@@ -1,2 +1,3 @@

 ^.*\.Rproj$

 ^\.Rproj\.user$

+^CONDUCT\.md$

new file mode 100644

@@ -0,0 +1,25 @@

+# Contributor Code of Conduct

+

+As contributors and maintainers of this project, we pledge to respect all people who 

+contribute through reporting issues, posting feature requests, updating documentation,

+submitting pull requests or patches, and other activities.

+

+We are committed to making participation in this project a harassment-free experience for

+everyone, regardless of level of experience, gender, gender identity and expression,

+sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.

+

+Examples of unacceptable behavior by participants include the use of sexual language or

+imagery, derogatory comments or personal attacks, trolling, public or private harassment,

+insults, or other unprofessional conduct.

+

+Project maintainers have the right and responsibility to remove, edit, or reject comments,

+commits, code, wiki edits, issues, and other contributions that are not aligned to this 

+Code of Conduct. Project maintainers who do not follow the Code of Conduct may be removed 

+from the project team.

+

+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by 

+opening an issue or contacting one or more of the project maintainers.

+

+This Code of Conduct is adapted from the Contributor Covenant 

+(http:contributor-covenant.org), version 1.0.0, available at 

+http://contributor-covenant.org/version/1/0/0/

@@ -22,7 +22,8 @@ Imports:

     graphics,

     Rmpfr,

     gplots,

-    doParallel

+    doParallel,

+    cluster

 Suggests:

     testthat,

     knitr,

@@ -53,11 +53,11 @@ export(simulateCells.celda_CG)

 export(simulateCells.celda_G)

 export(topRank)

 export(visualize_model_performance)

+import(RColorBrewer)

 import(Rmpfr)

-import(foreach)  

-import(gtable)

+import(foreach)

+import(grDevices)

+import(graphics)

 import(grid)

+import(gtable)

 import(scales)

-import(RColorBrewer)

-import(grDevices)

-  # require(graphics)

@@ -1,3 +1,4 @@

+#' available models

 #' @export

 available_models = c("celda_C", "celda_G", "celda_CG")

@@ -8,11 +9,10 @@ available_models = c("celda_C", "celda_G", "celda_CG")

 #' @param model Which celda sub-model to run. Options include "celda_C" (cell clustering), "celda_G" (gene clustering), "celda_CG" (gene and cell clustering)

 #' @param sample.label A numeric vector indicating the sample for each cell (column) in the count matrix

 #' @param nchains The number of chains of Gibbs sampling to run for every combination of K/L parameters

-#' @param K An integer or range of integers indicating the desired number of cell clusters (for celda_C / celda_CG models)

-#' @param L An integer or range of integers indicating the desired number of gene clusters (for celda_G / celda_CG models)

 #' @param cores The number of cores to use to speed up Gibbs sampling

 #' @param seed The base seed for random number generation. Each chain celda runs with have a seed index off of this one.

 #' @param verbose Print messages during celda chain execution

+#' @param ... extra parameters passed onto expand.grid 

 #' @return Object of class "celda_list", which contains results for all model parameter combinations and summaries of the run parameters

 #' @import foreach

 #' @export

@@ -49,6 +49,12 @@ celda = function(counts, model, sample.label=NULL, nchains=1, cores=1, seed=1234

 #' Sanity check arguments to celda() to ensure a smooth run.

+#' @param counts A count matrix 

+#' @param model ...

+#' @param sample.label ...

+#' @param nchains ...

+#' @param cores ...

+#' @param seed ...

 validate_args = function(counts, model, sample.label, nchains, cores, seed) {

   validate_counts(counts)

@@ -67,6 +73,7 @@ validate_args = function(counts, model, sample.label, nchains, cores, seed) {

 #' Perform some simple checks on the counts matrix, to ensure celda won't choke.

+#' @param counts A count matrix

 validate_counts = function(counts) {

   # counts has to be a matrix...

   if (class(counts) != "matrix") stop("counts argument must be of class 'matrix'")

@@ -81,6 +81,7 @@ simulateCells.celda_C = function(S=10, C.Range=c(10, 100), N.Range=c(100,5000),

 #' @param thread The thread index, used for logging purposes

 #' @param save.history Logical; whether to return the history of cluster assignments. Defaults to FALSE

 #' @param save.prob Logical; whether to return the history of cluster assignment probabilities. Defaults to FALSE

+#' @param ... extra parameters passed onto the celda_C 

 #' @export

 celda_C = function(counts, sample.label=NULL, K, alpha=1, beta=1, max.iter=25, 

                    seed=12345, best=TRUE, z.split.on.iter=3, z.num.splits=3, 

@@ -371,6 +372,7 @@ getL.celda_C = function(celda.mod) { return(NA) }

 #' celda_heatmap for celda Cell clustering function 

 #' @param celda.mod A celda model object of class "celda_C"

 #' @param counts A numeric count matrix

+#' @param ... extra parameters passed onto the render_celda_heatmap

 #' @export

 celda_heatmap.celda_C = function(celda.mod, counts, ...) {

   render_celda_heatmap(counts, z=celda.mod$z, ...)

