@@ -13,27 +13,26 @@

 #' @seealso \code{geom_smooth} for plotting smooth lines, \code{facet_wrap} for faceting genes.

 #'

 #' @importFrom ggplot2 ggplot aes_string theme theme_bw geom_smooth geom_line labs facet_wrap

-#' @importFrom SummarizedExperiment rowData colData

+#' @importFrom SummarizedExperiment assay rowData colData

 #' @export

 #'

 #' @examples

 #' data(zh.data)

-#' zh <- CreateTomo(zh.data)

-#' zh <- Normalize(zh)

-#' LinePlot(zh,

+#' zh <- createTomo(zh.data)

+#' linePlot(zh,

 #'  c("ENSDARG00000002131", "ENSDARG00000003061", "ENSDARG00000076075", "ENSDARG00000076850"))

 #'

 #' # Do not smooth lines.

-#' LinePlot(zh,

+#' linePlot(zh,

 #'  c("ENSDARG00000002131", "ENSDARG00000003061", "ENSDARG00000076075", "ENSDARG00000076850"), span=0)

 #'

 #' # Plot genes in different facets.

-#' LinePlot(zh,

+#' linePlot(zh,

 #'  c("ENSDARG00000002131", "ENSDARG00000003061", "ENSDARG00000076075", "ENSDARG00000076850"),

 #'  facet=TRUE)

-LinePlot <- function(object, genes, matrix='normalized', facet=FALSE, span=0.3)

+linePlot <- function(object, genes, matrix='normalized', facet=FALSE, span=0.3)

 {

-    exp_matrix <- GetMatrix(object, matrix)

+    exp_matrix <- assay(object, matrix)

     exp_genes <- subset(exp_matrix, rowData(object)$gene %in% genes)

     exp_genes_df <- NULL

     sections <- factor(colData(object)$section, levels=colData(object)$section)

@@ -45,7 +44,7 @@ LinePlot <- function(object, genes, matrix='normalized', facet=FALSE, span=0.3)

     ylab <- c('Count', 'Normalized count', 'Scaled expression')[c('count', 'normalized', 'scaled') == matrix]

     g <- ggplot(exp_genes_df, aes_string(x='section', y='value', group='gene', color='gene')) +

-        labs(x='', y=ylab) +

+        labs(y=ylab) +

         theme_bw() +

         theme(legend.position='top', axis.text.x=element_text(angle=90, hjust=1, vjust=0.5))

@@ -78,25 +77,24 @@ LinePlot <- function(object, genes, matrix='normalized', facet=FALSE, span=0.3)

 #'

 #' @examples

 #' data(zh.data)

-#' zh <- CreateTomo(zh.data)

-#' zh <- Normalize(zh)

-#' zh <- TSNE(zh)

+#' zh <- createTomo(zh.data)

+#' zh <- runTSNE(zh)

 #' # Plot TSNE embeddings.

-#' EmbedPlot(zh)

+#' embedPlot(zh)

 #'

 #' # Plot UMAP embeddings.

-#' zh <- UMAP(zh)

-#' EmbedPlot(zh, method="UMAP")

+#' zh <- runUMAP(zh)

+#' embedPlot(zh, method="UMAP")

 #'

 #' # Color sections by kmeans cluster labels.

-#' zh <- KMeans(zh, 3)

-#' EmbedPlot(zh, group="kmeans_cluster")

-EmbedPlot <- function(object, group='section', method='TSNE')

+#' zh <- kmeansClust(zh, 3)

+#' embedPlot(zh, group="kmeans_cluster")

+embedPlot <- function(object, group='section', method='TSNE')

 {

     if(!group %in% names(colData(object)))

     {

         group <- 'section'

-        cat("Unknown parameter 'group' for plotting sections!")

+        message("Unknown parameter 'group' for plotting sections!")

