), verbose)
 
     return(result)
 })

R/method-correlation.R

@@ -1,12 +1,12 @@
-correlationGeneric <- getGeneric("correlation", package = "veupathUtils")
-selfCorrelationGeneric <- getGeneric("selfCorrelation", package = "veupathUtils")
+correlationGeneric <- getGeneric("correlation", package = "mbioUtils")
+selfCorrelationGeneric <- getGeneric("selfCorrelation", package = "mbioUtils")
 
 setClassUnion("missingOrNULL", c("missing", "NULL"))
 
 #' Self Correlation
 #'
 #' This function returns correlation coefficients for variables in one AbundanceData object against itself. It generally serves as a 
-#' convenience wrapper around veupathUtils::correlation, with the exception that it additionally supports sparcc.
+#' convenience wrapper around mbioUtils::correlation, with the exception that it additionally supports sparcc.
 #' 
 #' @param data An AbundanceData object
 #' @param method string defining the type of correlation to run. The currently supported values are 'spearman','pearson' and 'sparcc'
@@ -16,31 +16,31 @@ setClassUnion("missingOrNULL", c("missing", "NULL"))
 #' @param varianceThreshold numeric threshold to filter features by variance across samples
 #' @param stdDevThreshold numeric threshold to filter features by standard deviation across samples
 #' @return ComputeResult object
-#' @import veupathUtils
+#' @import mbioUtils
 #' @export
 #' @rdname selfCorrelation-methods
 #' @aliases selfCorrelation,AbundanceData-method
 setMethod(selfCorrelationGeneric, signature("AbundanceData"), 
 function(data, method = c('spearman','pearson','sparcc'), format = c('ComputeResult', 'data.table'), verbose = c(TRUE, FALSE), proportionNonZeroThreshold = 0.5, varianceThreshold = 0, stdDevThreshold = 0) {
 
-  format <- veupathUtils::matchArg(format)
-  method <- veupathUtils::matchArg(method)
-  verbose <- veupathUtils::matchArg(verbose)
+  format <- mbioUtils::matchArg(format)
+  method <- mbioUtils::matchArg(method)
+  verbose <- mbioUtils::matchArg(verbose)
 
   #prefilters applied
-  data <- veupathUtils::pruneFeatures(data, predicateFactory('proportionNonZero', proportionNonZeroThreshold), verbose)
-  data <- veupathUtils::pruneFeatures(data, predicateFactory('variance', varianceThreshold), verbose)
-  data <- veupathUtils::pruneFeatures(data, predicateFactory('sd', stdDevThreshold), verbose)
+  data <- mbioUtils::pruneFeatures(data, predicateFactory('proportionNonZero', proportionNonZeroThreshold), verbose)
+  data <- mbioUtils::pruneFeatures(data, predicateFactory('variance', varianceThreshold), verbose)
+  data <- mbioUtils::pruneFeatures(data, predicateFactory('sd', stdDevThreshold), verbose)
 
   abundances <- getAbundances(data, FALSE, FALSE, verbose)
-  corrResult <- veupathUtils::correlation(abundances, NULL, method = method, format = 'data.table', verbose = verbose)
+  corrResult <- mbioUtils::correlation(abundances, NULL, method = method, format = 'data.table', verbose = verbose)
 
-  veupathUtils::logWithTime(paste("Received df table with", nrow(abundances), "samples and", (ncol(abundances)-1), "features with abundances."), verbose)
+  mbioUtils::logWithTime(paste("Received df table with", nrow(abundances), "samples and", (ncol(abundances)-1), "features with abundances."), verbose)
 
   if (format == 'data.table') {
     return(corrResult)
   } else {
-    result <- veupathUtils::buildCorrelationComputeResult(corrResult, data, NULL, method, verbose)
+    result <- mbioUtils::buildCorrelationComputeResult(corrResult, data, NULL, method, verbose)
     result@computationDetails <- 'selfCorrelation'
     return(result)
   }