@@ -78,6 +78,7 @@ exportMethods(runParams)

 exportMethods(sampleLabel)

 import(Rcpp)

 import(RcppEigen)

+import(foreach)

 import(ggplot2)

 import(graphics)

 import(grid)

@@ -98,7 +99,6 @@ importFrom(digest,digest)

 importFrom(doParallel,registerDoParallel)

 importFrom(enrichR,enrichr)

 importFrom(enrichR,listEnrichrDbs)

-importFrom(foreach,foreach)

 importFrom(ggrepel,geom_text_repel)

 importFrom(grDevices,colorRampPalette)

 importFrom(grDevices,colors)

@@ -46,7 +46,7 @@

 #'     bestOnly = TRUE,

 #'     nchains = 1,

 #'     cores = 2)

-#' @importFrom foreach foreach

+#' @import foreach

 #' @importFrom doParallel registerDoParallel

 #' @importFrom methods is

 #' @export

@@ -10,10 +10,10 @@ vignette: >

   %\VignetteEngine{knitr::rmarkdown}

   %\VignetteEncoding{UTF-8}

 ---

-  

+

 # Introduction

 **CE**llular **L**atent **D**irichlet **A**llocation (celda) is a collection of Bayesian hierarchical models to perform feature and cell bi-clustering for count data generated by single-cell platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications and has shown good performance with sparse data. celda simultaneously clusters features (i.e. gene expression) into modules based on co-expression patterns across cells and cells into subpopulations based on the probabilities of the feature modules within each cell. celda uses Dirichlet-multinomial distributions to model cells and genes so no additional normalization is required for single-cell counts. 

- 

+

 In this vignette we will demonstrate how to use celda to perform cell and feature clustering with simulated data.

 # Installation