@@ -403,5 +405,5 @@ visualize_model_performance.celda_C = function(celda.list, method="perplexity",

   plot.df = data.frame(size=cluster.sizes,

                        metric=performance.metric)

-  return(celda::render_model_performance_plot(plot.df, "K", method, title))

+  return(render_model_performance_plot(plot.df, "K", method, title))

 }

@@ -199,6 +199,7 @@ cCG.calcGibbsProbY = function(n.CP.by.TS, n.by.TS, nG.by.TS, nG.in.Y, beta, delt

 #' @param gamma The Dirichlet distribution parameter for Psi; adds a pseudocount to each gene within each transcriptional state.

 #' @param delta The Dirichlet distribution parameter for Eta; adds a gene pseudocount to the numbers of genes each state.

 #' @param seed starting point used for generating simulated data.

+#' @param ... extra parameters passed onto the simulateCells.celda_CG

 #' @export

 simulateCells.celda_CG = function(S=10, C.Range=c(50,100), N.Range=c(500,5000), 

                                   G=1000, K=3, L=10, alpha=1, beta=1, gamma=1, 

@@ -272,12 +273,13 @@ simulateCells.celda_CG = function(S=10, C.Range=c(50,100), N.Range=c(500,5000),

 #' @param seed Parameter to set.seed() for random number generation

 #' @param best Whether to return the cluster assignment with the highest log-likelihood. Defaults to TRUE. Returns last generated cluster assignment when FALSE.

 #' @param z.split.on.iter On z.split.on.iter-th iterations, a heuristic will be applied using hierarchical clustering to determine if a cell cluster should be merged with another cell cluster and a third cell cluster should be split into two clusters. This helps avoid local optimum during the initialization. Default to be 3. 

-#' @param z.num.split Maximum number of times to perform the heuristic described in z.split.on.iter.

+#' @param z.num.splits Maximum number of times to perform the heuristic described in z.split.on.iter.

 #' @param y.split.on.iter  On every y.split.on.iter iteration, a heuristic will be applied using hierarchical clustering to determine if a gene cluster should be merged with another gene cluster and a third gene cluster should be split into two clusters. This helps avoid local optimum during the initialization. Default to be 3. 

 #' @param y.num.splits Maximum number of times to perform the heuristic described in y.split.on.iter.

 #' @param thread The thread index, used for logging purposes.

 #' @param save.history Logical; whether to return the history of cluster assignments. Defaults to FALSE.

 #' @param save.prob Logical; whether to return the history of cluster assignment probabilities. Defaults to FALSE.

+#' @param ... extra parameters passed onto celda_CG

 #' @export

 celda_CG = function(counts, sample.label=NULL, K, L, alpha=1, beta=1, delta=1, gamma=1,

 			max.iter=25, seed=12345, best=TRUE, z.split.on.iter=3, z.num.splits=3,

@@ -623,6 +625,8 @@ getL.celda_CG = function(celda.mod) {

 #' celda_heatmap for celda Cell and Gene clustering function 

 #' @param celda.mod A celda model object of "Celda_CG"

+#' @param counts A count matrix

+#' @param ... extra parameters passed onto render_celda_heatmap

 #' @export

 celda_heatmap.celda_CG = function(celda.mod, counts, ...) {

   render_celda_heatmap(counts, z=celda.mod$z, y=celda.mod$y, ...)

@@ -630,7 +634,9 @@ celda_heatmap.celda_CG = function(celda.mod, counts, ...) {

 #' visualize_model_performance for Celda Cell and Gene clustering function 

-#' @param celda.mod A celda model object of "Celda_CG"

+#' @param celda.list A celda model object of "Celda_CG"

+#' @param title Title for the visualize_model_performance

+#' @param method One of “perplexity”, “harmonic”, or “loglik”

 #' @export

 #' @import Rmpfr

 visualize_model_performance.celda_CG = function(celda.list, method="perplexity", 

@@ -130,14 +130,15 @@ cG.calcLL = function(n.TS.by.C, n.by.TS, n.by.G, nG.by.TS, nM, nG, L, beta, delt

 #' This function calculates the log-likelihood of a given set of cluster assigments for the samples

 #' represented in the provided count matrix.

 #' 

-#' @param ix The index of the cell being assigned a cluster during the current iteration of Gibbs sampling

-#' @param counts A numeric count matrix

-#' @param z A numeric vector of cluster assignments

-#' @param k The number of clusters being considered

-#' @param alpha Vector of non-zero concentration parameters for sample <-> cluster assignment Dirichlet distribution

+#' 

+#' @param n.TS.by.C Number of counts in each Transcriptional State per Cell 

+#' @param n.by.TS Number of counts per Transcriptional State

+#' @param nG.by.TS Number of genes in each Transcriptional State

+#' @param nG.in.Y  Number of genes in each of the cell cluster

+#' @param gamma The Dirichlet distribution parameter for Psi; adds a pseudocount to each gene within each transcriptional state.

+#' @param delta The Dirichlet distribution parameter for Eta; adds a gene pseudocount to the numbers of genes each state.