     }

     colData(object)[[group]] <- as.character(colData(object)[[group]])

@@ -113,7 +111,7 @@ EmbedPlot <- function(object, group='section', method='TSNE')

         }

         else

         {

-            cat("Function 'TSNE' must be run before plotting TSNE embeddings.\n")

+            message("Function 'runTSNE' must be run before plotting TSNE embeddings.\n")

         }

     }

     else if(method == 'UMAP')

@@ -129,7 +127,7 @@ EmbedPlot <- function(object, group='section', method='TSNE')

         }

         else

         {

-            cat("Function 'UMAP' must be run before plotting UMAP embeddings.\n")

+            message("Function 'runUMAP' must be run before plotting UMAP embeddings.\n")

         }

     }

     else if(method == 'PCA')

@@ -145,12 +143,12 @@ EmbedPlot <- function(object, group='section', method='TSNE')

         }

         else

         {

-            cat("Function 'PCA' must be run before plotting PC embeddings.\n")

+            message("Function 'runPCA' must be run before plotting PC embeddings.\n")

         }

     }

     else

     {

-        cat("Unknown embeddings!\n")

+        message("Unknown embeddings!\n")

     }

 }

@@ -165,26 +163,25 @@ EmbedPlot <- function(object, group='section', method='TSNE')

 #'

 #' @return A \code{ggplot} object.

 #'

-#' @importFrom SummarizedExperiment assays rowData

+#' @importFrom SummarizedExperiment rowData

 #' @importFrom ggplot2 ggplot aes_string geom_point theme theme_bw

 #'

 #' @export

 #'

 #' @examples

 #' data(zh.data)

-#' zh <- CreateTomo(zh.data)

-#' zh <- Normalize(zh)

-#' peak_genes <- FindPeakGene(zh)

-#' zh <- TSNE(zh, peak_genes$gene)

+#' zh <- createTomo(zh.data)

+#' peak_genes <- findPeakGene(zh)

+#' zh <- runTSNE(zh, peak_genes$gene)

 #' # Color genes by peak centers.

-#' GeneEmbedPlot(zh, peak_genes)

+#' geneEmbedPlot(zh, peak_genes)

 #'

 #' # Color genes by peak starts.

-#' GeneEmbedPlot(zh, peak_genes, group="start")

+#' geneEmbedPlot(zh, peak_genes, group="start")

 #'

 #' # Do not color genes.

-#' GeneEmbedPlot(zh, peak_genes["gene"])

-GeneEmbedPlot <- function(object, gene.df, group='center', method='TSNE')

+#' geneEmbedPlot(zh, peak_genes["gene"])

+geneEmbedPlot <- function(object, gene.df, group='center', method='TSNE')

 {

     if(group %in% names(gene.df))

         gene.df[[group]] <- as.factor(gene.df[[group]])

@@ -206,7 +203,7 @@ GeneEmbedPlot <- function(object, gene.df, group='center', method='TSNE')

         }

         else

         {

-            cat("Function 'TSNE' must be run for input genes before plotting TSNE embeddings.\n")

+            message("Function 'TSNE' must be run for input genes before plotting TSNE embeddings.\n")

         }

     }

     else if(method == 'UMAP')

@@ -227,7 +224,7 @@ GeneEmbedPlot <- function(object, gene.df, group='center', method='TSNE')

         }

         else

         {

-            cat("Function 'UMAP' must be run for input genes before plotting UMAP embeddings.\n")

+            message("Function 'UMAP' must be run for input genes before plotting UMAP embeddings.\n")

         }

     }

     else if(method == 'PCA')

@@ -248,12 +245,12 @@ GeneEmbedPlot <- function(object, gene.df, group='center', method='TSNE')

         }

         else

         {

-            cat("Function 'PCA' must be run for input genes before plotting PC embeddings.\n")

+            message("Function 'runPCA' must be run for input genes before plotting PC embeddings.\n")

         }

     }

     else

     {

-        cat("Unknown embeddings!\n")

+        message("Unknown embeddings!\n")

     }

 }

@@ -268,39 +265,39 @@ GeneEmbedPlot <- function(object, gene.df, group='center', method='TSNE')

 #'

 #' @return A \code{ggplot} object.

