@@ -1,7 +1,9 @@

 #' evaluateClustering

 #'

 #' Evaluates a clustering using 'true' labels. Entries with missing true labels

-#' (i.e. NA) are excluded from calculations.

+#' (i.e. NA) are excluded from calculations. If using `evaluteClustering` in a

+#' custom pipeline, you might want to use the corresponding 

+#' `pipeComp:::.aggregateClusterEvaluation` aggregation function.

 #'

 #' @param x The clustering labels

 #' @param tl The true labels

@@ -123,6 +125,8 @@ evaluateClustering <- function(x, tl=NULL){

 #' evaluateDimRed

 #'

 #' Gathers evaluation statistics on a reduced space using known cell labels.

+#' If using `evaluteDimRed` in a custom pipeline, you will probably want to use

+#' `pipeComp:::.aggregateDR` as the corresponding aggregation function.

 #'

 #' @param x The matrix of the reduced space, with cells as rows and components 

 #' as columns

@@ -17,7 +17,9 @@ details)

 }

 \description{

 Evaluates a clustering using 'true' labels. Entries with missing true labels

-(i.e. NA) are excluded from calculations.

+(i.e. NA) are excluded from calculations. If using `evaluteClustering` in a

+custom pipeline, you might want to use the corresponding 

+`pipeComp:::.aggregateClusterEvaluation` aggregation function.

 }

 \examples{

 # random data

@@ -17,7 +17,8 @@ to gather statistics (default `c(10,20,50)`). Will use all available

 dimensions if a higher number is given.}

 \item{covars}{A character vectors containing any additional covariates 

-(column names of `colData`) to track during evalutation.}

+(column names of `colData`) to track during evalutation. If missing, will

+attempt to use default covariates. To disable, set `covars=c()`.}

 }

 \value{

 A list with the following components:

@@ -30,6 +31,8 @@ explained by the clusters (i.e. R-squared of a linear model).

 }

 \description{

 Gathers evaluation statistics on a reduced space using known cell labels.

+If using `evaluteDimRed` in a custom pipeline, you will probably want to use

+`pipeComp:::.aggregateDR` as the corresponding aggregation function.

 }

 \examples{

 # random data