@@ -54,6 +54,7 @@ export(simulateCells.celda_G)

 export(simulateObservedMatrix)

 export(subsetCeldaList)

 export(topRank)

+export(violinPlot)

 exportMethods(celdaHeatmap)

 exportMethods(celdaProbabilityMap)

 exportMethods(celdaTsne)

@@ -61,7 +62,6 @@ exportMethods(clusterProbability)

 exportMethods(factorizeMatrix)

 exportMethods(featureModuleLookup)

 exportMethods(perplexity)

-export(violinPlot)

 import(RColorBrewer)

 import(data.table)

 import(foreach)

@@ -14,7 +14,7 @@ cC.splitZ = function(counts, m.CP.by.S, n.G.by.CP, n.CP, s, z, K, nS, nG, alpha,

   ## Loop through each split-able Z and perform split

   clust.split = lapply(z.to.split, function(x){

-    suppressMessages(.celda_C(counts[,z == x], K=2, max.iter=5, split.on.iter=-1, split.on.last=FALSE))$z

+    suppressMessages(.celda_C(counts[,z == x], K=2, max.iter=5, split.on.iter=-1, split.on.last=FALSE))@clusters$z

   })

   clust.split = vector("list", K)

@@ -6,9 +6,10 @@

 \usage{

 celda_C(counts, sample.label = NULL, K, alpha = 1, beta = 1,

   algorithm = c("EM", "Gibbs"), stop.iter = 10, max.iter = 200,

-  split.on.iter = 10, split.on.last = TRUE, seed = 12345, nchains = 3,

-  initialize = c("random", "split"), count.checksum = NULL, z.init = NULL,

-  logfile = NULL, verbose = TRUE)

+  split.on.iter = 10, split.on.last = TRUE, seed = 12345,

+  nchains = 3, initialize = c("random", "split"),

+  count.checksum = NULL, z.init = NULL, logfile = NULL,

+  verbose = TRUE)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}

@@ -5,11 +5,11 @@

 \title{Cell and feature clustering with Celda}

 \usage{

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

-  delta = 1, gamma = 1, algorithm = c("EM", "Gibbs"), stop.iter = 10,

-  max.iter = 200, split.on.iter = 10, split.on.last = TRUE,

-  seed = 12345, nchains = 3, initialize = c("random", "split"),

-  count.checksum = NULL, z.init = NULL, y.init = NULL, logfile = NULL,

-  verbose = TRUE)

+  delta = 1, gamma = 1, algorithm = c("EM", "Gibbs"),

+  stop.iter = 10, max.iter = 200, split.on.iter = 10,

+  split.on.last = TRUE, seed = 12345, nchains = 3,

+  initialize = c("random", "split"), count.checksum = NULL,

+  z.init = NULL, y.init = NULL, logfile = NULL, verbose = TRUE)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}

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

 celda_G(counts, L, beta = 1, delta = 1, gamma = 1, stop.iter = 10,

   max.iter = 200, split.on.iter = 10, split.on.last = TRUE,

   seed = 12345, nchains = 3, initialize = c("random", "split"),

-  count.checksum = NULL, y.init = NULL, logfile = NULL, verbose = TRUE)

+  count.checksum = NULL, y.init = NULL, logfile = NULL,

+  verbose = TRUE)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}

@@ -4,8 +4,8 @@

 \alias{differentialExpression}

 \title{Differential expression for cell subpopulations using MAST}

 \usage{

-differentialExpression(counts, celda.mod, c1, c2 = NULL, only.pos = FALSE,

-  log2fc.threshold = NULL, fdr.threshold = 1)

+differentialExpression(counts, celda.mod, c1, c2 = NULL,

+  only.pos = FALSE, log2fc.threshold = NULL, fdr.threshold = 1)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate `celda.mod`.}

@@ -4,8 +4,8 @@

 \alias{logLikelihood.celda_CG}

 \title{Calculate Celda_CG log likelihood}

 \usage{

-logLikelihood.celda_CG(counts, sample.label, z, y, K, L, alpha, beta, delta,

-  gamma)

+logLikelihood.celda_CG(counts, sample.label, z, y, K, L, alpha, beta,

+  delta, gamma)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}

@@ -4,9 +4,9 @@

 \alias{normalizeCounts}

 \title{Normalization of count data}

 \usage{

-normalizeCounts(counts, normalize = c("proportion", "cpm", "median", "mean"),

-  transformation.fun = NULL, scale.fun = NULL, pseudocount.normalize = 0,

-  pseudocount.transform = 0)