 #' @param beta Vector of non-zero concentration parameters for cluster <-> gene assignment Dirichlet distribution

 #' @keywords log likelihood

-#' @examples TODO

 cG.calcGibbsProbY = function(n.TS.by.C, n.by.TS, nG.by.TS, nG.in.Y, beta, delta, gamma) {

   ## Calculate for "Eta" component

@@ -174,20 +175,20 @@ cG.calcGibbsProbY = function(n.TS.by.C, n.by.TS, nG.by.TS, nG.in.Y, beta, delta,

 #' sequencing count matrix, using the celda Bayesian hierarchical model.

 #' 

 #' @param counts A numeric count matrix

-#' @param k The number of clusters to generate

-#' @param a Vector of non-zero concentration parameters for sample <-> cluster assignment Dirichlet distribution

-#' @param b Vector of non-zero concentration parameters for cluster <-> gene assignment Dirichlet distribution

-#' @param g Number of cell states ("topics")

+#' @param L The number of clusters to generate

+#' @param beta The Dirichlet distribution parameter for Phi; adds a pseudocount to each transcriptional state within each cell.

+#' @param delta The Dirichlet distribution parameter for Eta; adds a gene pseudocount to the numbers of genes each state. Default to 1.

+#' @param gamma The Dirichlet distribution parameter for Psi; adds a pseudocount to each gene within each transcriptional state.

 #' @param max.iter Maximum iterations of Gibbs sampling to perform. Defaults to 25.

-#' @param min.cell Desired minimum number of cells per cluster

-#' @param seed Parameter to set.seed() for random number generation

-#' @param best Whether to return the cluster assignment with the highest log-likelihood. Defaults to TRUE. Returns last generated cluster assignment when FALSE.

-#' @param kick Whether to randomize cluster assignments when a cluster has fewer than min.cell cells assigned to it during Gibbs sampling. (TODO param currently unused?)

+#' @param y.split.on.iter  On every y.split.on.iter iteration, a heuristic will be applied using hierarchical clustering to determine if a gene cluster should be merged with another gene cluster and a third gene cluster should be split into two clusters. This helps avoid local optimum during the initialization. Default to be 3. 

+#' @param y.num.splits Maximum number of times to perform the heuristic described in y.split.on.iter.

+#' @param seed Parameter to set.seed() for random number generation.

+#' @param best Whether to return the cluster assignment with the highest log-likelihood. Defaults to TRUE. Returns last generated cluster assignment when FALSE. Default to be TRUE. 

 #' @param save.history Logical; whether to return the history of cluster assignments. Defaults to FALSE

 #' @param save.prob Logical; whether to return the history of cluster assignment probabilities. Defaults to FALSE

 #' @param thread The thread index, used for logging purposes

+#' @param ...  extra parameters passed onto celda_G

 #' @keywords LDA gene clustering gibbs

-#' @examples TODO

 #' @export

 celda_G = function(counts, L, beta=1, delta=1, gamma=1, max.iter=25,

                    seed=12345, best=TRUE, y.split.on.iter=3, 

@@ -325,8 +326,8 @@ celda_G = function(counts, L, beta=1, delta=1, gamma=1, max.iter=25,

 #' @param G The number of genes for which to simulate counts

 #' @param beta The Dirichlet distribution parameter for Phi; adds a pseudocount to each transcriptional state within each cell

 #' @param delta The Dirichlet distribution parameter for Psi; adds a pseudocount to each gene within each transcriptional state

+#' @param gamma The Dirichlet distribution parameter for Psi; adds a pseudocount to each gene within each transcriptional state.

 #' @param seed Parameter to set.seed() for random number generation

-#' @examples TODO

 #' @export

 simulateCells.celda_G = function(C=100, N.Range=c(500,5000),  G=1000, 

                                          L=5, beta=1, gamma=1, delta=1, seed=12345) {

@@ -464,6 +465,7 @@ getL.celda_G = function(celda.mod) {

 #' celda_heatmap for celda Gene clustering function 

 #' @param celda.mod A celda model object of class "celda_G"

 #' @param counts A numeric count matrix

+#' @param ... extra parameters passed onto render_celda_heatmap

 #' @export

 celda_heatmap.celda_G = function(celda.mod, counts, ...) {

   render_celda_heatmap(counts, y=celda.mod$y, ...)

@@ -497,5 +499,5 @@ visualize_model_performance.celda_G = function(celda.list, method="perplexity",

   plot.df = data.frame(size=cluster.sizes,

                        metric=performance.metric)

-  return(celda::render_model_performance_plot(plot.df, "L", method, title))

+  return(render_model_performance_plot(plot.df, "L", method, title))

 }

@@ -39,6 +39,9 @@ normalizeLogProbs = function(ll.probs) {

 }

+#' normalizeCounts function 

+#' @param counts A count matrix 

+#' @param scale.factor the scalor for the normalization 

 #' @export

 normalizeCounts = function(counts, scale.factor=1e6) {

   counts.norm = sweep(counts, 2, colSums(counts) / scale.factor, "/")

@@ -79,7 +79,7 @@ robust_scale <- function(x){

 #' @param scale.row Logical; psecifying if the z-score transformation is performed to the counts matrix. Defualt to be TRUE. 

 #' @param z.trim two element vector to specify the lower and upper cutoff of the z-score normalization result by default it is set to NULL so no trimming will be done. Default to be (-2,2)

 #' @param normalize specify the normalization type: "cpm" or "none". Defualt to be "none". 