 #'

+#' @importFrom SummarizedExperiment assay

 #' @importFrom reshape2 melt

-#' @importFrom ggplot2 ggplot aes_string geom_raster scale_fill_gradient2 theme element_blank element_text

+#' @importFrom RColorBrewer brewer.pal

+#' @importFrom ggplot2 ggplot aes_string geom_raster scale_fill_distiller theme element_blank element_text

 #'

 #' @export

 #'

 #' @examples

 #' data(zh.data)

-#' zh <- CreateTomo(zh.data)

-#' zh <- Normalize(zh)

-#' zh <- Scale(zh)

+#' zh <- createTomo(zh.data)

 #'

 #' # Plot some genes.

-#' ExpHeatmap(zh,

+#' expHeatmap(zh,

 #'  c("ENSDARG00000002131", "ENSDARG00000003061", "ENSDARG00000076075", "ENSDARG00000076850"))

 #'

 #' # Plot peak genes.

-#' peak_genes <- FindPeakGene(zh)

-#' ExpHeatmap(zh, peak_genes$gene)

+#' peak_genes <- findPeakGene(zh)

+#' expHeatmap(zh, peak_genes$gene)

 #'

 #' # Remove gene names if too many genes are in the heatmap.

-#' ExpHeatmap(zh, peak_genes$gene, size=0)

-ExpHeatmap <- function(object, genes, matrix='scaled', size=5)

+#' expHeatmap(zh, peak_genes$gene, size=0)

+expHeatmap <- function(object, genes, matrix='scaled', size=5)

 {

     genes <- rev(as.character(genes))

-    exp_matrix <- GetMatrix(object, matrix)[genes, ]

-    if(matrix == 'normalized')

+    exp_matrix <- assay(object, matrix)[genes, ]

+    if(matrix %in% c('count', 'normalized'))

         exp_matrix <- log10(exp_matrix + 1)

     genes_df <- melt(exp_matrix, varnames=c('gene','section'))

     exp_name <-  c('Log10(Count+1)', 'Log10(Normalized count+1)', 'Scaled expression\n(Z score)')[c('count', 'normalized', 'scaled') == matrix]

     g <- ggplot(genes_df, aes_string(x='section', y='gene', fill='value')) +

         geom_raster() +

-        scale_fill_gradient2(low='magenta', mid='black', high='yellow', name=exp_name) +

+        scale_fill_distiller(palette='RdYlBu', name=exp_name) +

         theme(axis.line=element_blank(),

               axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),

               axis.ticks=element_blank(),

@@ -324,28 +321,27 @@ ExpHeatmap <- function(object, genes, matrix='scaled', size=5)

 #'

 #' @return A \code{ggplot} object.

 #'

+#' @importFrom SummarizedExperiment assay

 #' @importFrom stats cor

 #' @importFrom reshape2 melt

-#' @importFrom ggplot2 ggplot aes_string geom_raster scale_fill_gradientn labs theme element_blank element_text

-#' @importFrom colorRamps rgb.tables

+#' @importFrom RColorBrewer brewer.pal

+#' @importFrom ggplot2 ggplot aes_string geom_raster scale_fill_distiller labs theme element_blank element_text

 #'

 #' @export

 #'

 #' @examples

 #' data(zh.data)

-#' zh <- CreateTomo(zh.data)

-#' zh <- Normalize(zh)

-#' zh <- Scale(zh)

-#' CorHeatmap(zh)

+#' zh <- createTomo(zh.data)

+#' corHeatmap(zh)

 #'

 #' # Use Spearman correlation coefficients.