+normalizeCounts(counts, normalize = c("proportion", "cpm", "median",

+  "mean"), transformation.fun = NULL, scale.fun = NULL,

+  pseudocount.normalize = 0, pseudocount.transform = 0)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent cells.}

@@ -4,8 +4,9 @@

 \alias{plotDimReduceCluster}

 \title{Plotting the cell labels on a dimensionality reduction plot}

 \usage{

-plotDimReduceCluster(dim1, dim2, cluster, size = 1, xlab = "Dimension_1",

-  ylab = "Dimension_2", specific_clusters = NULL)

+plotDimReduceCluster(dim1, dim2, cluster, size = 1,

+  xlab = "Dimension_1", ylab = "Dimension_2",

+  specific_clusters = NULL)

 }

 \arguments{

 \item{dim1}{Numeric vector. First dimension from data dimensionality reduction output.}

@@ -5,9 +5,9 @@

 \title{Plotting feature expression on a dimensionality reduction plot}

 \usage{

 plotDimReduceFeature(dim1, dim2, counts, features, normalize = TRUE,

-  exact.match = TRUE, trim = c(-2, 2), size = 1, xlab = "Dimension_1",

-  ylab = "Dimension_2", color_low = "grey", color_mid = NULL,

-  color_high = "blue")

+  exact.match = TRUE, trim = c(-2, 2), size = 1,

+  xlab = "Dimension_1", ylab = "Dimension_2", color_low = "grey",

+  color_mid = NULL, color_high = "blue")

 }

 \arguments{

 \item{dim1}{Numeric vector. First dimension from data dimensionality reduction output.}

@@ -4,8 +4,8 @@

 \alias{plotDimReduceGrid}

 \title{Mapping the dimensionality reduction plot}

 \usage{

-plotDimReduceGrid(dim1, dim2, matrix, size, xlab, ylab, color_low, color_mid,

-  color_high, var_label)

+plotDimReduceGrid(dim1, dim2, matrix, size, xlab, ylab, color_low,

+  color_mid, color_high, var_label)

 }

 \arguments{

 \item{dim1}{Numeric vector. First dimension from data dimensionality reduction output.}

@@ -5,8 +5,9 @@

 \title{Plotting the Celda module probability on a dimensionality reduction plot}

 \usage{

 plotDimReduceModule(dim1, dim2, counts, celda.mod, modules = NULL,

-  rescale = TRUE, size = 1, xlab = "Dimension_1", ylab = "Dimension_2",

-  color_low = "grey", color_mid = NULL, color_high = "blue")

+  rescale = TRUE, size = 1, xlab = "Dimension_1",

+  ylab = "Dimension_2", color_low = "grey", color_mid = NULL,

+  color_high = "blue")

 }

 \arguments{

 \item{dim1}{Numeric vector. First dimension from data dimensionality reduction output.}

@@ -4,9 +4,10 @@

 \alias{plotHeatmap}

 \title{Renders a heatmap based on a matrix of counts where rows are features and columns are cells.}