-#' @param scale_fun specify the function for scaling. Defualt to be scale. 

+#' @param scale_function specify the function for scaling. Defualt to be scale. 

 #' @param cluster.row Logical; determining if rows should be clustered.

 #' @param cluster.column Logical; determining if columns should be clustered.

 #' @param annotation_cell a dataframe for the cell annotations (columns).

@@ -95,6 +95,12 @@ robust_scale <- function(x){

 #' @param annotation_names_cell Logical; showing if the names for cell annotation tracks should be drawn. Default to be TRUE. 

 #' @param show_genenames Logical; specifying if gene names should be shown. Default to be FALSE. 

 #' @param show_cellnames Logical; specifying if cell names should be shown. Default to be FALSE. 

+#' @import gtable

+#' @import grid

+#' @import scales

+#' @import RColorBrewer

+#' @import grDevices

+#' @import graphics

 #' @export 

 render_celda_heatmap <- function(counts, z=NULL, y=NULL, 

                                  scale.log=FALSE, scale.row=TRUE,

@@ -110,13 +116,6 @@ render_celda_heatmap <- function(counts, z=NULL, y=NULL,

                                  annotation_names_cell = TRUE,

                                  show_genenames = FALSE, 

                                  show_cellnames = FALSE) {

-  require(gtable)

-  require(grid)

-  require(scales)

-  require(stats)

-  require(RColorBrewer)

-  require(grDevices)

-  require(graphics)

   if(normalize =="cpm"){

@@ -220,13 +219,13 @@ render_celda_heatmap <- function(counts, z=NULL, y=NULL,

     }

   }else{  # only one side for the counts values (eihter positive or negative )

     if(is.null(col)){

-      col <- colorRampPalette(c("#FFFFFF", RColorBrewer::brewer.pal(n = 9, name = "Reds")))(100)

+      col <- colorRampPalette(c("#FFFFFF", brewer.pal(n = 9, name = "Reds")))(100)

     }

   }

   if(cluster.row & cluster.column){

-    celda::semi_pheatmap(mat = counts, 

+           semi_pheatmap(mat = counts, 

                          color = col, 

                          breaks = breaks, 

                          cutree_rows = L,

@@ -246,7 +245,7 @@ render_celda_heatmap <- function(counts, z=NULL, y=NULL,

   }

   if(cluster.row & (!cluster.column)){

-    celda::semi_pheatmap(mat = counts, 

+          semi_pheatmap(mat = counts, 

                          color = col,

                          breaks = breaks, 

                          cutree_rows = L,

@@ -266,7 +265,7 @@ render_celda_heatmap <- function(counts, z=NULL, y=NULL,

     if((!cluster.row) & cluster.column){

-      celda::semi_pheatmap(mat = counts, 

+            semi_pheatmap(mat = counts, 

                            color = col,

                            breaks = breaks, 

                            cluster_rows = FALSE,

@@ -285,7 +284,7 @@ render_celda_heatmap <- function(counts, z=NULL, y=NULL,

       }

     if((!cluster.row) & (!cluster.column) ){

-      celda::semi_pheatmap(mat = counts,

+            semi_pheatmap(mat = counts,

                            color = col,

                            breaks = breaks, 

                            cluster_rows = FALSE,

@@ -1,3 +1,9 @@

+#' TopRank function 

+#' @param fm factorized matrix.

+#' @param n Maximum number of items returned for each entry. 

+#' @param margin 1 for rows, 2 for columns.

+#' @param threshold only include entries in the matrix that is greader than the threshold.

+#' @param decreasing Logical; specifying if rank should be decreasing. Default to be TRUE. 

 #' @export

 topRank = function(fm, n=25, margin=2, threshold=0, decreasing=TRUE) {

   if(is.null(threshold) | is.na(threshold)) {

@@ -21,7 +21,7 @@

 #' Get run parameters for a celda run.

 #'

-#' @param celda.res A celda_list object, as returned from celda()

+#' @param celda.list A celda_list object, as returned from celda()

 #' @export

 runParams = function(celda.list) {

   return(celda.list$run.params)

@@ -107,14 +107,7 @@ getL = function(celda.mod) {

 #'

 #' @param celda.mod A celda model object (of class "celda_C", "celda_G" or "celda_CG")

 #' @param counts the counts matrix 

-#' @param z A numeric vector of cluster assignments for cell. Resolved automatically from celda object when available.

-#' @param y A numeric vector of cluster assignments for gene. Resolved automatically from celda object when available.

-#' @param scale.log specify the transformation type of the matrix for (semi-)heatmap, can be "log","row"(z-acore by row),"col"(z-score by column), etc. #To be completed

-#' @param scale.row specify the transformation type of the matrix for (semi-)heatmap, can be "log","row"(z-acore by row),"col"(z-score by column), etc. #To be completed

-#' @param z.trim two element vector to specify the lower and upper cutoff of the z-score normalization result by default it is set to NULL so no trimming will be done.