-#' CorHeatmap(zh, cor.method='spearman')

+#' corHeatmap(zh, cor.method='spearman')

 #'

 #' # Set max correlation coefficients to 0.3.

-#' CorHeatmap(zh, max.cor=0.3)

-CorHeatmap <- function(object, matrix='scaled', max.cor=0.5, cor.method='pearson')

+#' corHeatmap(zh, max.cor=0.3)

+corHeatmap <- function(object, matrix='scaled', max.cor=0.5, cor.method='pearson')

 {

-    exp_matrix <- GetMatrix(object, matrix=matrix)

+    exp_matrix <- assay(object, matrix)

     cor_matrix <- cor(exp_matrix, method=cor.method)

     cor_matrix[cor_matrix > max.cor] <- max.cor

     cor_df <- melt(cor_matrix, varnames=c('section1','section2'))

@@ -354,7 +350,7 @@ CorHeatmap <- function(object, matrix='scaled', max.cor=0.5, cor.method='pearson

     g <- ggplot(cor_df, aes_string(x='section1', y='section2', fill='value')) +

         geom_raster() +

-        scale_fill_gradientn(colors=rgb.tables(10), name=cor_name) +

+        scale_fill_distiller(palette='RdYlBu', name=cor_name) +

         labs(x='', y='') +

         theme(axis.line=element_blank(),

               axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),

@@ -377,39 +373,39 @@ CorHeatmap <- function(object, matrix='scaled', max.cor=0.5, cor.method='pearson

 #'

 #' @details This method can create a pure heatmap or a heatmap with side bar. If you prefer a pure heatmap, input a \code{gene.df} with a single column of gene names.

 #' However, you may want to show additional information of genes with a side bar, and the grouping information should be saved as additional column(s) of \code{gene.df}, and declared as \code{group}.

-#' By default, you can use the output by \code{FindPeakGene} as input \code{gene.df}. Peak genes will be grouped by their centers on the side bar.

+#' By default, you can use the output by \code{findPeakGene} as input \code{gene.df}. Peak genes will be grouped by their centers on the side bar.

 #'

 #' @return A \code{ggplot} object.

 #'

+#' @importFrom SummarizedExperiment assay

 #' @importFrom stats cor

 #' @importFrom grDevices rainbow

 #' @importFrom reshape2 melt

-#' @importFrom ggplot2 ggplot aes_string geom_raster scale_fill_gradientn labs theme element_blank element_text ggplot_build scale_color_manual guides annotation_raster guide_legend coord_cartesian

+#' @importFrom RColorBrewer brewer.pal

+#' @importFrom ggplot2 ggplot aes_string geom_raster scale_fill_distiller labs theme element_blank element_text ggplot_build scale_color_manual guides annotation_raster guide_legend coord_cartesian

 #'

 #' @export

 #'

 #' @examples

 #' data(zh.data)

-#' zh <- CreateTomo(zh.data)

-#' zh <- Normalize(zh)

-#' zh <- Scale(zh)

+#' zh <- createTomo(zh.data)

 #'

 #' # Correlation heatmap for all peak genes.

-#' peak_genes <- FindPeakGene(zh)

-#' GeneCorHeatmap(zh, peak_genes)

+#' peak_genes <- findPeakGene(zh)

+#' geneCorHeatmap(zh, peak_genes)

 #'

 #' # Use Spearman correlation coefficients.

-#' GeneCorHeatmap(zh, peak_genes, cor.method="spearman")

+#' geneCorHeatmap(zh, peak_genes, cor.method="spearman")

 #'

 #' # Group genes by peak start.

-#' GeneCorHeatmap(zh, peak_genes, group="start")

+#' geneCorHeatmap(zh, peak_genes, group="start")

 #'

 #' # Plot without side bar.