 \usage{

-plotHeatmap(counts, z = NULL, y = NULL, scale.row = scale, trim = c(-2,

-  2), feature.ix = NULL, cell.ix = NULL, cluster.feature = TRUE,

-  cluster.cell = TRUE, color.scheme = c("divergent", "sequential"),

+plotHeatmap(counts, z = NULL, y = NULL, scale.row = scale,

+  trim = c(-2, 2), feature.ix = NULL, cell.ix = NULL,

+  cluster.feature = TRUE, cluster.cell = TRUE,

+  color.scheme = c("divergent", "sequential"),

   color.scheme.symmetric = TRUE, color.scheme.center = 0, col = NULL,

   annotation.cell = NULL, annotation.feature = NULL,

   annotation.color = NULL, breaks = NULL, legend = TRUE,

@@ -5,19 +5,21 @@

 \title{A function to draw clustered heatmaps.}

 \usage{

 semi_pheatmap(mat, color = colorRampPalette(rev(brewer.pal(n = 7, name =

-  "RdYlBu")))(100), kmeans_k = NA, breaks = NA, border_color = "grey60",

-  cellwidth = NA, cellheight = NA, scale = "none", cluster_rows = TRUE,

-  cluster_cols = TRUE, clustering_distance_rows = "euclidean",

-  clustering_distance_cols = "euclidean", clustering_method = "complete",

-  clustering_callback = identity2, cutree_rows = NA, cutree_cols = NA,

+  "RdYlBu")))(100), kmeans_k = NA, breaks = NA,

+  border_color = "grey60", cellwidth = NA, cellheight = NA,

+  scale = "none", cluster_rows = TRUE, cluster_cols = TRUE,

+  clustering_distance_rows = "euclidean",

+  clustering_distance_cols = "euclidean",

+  clustering_method = "complete", clustering_callback = identity2,

+  cutree_rows = NA, cutree_cols = NA,

   treeheight_row = ifelse(cluster_rows, 50, 0),

   treeheight_col = ifelse(cluster_cols, 50, 0), legend = TRUE,

   legend_breaks = NA, legend_labels = NA, annotation_row = NA,

   annotation_col = NA, annotation = NA, annotation_colors = NA,

   annotation_legend = TRUE, annotation_names_row = TRUE,

-  annotation_names_col = TRUE, drop_levels = TRUE, show_rownames = TRUE,

-  show_colnames = TRUE, main = NA, fontsize = 10,

-  fontsize_row = fontsize, fontsize_col = fontsize,

+  annotation_names_col = TRUE, drop_levels = TRUE,

+  show_rownames = TRUE, show_colnames = TRUE, main = NA,

+  fontsize = 10, fontsize_row = fontsize, fontsize_col = fontsize,

   display_numbers = FALSE, number_format = "\%.2f",

   number_color = "grey30", fontsize_number = 0.8 * fontsize,

   gaps_row = NULL, gaps_col = NULL, labels_row = NULL,

@@ -5,8 +5,8 @@

 \title{This function generates a list containing two count matrices -- one for real expression, the other one for contamination, as well as other parameters 

 used in the simulation which can be useful for running decontamination}

 \usage{

-simulateObservedMatrix(C = 300, G = 100, K = 3, N.Range = c(500, 1000),

-  beta = 0.5, delta = c(1, 2), seed = 12345)

+simulateObservedMatrix(C = 300, G = 100, K = 3, N.Range = c(500,

+  1000), beta = 0.5, delta = c(1, 2), seed = 12345)

 }

 \arguments{

 \item{C}{Integer. Number of cells to be simulated. Default to be 300}

@@ -4,7 +4,8 @@

 \alias{topRank}

 \title{Identify features with the highest influence on clustering.}

 \usage{

-topRank(matrix, n = 25, margin = 2, threshold = 0, decreasing = TRUE)

+topRank(matrix, n = 25, margin = 2, threshold = 0,

+  decreasing = TRUE)

 }

 \arguments{

 \item{matrix}{Numeric matrix.}

old mode 100644

new mode 100755

@@ -67,12 +67,12 @@ static const R_CallMethodDef CallEntries[] = {

     {"_celda_fastNormProp", (DL_FUNC) &_celda_fastNormProp, 2},

     {"_celda_fastNormPropLog", (DL_FUNC) &_celda_fastNormPropLog, 2},

     {"_celda_fastNormPropSqrt", (DL_FUNC) &_celda_fastNormPropSqrt, 2},

-    {"_colSumByGroup",          (DL_FUNC) &_colSumByGroup,          2},

-    {"_colSumByGroup_numeric",  (DL_FUNC) &_colSumByGroup_numeric,  2},

-    {"_colSumByGroupChange",    (DL_FUNC) &_colSumByGroupChange,    4},

-    {"_rowSumByGroup",          (DL_FUNC) &_rowSumByGroup,          2},

-    {"_rowSumByGroup_numeric",  (DL_FUNC) &_rowSumByGroup_numeric,  2},

-    {"_rowSumByGroupChange",    (DL_FUNC) &_rowSumByGroupChange,    4},

+    {"_colSumByGroup",         (DL_FUNC) &_colSumByGroup,         2},

+    {"_colSumByGroup_numeric", (DL_FUNC) &_colSumByGroup_numeric, 2},

+    {"_colSumByGroupChange",   (DL_FUNC) &_colSumByGroupChange,   4},

+    {"_rowSumByGroup",         (DL_FUNC) &_rowSumByGroup,         2},

+    {"_rowSumByGroup_numeric", (DL_FUNC) &_rowSumByGroup_numeric, 2},

+    {"_rowSumByGroupChange",   (DL_FUNC) &_rowSumByGroupChange,   4},

     {NULL, NULL, 0}

 };