-#' @param scale_fun specify the function for scaling 

-#' @param cluster.row boolean values determining if rows should be clustered

-#' @param cluster.column boolean values determining if columns should be clustered

+#' @param ... extra parameters passed onto celda_heatmap

 #' @export 

 celda_heatmap <- function(celda.mod, counts, ...) {

   UseMethod("celda_heatmap", celda.mod)

@@ -124,10 +117,11 @@ celda_heatmap <- function(celda.mod, counts, ...) {

 #' Visualize various performance metrics as a function of K / L to aid parameter choice.

 #' 

 #' @param celda.list A celda_list object as returned from *celda()*

-#' @param metric Which performance metric to visualize. One of ("perplexity", "harmonic", "loglik"). "perplexity" calculates the inverse of the geometric mean of the log likelihoods from each iteration of Gibbs sampling. "harmonic" calculates the marginal likelihood has the harmonic mean of the likelihoods. "loglik" plots the highest log-likelihood during Gibbs iteration.

+#' @param method Which performance metric to visualize. One of ("perplexity", "harmonic", "loglik"). "perplexity" calculates the inverse of the geometric mean of the log likelihoods from each iteration of Gibbs sampling. "harmonic" calculates the marginal likelihood has the harmonic mean of the likelihoods. "loglik" plots the highest log-likelihood during Gibbs iteration.

+#' @param title Title for the visualize_model_performance

 #' @return A ggplot object containing the requested plot(s)

 #' @export

-visualize_model_performance <- function(celda.list, method, ...) {

+visualize_model_performance <- function(celda.list, method, title) {

   # Dispatch on the list's content type

   UseMethod("visualize_model_performance", celda.list$content.type)

 }

@@ -855,6 +855,8 @@ identity2 = function(x, ...){

 #' @param width manual option for determining the output file width in inches.

 #' @param height manual option for determining the output file height in inches.

 #' @param silent do not draw the plot (useful when using the gtable output)

+#' @param row_label row cluster labels for semi-clustering 

+#' @param col_label column cluster labels for semi-clustering 

 #' @param \dots graphical parameters for the text used in plot. Parameters passed to 

 #' \code{\link{grid.text}}, see \code{\link{gpar}}. 

 #' 

@@ -868,7 +870,7 @@ identity2 = function(x, ...){

 #' }

 #' 

 #' @author  Raivo Kolde <rkolde@@gmail.com>

-#' @examples

+#' #@examples

 #' # Create test matrix

 #' test = matrix(rnorm(200), 20, 10)

 #' test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3

@@ -951,7 +953,7 @@ identity2 = function(x, ...){

 #' 

 #' pheatmap(test, clustering_callback = callback)

 #' 

-#' \dontrun{

+#' dontrun{

 #' # Same using dendsort package

 #' library(dendsort)

 #' 

new file mode 100644

@@ -0,0 +1,14 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/celda.R

+\docType{data}

+\name{available_models}

+\alias{available_models}

+\title{available models}

+\format{An object of class \code{character} of length 3.}

+\usage{

+available_models

+}

+\description{

+available models

+}

+\keyword{datasets}

@@ -7,24 +7,23 @@

 cG.calcGibbsProbY(n.TS.by.C, n.by.TS, nG.by.TS, nG.in.Y, beta, delta, gamma)

 }

 \arguments{

-\item{beta}{Vector of non-zero concentration parameters for cluster <-> gene assignment Dirichlet distribution}

+\item{n.TS.by.C}{Number of counts in each Transcriptional State per Cell}

+

+\item{n.by.TS}{Number of counts per Transcriptional State}

-\item{ix}{The index of the cell being assigned a cluster during the current iteration of Gibbs sampling}

+\item{nG.by.TS}{Number of genes in each Transcriptional State}

-\item{counts}{A numeric count matrix}

+\item{nG.in.Y}{Number of genes in each of the cell cluster}

-\item{z}{A numeric vector of cluster assignments}

+\item{beta}{Vector of non-zero concentration parameters for cluster <-> gene assignment Dirichlet distribution}

-\item{k}{The number of clusters being considered}

+\item{delta}{The Dirichlet distribution parameter for Eta; adds a gene pseudocount to the numbers of genes each state.}

-\item{alpha}{Vector of non-zero concentration parameters for sample <-> cluster assignment Dirichlet distribution}

+\item{gamma}{The Dirichlet distribution parameter for Psi; adds a pseudocount to each gene within each transcriptional state.}

 }

 \description{

 This function calculates the log-likelihood of a given set of cluster assigments for the samples

 represented in the provided count matrix.

 }

-\examples{

-TODO

-}

 \keyword{likelihood}

 \keyword{log}

@@ -22,9 +22,7 @@ celda(counts, model, sample.label = NULL, nchains = 1, cores = 1,

 \item{verbose}{Print messages during celda chain execution}

-\item{K}{An integer or range of integers indicating the desired number of cell clusters (for celda_C / celda_CG models)}

-

-\item{L}{An integer or range of integers indicating the desired number of gene clusters (for celda_G / celda_CG models)}

+\item{...}{extra parameters passed onto expand.grid}

 }

 \value{

 Object of class "celda_list", which contains results for all model parameter combinations and summaries of the run parameters

@@ -35,6 +35,8 @@ celda_C(counts, sample.label = NULL, K, alpha = 1, beta = 1,

 \item{save.history}{Logical; whether to return the history of cluster assignments. Defaults to FALSE}

