@@ -1,18 +1,39 @@

 Package: scone

-Version: 0.0.2

+Version: 0.0.2-9000

 Title: Single Cell Overview of Normalized Expression data

-Description: scone is a package to compare and rank the performance of different normalization

-             schemes in real single-cell RNA-seq datasets.

+Description: scone is a package to compare and rank the performance of different normalization schemes in real single-cell RNA-seq datasets.

 Authors@R: c(person("Michael", "Cole", email = "mbeloc@gmail.com",

 	          role = c("aut", "cre", "cph")),

 	     person("Davide", "Risso", email = "risso.davide@gmail.com",

 	          role = c("aut")))

 Author: Michael Cole [aut, cre, cph] and Davide Risso [aut]

 Maintainer: Michael Cole <mbeloc@gmail.com>

-Depends: R (>= 3.1), BiocParallel

-Imports: DESeq, EDASeq, MASS, RUVSeq, aroma.light, class,

-	 diptest, edgeR, fpc, gplots, limma, matrixStats, mixtools, scde

-Date: 02-14-2016

+Date: 2016-02-14

 License: Artistic-2.0

+Depends:

+  R (>= 3.1)

+Imports:

+  BiocParallel,

+  clusterCells,

+  DESeq,

+  EDASeq,

+  MASS,

+  RUVSeq,

+  aroma.light,

+  class,

+  diptest,

+  edgeR,

+  fpc,

+  gplots,

+  limma,

+  matrixStats,

+  mixtools,

+  scde

+Suggests:

+  knitr,

+  rmarkdown,

+  testthat

+VignetteBuilder: knitr

 LazyLoad: yes

+BugReports: https://github.com/epurdom/clusterExperiment/issues

 RoxygenNote: 5.0.1

@@ -31,7 +31,7 @@ loglik_small(parms, Y, Y1, X, W, kx, kw, offsetx, offsetw, linkobj)

 This function computes the log-likelihood of a standard regression model

 }

 \details{

-This is a (hopefully) memory-efficient implementation of the log-likelihood of a 

+This is a (hopefully) memory-efficient implementation of the log-likelihood of a

 zero-inflated negative binomial regression model.

 In this attempt, the design matrices don't have n*J rows, but n and J, respectively.

 The computation is a bit slower, but the memory usage should be much smaller for

@@ -22,16 +22,16 @@ If NULL, weighted PCA is used for projection}

 \item{weights}{matrix. A numeric data matrix to be used for weighted PCA (genes in rows, cells in columns).

 If NULL, regular PCA is used for projection}

-\item{seed}{numeric. Random seed, passed to bwpca. 

+\item{seed}{numeric. Random seed, passed to bwpca.

 Ignored if is.null(weights) or !is.null(proj).}

-\item{em.maxiter}{numeric. Maximum EM iterations, passed to bwpca. 

+\item{em.maxiter}{numeric. Maximum EM iterations, passed to bwpca.

 Ignored if is.null(weights) or !is.null(proj).}

 \item{eval_knn}{numeric. The number of nearest neighbors to use for evaluation.

 If NULL, all KNN concordances will be returned NA.}

-\item{eval_kclust}{numeric. The number of clusters (> 1) to be used for pam tightness and stability evaluation. 

+\item{eval_kclust}{numeric. The number of clusters (> 1) to be used for pam tightness and stability evaluation.

 If an array of integers, largest average silhoutte width (tightness) / maximum co-clustering compactness (stability) will be reported. If NULL, tightness and stability will be returned NA.}

 \item{bio}{factor. The biological condition (variation to be preserved), NA is allowed.

@@ -54,7 +54,7 @@ If NULL, wv correlations will be returned NA.}

 \item{is_log}{logical. If TRUE the expr matrix is already logged and log transformation will not be carried out.}

-\item{conditional_pam}{logical. If TRUE then maximum ASW is separately computed for each biological condition (including NA), 

+\item{conditional_pam}{logical. If TRUE then maximum ASW is separately computed for each biological condition (including NA),

 and a weighted average is returned.}

 }

 \value{

@@ -71,7 +71,7 @@ A list with the following elements:

 }

 }

 \description{

-This function evaluates an expression matrix using SCONE criteria, producing a number of scores based on 

+This function evaluates an expression matrix using SCONE criteria, producing a number of scores based on

 weighted (or unweighted) projections of the normalized data.

 }

 \details{