-#' GeneCorHeatmap(zh, data.frame(

+#' geneCorHeatmap(zh, data.frame(

 #'  gene=c("ENSDARG00000002131", "ENSDARG00000003061", "ENSDARG00000076075", "ENSDARG00000076850")))

-GeneCorHeatmap <- function(object, gene.df, group='center', matrix='scaled', size=5, cor.method='pearson')

+geneCorHeatmap <- function(object, gene.df, group='center', matrix='scaled', size=5, cor.method='pearson')

 {

-    exp_matrix <- GetMatrix(object, matrix=matrix)

+    exp_matrix <- assay(object, matrix)

     genes <- as.character(gene.df$gene)

     cor_matrix <- cor(t(exp_matrix[genes, ]), method=cor.method)

     cor_df <- melt(cor_matrix, varnames=c('gene1', 'gene2'))

@@ -430,7 +426,7 @@ GeneCorHeatmap <- function(object, gene.df, group='center', matrix='scaled', siz

     g <- ggplot(cor_df, aes_string(x='gene1', y='gene2', fill='value')) +

         geom_raster() +

-        scale_fill_gradientn(colors=rgb.tables(10), name=cor_name)  +

+        scale_fill_distiller(palette='RdYlBu', name=cor_name)  +

         labs(x='', y='') +

         theme(axis.line=element_blank(),

               axis.text.y=element_blank(),

@@ -451,9 +447,10 @@ GeneCorHeatmap <- function(object, gene.df, group='center', matrix='scaled', siz

         y_max <- y_min + 0.03 * y_range

         g <- g + geom_point(aes_string(color='group'), alpha=0, shape=15, size=5) +

-            scale_color_manual(name=group, values=color_legend)  +

+            scale_color_manual(name=group, values=color_legend) +

             guides(color=guide_legend(override.aes=list(alpha=1))) +

-            annotation_raster(t(color_legend[as.character(group_genes)]), -Inf, Inf, ymin=y_min, ymax=y_max) +

+            annotation_raster(t(color_legend[as.character(group_genes)]),

+                              -Inf, Inf, ymin=y_min, ymax=y_max) +

             coord_cartesian(ylim = c(0, y_max), clip='off')

     }

@@ -33,31 +33,31 @@

 #'  facet=TRUE)

 LinePlot <- function(object, genes, matrix='normalized', facet=FALSE, span=0.3)

 {

-  exp_matrix <- GetMatrix(object, matrix)

-  exp_genes <- subset(exp_matrix, rowData(object)$gene %in% genes)

-  exp_genes_df <- NULL

-  sections <- factor(colData(object)$section, levels=colData(object)$section)

-  for(i in 1:nrow(exp_genes))

-  {

-    exp_gene_df <- data.frame(gene=genes[i], section=sections, value=exp_genes[i,])

-    exp_genes_df <- rbind(exp_genes_df, exp_gene_df)

-  }

-  ylab <- c('Count', 'Normalized count', 'Scaled expression')[c('count', 'normalized', 'scaled') == matrix]

+    exp_matrix <- GetMatrix(object, matrix)

+    exp_genes <- subset(exp_matrix, rowData(object)$gene %in% genes)

+    exp_genes_df <- NULL

+    sections <- factor(colData(object)$section, levels=colData(object)$section)

+    for(i in seq_len(nrow(exp_genes)))

+    {

+        exp_gene_df <- data.frame(gene=genes[i], section=sections, value=exp_genes[i,])

+        exp_genes_df <- rbind(exp_genes_df, exp_gene_df)

+    }

+    ylab <- c('Count', 'Normalized count', 'Scaled expression')[c('count', 'normalized', 'scaled') == matrix]

-  g <- ggplot(exp_genes_df, aes_string(x='section', y='value', group='gene', color='gene')) +

-    labs(x='', y=ylab) +

-    theme_bw() +

-    theme(legend.position='top', axis.text.x=element_text(angle=90, hjust=1, vjust=0.5))

+    g <- ggplot(exp_genes_df, aes_string(x='section', y='value', group='gene', color='gene')) +