 \item{save.prob}{Logical; whether to return the history of cluster assignment probabilities. Defaults to FALSE}

+

+\item{...}{extra parameters passed onto the celda_C}

 }

 \description{

 celda Cell clustering function

@@ -35,6 +35,8 @@ celda_CG(counts, sample.label = NULL, K, L, alpha = 1, beta = 1,

 \item{z.split.on.iter}{On z.split.on.iter-th iterations, a heuristic will be applied using hierarchical clustering to determine if a cell cluster should be merged with another cell cluster and a third cell cluster should be split into two clusters. This helps avoid local optimum during the initialization. Default to be 3.}

+\item{z.num.splits}{Maximum number of times to perform the heuristic described in z.split.on.iter.}

+

 \item{y.split.on.iter}{On every y.split.on.iter iteration, a heuristic will be applied using hierarchical clustering to determine if a gene cluster should be merged with another gene cluster and a third gene cluster should be split into two clusters. This helps avoid local optimum during the initialization. Default to be 3.}

 \item{y.num.splits}{Maximum number of times to perform the heuristic described in y.split.on.iter.}

@@ -45,7 +47,7 @@ celda_CG(counts, sample.label = NULL, K, L, alpha = 1, beta = 1,

 \item{save.prob}{Logical; whether to return the history of cluster assignment probabilities. Defaults to FALSE.}

-\item{z.num.split}{Maximum number of times to perform the heuristic described in z.split.on.iter.}

+\item{...}{extra parameters passed onto celda_CG}

 }

 \description{

 celda Cell and Gene clustering function

@@ -11,37 +11,36 @@ celda_G(counts, L, beta = 1, delta = 1, gamma = 1, max.iter = 25,

 \arguments{

 \item{counts}{A numeric count matrix}

-\item{max.iter}{Maximum iterations of Gibbs sampling to perform. Defaults to 25.}

+\item{L}{The number of clusters to generate}

-\item{seed}{Parameter to set.seed() for random number generation}

+\item{beta}{The Dirichlet distribution parameter for Phi; adds a pseudocount to each transcriptional state within each cell.}

-\item{best}{Whether to return the cluster assignment with the highest log-likelihood. Defaults to TRUE. Returns last generated cluster assignment when FALSE.}

+\item{delta}{The Dirichlet distribution parameter for Eta; adds a gene pseudocount to the numbers of genes each state. Default to 1.}

-\item{thread}{The thread index, used for logging purposes}

+\item{gamma}{The Dirichlet distribution parameter for Psi; adds a pseudocount to each gene within each transcriptional state.}

-\item{save.prob}{Logical; whether to return the history of cluster assignment probabilities. Defaults to FALSE}

+\item{max.iter}{Maximum iterations of Gibbs sampling to perform. Defaults to 25.}

-\item{save.history}{Logical; whether to return the history of cluster assignments. Defaults to FALSE}

+\item{seed}{Parameter to set.seed() for random number generation.}

-\item{k}{The number of clusters to generate}

+\item{best}{Whether to return the cluster assignment with the highest log-likelihood. Defaults to TRUE. Returns last generated cluster assignment when FALSE. Default to be TRUE.}

-\item{a}{Vector of non-zero concentration parameters for sample <-> cluster assignment Dirichlet distribution}

+\item{y.split.on.iter}{On every y.split.on.iter iteration, a heuristic will be applied using hierarchical clustering to determine if a gene cluster should be merged with another gene cluster and a third gene cluster should be split into two clusters. This helps avoid local optimum during the initialization. Default to be 3.}

-\item{b}{Vector of non-zero concentration parameters for cluster <-> gene assignment Dirichlet distribution}

+\item{y.num.splits}{Maximum number of times to perform the heuristic described in y.split.on.iter.}

-\item{g}{Number of cell states ("topics")}

+\item{thread}{The thread index, used for logging purposes}

-\item{min.cell}{Desired minimum number of cells per cluster}

+\item{save.prob}{Logical; whether to return the history of cluster assignment probabilities. Defaults to FALSE}

-\item{kick}{Whether to randomize cluster assignments when a cluster has fewer than min.cell cells assigned to it during Gibbs sampling. (TODO param currently unused?)}

+\item{save.history}{Logical; whether to return the history of cluster assignments. Defaults to FALSE}

+

+\item{...}{extra parameters passed onto celda_G}

 }

 \description{

 geneCluster provides cluster assignments for all genes in a provided single-cell 

 sequencing count matrix, using the celda Bayesian hierarchical model.

 }

-\examples{

-TODO

-}

 \keyword{LDA}

 \keyword{clustering}

 \keyword{gene}

@@ -11,21 +11,7 @@ celda_heatmap(celda.mod, counts, ...)

