@@ -51,6 +51,6 @@ VignetteBuilder: knitr

 License: MIT

 Encoding: UTF-8

 LazyData: true

-RoxygenNote: 6.1.0

+RoxygenNote: 6.1.0.9000

 BugReports: https://github.com/definitelysean/celda/issues

 biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian

@@ -22,6 +22,7 @@ export(distinct_colors)

 export(factorizeMatrix)

 export(featureModuleLookup)

 export(featureModuleTable)

+export(geneSetEnrich)

 export(logLikelihood)

 export(logLikelihood.celda_C)

 export(logLikelihood.celda_CG)

@@ -13,7 +13,7 @@

 #'  enriched.

 #' @return List of length 'L' where each member contains the significantly

 #'  enriched terms for the corresponding module.

-#' @example

+#' @examples

 #' if (!requireNamespace("BiocManager", quietly = TRUE))

 #'     install.packages("M3DExampleData")

 #' library(M3DExampleData)

new file mode 100644

@@ -0,0 +1,47 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/geneSetEnrich.R

+\name{geneSetEnrich}

+\alias{geneSetEnrich}

+\title{Gene set enrichment}

+\usage{

+geneSetEnrich(counts, celdaModel, databases, fdr = 0.05)

+}

+\arguments{

+\item{counts}{Integer count matrix. Rows represent genes and columns

+represent cells. Row names of the matrix should be gene names.}

+

+\item{celdaModel}{Celda object of class `celda_G` or `celda_CG`.}

+

+\item{databases}{Character vector. Name of reference database. Available

+databases can be viewed by running \code{enrichR::listEnrichrDbs()}.}

+

+\item{fdr}{False discovery rate (FDR). Numeric. Cutoff value for adjusted

+p-value, terms with FDR below this value are considered significantly

+enriched.}

+}

+\value{

+List of length 'L' where each member contains the significantly

+ enriched terms for the corresponding module.

+}

+\description{

+Identify and return significantly-enriched terms for each gene

+ module in a Celda object. Performs gene set enrichment analysis for Celda

+ identified modules using the enrichR package.

+}

+\examples{

+if (!requireNamespace("BiocManager", quietly = TRUE))

+    install.packages("M3DExampleData")

+library(M3DExampleData)

+counts <- M3DExampleData::Mmus_example_list$data

+#subset 100 genes for fast clustering

+counts <- countsMatrix[sample(seq_len(nrow(countsMatrix)),

+    size = 100),]

+#cluster genes into 10 modules for quick demo

+cm <- celda_G(counts = as.matrix(countsMatrix), L = 10, verbose = FALSE)

+gse <- geneSetEnrich(counts,

+    cm,

+    databases = c('GO_Biological_Process_2018','GO_Molecular_Function_2018'))

+}

+\author{

+Ahmed Youssef

+}