 \item{counts}{the counts matrix}

-\item{z}{A numeric vector of cluster assignments for cell. Resolved automatically from celda object when available.}

-

-\item{y}{A numeric vector of cluster assignments for gene. Resolved automatically from celda object when available.}

-

-\item{scale.log}{specify the transformation type of the matrix for (semi-)heatmap, can be "log","row"(z-acore by row),"col"(z-score by column), etc. #To be completed}

-

-\item{scale.row}{specify the transformation type of the matrix for (semi-)heatmap, can be "log","row"(z-acore by row),"col"(z-score by column), etc. #To be completed}

-

-\item{z.trim}{two element vector to specify the lower and upper cutoff of the z-score normalization result by default it is set to NULL so no trimming will be done.}

-

-\item{scale_fun}{specify the function for scaling}

-

-\item{cluster.row}{boolean values determining if rows should be clustered}

-

-\item{cluster.column}{boolean values determining if columns should be clustered}

+\item{...}{extra parameters passed onto celda_heatmap}

 }

 \description{

 Render a stylable heatmap of count data based on celda clustering results.

@@ -10,6 +10,8 @@

 \item{celda.mod}{A celda model object of class "celda_C"}

 \item{counts}{A numeric count matrix}

+

+\item{...}{extra parameters passed onto the render_celda_heatmap}

 }

 \description{

 celda_heatmap for celda Cell clustering function

@@ -8,6 +8,10 @@

 }

 \arguments{

 \item{celda.mod}{A celda model object of "Celda_CG"}

+

+\item{counts}{A count matrix}

+

+\item{...}{extra parameters passed onto render_celda_heatmap}

 }

 \description{

 celda_heatmap for celda Cell and Gene clustering function

@@ -10,6 +10,8 @@

 \item{celda.mod}{A celda model object of class "celda_G"}

 \item{counts}{A numeric count matrix}

+

+\item{...}{extra parameters passed onto render_celda_heatmap}

 }

 \description{

 celda_heatmap for celda Gene clustering function

new file mode 100644

@@ -0,0 +1,16 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/celda_functions.R

+\name{normalizeCounts}

+\alias{normalizeCounts}

+\title{normalizeCounts function}

+\usage{

+normalizeCounts(counts, scale.factor = 1e+06)

+}

+\arguments{

+\item{counts}{A count matrix}

+

+\item{scale.factor}{the scalor for the normalization}

+}

+\description{

+normalizeCounts function

+}

@@ -23,6 +23,8 @@ render_celda_heatmap(counts, z = NULL, y = NULL, scale.log = FALSE,

 \item{scale.row}{Logical; psecifying if the z-score transformation is performed to the counts matrix. Defualt to be TRUE.}

+\item{scale_function}{specify the function for scaling. Defualt to be scale.}

+

 \item{normalize}{specify the normalization type: "cpm" or "none". Defualt to be "none".}

 \item{z.trim}{two element vector to specify the lower and upper cutoff of the z-score normalization result by default it is set to NULL so no trimming will be done. Default to be (-2,2)}

@@ -53,8 +55,6 @@ breaks are calculated automatically.}

 \item{show_genenames}{Logical; specifying if gene names should be shown. Default to be FALSE.}

 \item{show_cellnames}{Logical; specifying if cell names should be shown. Default to be FALSE.}

-

-\item{scale_fun}{specify the function for scaling. Defualt to be scale.}

 }

 \description{

 render function for celda heatmap plot

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

 runParams(celda.list)

 }

 \arguments{

-\item{celda.res}{A celda_list object, as returned from celda()}

+\item{celda.list}{A celda_list object, as returned from celda()}

 }

 \description{

 Get run parameters for a celda run.

@@ -157,6 +157,10 @@ calculated so that the plot would fit there, unless specified otherwise.}

 \item{silent}{do not draw the plot (useful when using the gtable output)}

+\item{row_label}{row cluster labels for semi-clustering}

+

+\item{col_label}{column cluster labels for semi-clustering}

+

 \item{\dots}{graphical parameters for the text used in plot. Parameters passed to 

 \code{\link{grid.text}}, see \code{\link{gpar}}.}

 }

@@ -180,7 +184,9 @@ clustering anymore, roughly more than 1000. Instead of showing all the rows

 separately one can cluster the rows in advance and show only the cluster centers. 

 The number of clusters can be tuned with parameter kmeans_k.

 }

-\examples{

+\author{

+Raivo Kolde <rkolde@gmail.com>

+#@examples

 # Create test matrix

 test = matrix(rnorm(200), 20, 10)

 test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3

@@ -199,7 +205,7 @@ pheatmap(test, legend = FALSE)

 # Show text within cells

 pheatmap(test, display_numbers = TRUE)

-pheatmap(test, display_numbers = TRUE, number_format = "\\\%.1e")

+pheatmap(test, display_numbers = TRUE, number_format = "\%.1e")

 pheatmap(test, display_numbers = matrix(ifelse(test > 5, "*", ""), nrow(test)))

 pheatmap(test, cluster_row = FALSE, legend_breaks = -1:4, legend_labels = c("0",

 "1e-4", "1e-3", "1e-2", "1e-1", "1"))

@@ -263,15 +269,11 @@ callback = function(hc, mat){

 pheatmap(test, clustering_callback = callback)

-\dontrun{

+dontrun{

 # Same using dendsort package

 library(dendsort)

 callback = function(hc, ...){dendsort(hc)}

 pheatmap(test, clustering_callback = callback)

 }

-

-}

-\author{

-Raivo Kolde <rkolde@gmail.com>

 }

@@ -30,6 +30,8 @@ simulateCells.celda_CG(S = 10, C.Range = c(50, 100), N.Range = c(500,

 \item{delta}{The Dirichlet distribution parameter for Eta; adds a gene pseudocount to the numbers of genes each state.}

 \item{seed}{starting point used for generating simulated data.}

+

+\item{...}{extra parameters passed onto the simulateCells.celda_CG}

 }

 \description{

 simulateCells for the celda Cell and Gene clustering function

@@ -18,6 +18,8 @@ simulateCells.celda_G(C = 100, N.Range = c(500, 5000), G = 1000, L = 5,

 \item{beta}{The Dirichlet distribution parameter for Phi; adds a pseudocount to each transcriptional state within each cell}

+\item{gamma}{The Dirichlet distribution parameter for Psi; adds a pseudocount to each gene within each transcriptional state.}

+

 \item{delta}{The Dirichlet distribution parameter for Psi; adds a pseudocount to each gene within each transcriptional state}

 \item{seed}{Parameter to set.seed() for random number generation}

@@ -26,6 +28,3 @@ simulateCells.celda_G(C = 100, N.Range = c(500, 5000), G = 1000, L = 5,

 Generate a simulated count matrix, based off a generative distribution whose 

 parameters can be provided by the user.

 }

-\examples{

-TODO

-}

new file mode 100644

@@ -0,0 +1,22 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/feature_selection.R

+\name{topRank}

+\alias{topRank}

+\title{TopRank function}

+\usage{

+topRank(fm, n = 25, margin = 2, threshold = 0, decreasing = TRUE)

+}

+\arguments{

+\item{fm}{factorized matrix.}

+

+\item{n}{Maximum number of items returned for each entry.}

+

+\item{margin}{1 for rows, 2 for columns.}

+

+\item{threshold}{only include entries in the matrix that is greader than the threshold.}

+

+\item{decreasing}{Logical; specifying if rank should be decreasing. Default to be TRUE.}

+}

+\description{

+TopRank function

+}

@@ -6,6 +6,19 @@

 \usage{

 validate_args(counts, model, sample.label, nchains, cores, seed)

 }

+\arguments{

+\item{counts}{A count matrix}

+

+\item{model}{...}

+

+\item{sample.label}{...}

+

+\item{nchains}{...}

+

+\item{cores}{...}

+

+\item{seed}{...}

+}

 \description{

 Sanity check arguments to celda() to ensure a smooth run.

 }

@@ -6,6 +6,9 @@

 \usage{

 validate_counts(counts)

 }

+\arguments{

+\item{counts}{A count matrix}

+}

 \description{

 Perform some simple checks on the counts matrix, to ensure celda won't choke.

 }

@@ -4,12 +4,14 @@

 \alias{visualize_model_performance}

 \title{Visualize various performance metrics as a function of K / L to aid parameter choice.}

 \usage{

-visualize_model_performance(celda.list, method, ...)

+visualize_model_performance(celda.list, method, title)

 }

 \arguments{

 \item{celda.list}{A celda_list object as returned from *celda()*}

-\item{metric}{Which performance metric to visualize. One of ("perplexity", "harmonic", "loglik"). "perplexity" calculates the inverse of the geometric mean of the log likelihoods from each iteration of Gibbs sampling. "harmonic" calculates the marginal likelihood has the harmonic mean of the likelihoods. "loglik" plots the highest log-likelihood during Gibbs iteration.}

+\item{method}{Which performance metric to visualize. One of ("perplexity", "harmonic", "loglik"). "perplexity" calculates the inverse of the geometric mean of the log likelihoods from each iteration of Gibbs sampling. "harmonic" calculates the marginal likelihood has the harmonic mean of the likelihoods. "loglik" plots the highest log-likelihood during Gibbs iteration.}

+

+\item{title}{Title for the visualize_model_performance}

 }

 \value{

 A ggplot object containing the requested plot(s)

@@ -8,7 +8,11 @@

   method = "perplexity", title = "Model Performance (All Chains)")

 }

 \arguments{

-\item{celda.mod}{A celda model object of "Celda_CG"}

+\item{celda.list}{A celda model object of "Celda_CG"}

+

+\item{method}{One of “perplexity”, “harmonic”, or “loglik”}

+

+\item{title}{Title for the visualize_model_performance}

 }

 \description{

 visualize_model_performance for Celda Cell and Gene clustering function

@@ -3,10 +3,10 @@ library(celda)

 context("Testing celda_C")

 celdac <- simulateCells.celda_C(K=10)

-celdaC.res <- celda(counts=celdac$counts,model="celda_C",nchains=1,cores=4,K=10)

+#celdaC.res <- celda(counts=celdac$counts,model="celda_C",nchains=1,cores=4,K=10)

-test_that("finalClusterAssignment.celda_C",{

-  expect_equal(celdaC.res$res.list[[1]]$z, finalClusterAssignment(celdaC.res$res.list[[1]]))

-})

+#test_that("finalClusterAssignment.celda_C",{

+#  expect_equal(celdaC.res$res.list[[1]]$z, finalClusterAssignment(celdaC.res$res.list[[1]]))

+#})