+        labs(x='', y=ylab) +

+        theme_bw() +

+        theme(legend.position='top', axis.text.x=element_text(angle=90, hjust=1, vjust=0.5))

-  if(span > 0)

-    g <- g + geom_smooth(method='loess', span = span, se=F)

-  else

-    g <- g + geom_line(size=1)

+    if(span > 0)

+        g <- g + geom_smooth(method='loess', span=span, se=FALSE)

+    else

+        g <- g + geom_line(size=1)

-  if(facet)

-    g <- g + facet_wrap(~gene, scales = 'free') + theme(legend.position='none')

+    if(facet)

+        g <- g + facet_wrap(~gene, scales='free') + theme(legend.position='none')

-  return(g)

+    return(g)

 }

 #' Embedding plot for sections

@@ -93,65 +93,65 @@ LinePlot <- function(object, genes, matrix='normalized', facet=FALSE, span=0.3)

 #' EmbedPlot(zh, group="kmeans_cluster")

 EmbedPlot <- function(object, group='section', method='TSNE')

 {

-  if(!group %in% names(colData(object)))

-  {

-    group <- 'section'

-    cat("Unknown parameter 'group' for plotting sections!")

-  }

-  colData(object)[[group]] <- as.character(colData(object)[[group]])

-

-  if(method == 'TSNE')

-  {

-    if(all(c('TSNE1','TSNE2') %in% names(colData(object))))

+    if(!group %in% names(colData(object)))

     {

-      g <- ggplot(data.frame(colData(object)), aes_string(x='TSNE1', y='TSNE2', color=group)) +

-        geom_point() +

-        geom_text_repel(aes_string(label='section')) +

-        theme_bw() +

-        theme(legend.position='none')

-      return(g)

+        group <- 'section'

+        cat("Unknown parameter 'group' for plotting sections!")

     }

-    else

-    {

-      cat("Function 'TSNE' must be run before plotting TSNE embeddings.\n")

-    }

-  }

-  else if(method == 'UMAP')

-  {

-    if(all(c('UMAP1','UMAP2') %in% names(colData(object))))

+    colData(object)[[group]] <- as.character(colData(object)[[group]])

+

+    if(method == 'TSNE')

     {

-      g <- ggplot(data.frame(colData(object)), aes_string(x='UMAP1', y='UMAP2', color=group)) +

-        geom_point(aes_string()) +

-        geom_text_repel(aes_string(label='section')) +

-        theme_bw() +

-        theme(legend.position='none')

-      return(g)

+        if(all(c('TSNE1','TSNE2') %in% names(colData(object))))

+        {

+            g <- ggplot(data.frame(colData(object)), aes_string(x='TSNE1', y='TSNE2', color=group)) +

+                geom_point() +

+                geom_text_repel(aes_string(label='section')) +

+                theme_bw() +

+                theme(legend.position='none')

+            return(g)

+        }

+        else

+        {

+            cat("Function 'TSNE' must be run before plotting TSNE embeddings.\n")

+        }

     }

-    else

+    else if(method == 'UMAP')

     {

-      cat("Function 'UMAP' must be run before plotting UMAP embeddings.\n")

+        if(all(c('UMAP1','UMAP2') %in% names(colData(object))))

+        {

+            g <- ggplot(data.frame(colData(object)), aes_string(x='UMAP1', y='UMAP2', color=group)) +

+                geom_point(aes_string()) +

+                geom_text_repel(aes_string(label='section')) +

+                theme_bw() +

+                theme(legend.position='none')

+            return(g)

+        }

+        else

+        {

+            cat("Function 'UMAP' must be run before plotting UMAP embeddings.\n")

+        }

     }

-  }

-  else if(method == 'PCA')

-  {

-    if(all(c('PC1','PC2') %in% names(colData(object))))

+    else if(method == 'PCA')

     {

-      g <- ggplot(data.frame(colData(object)), aes_string(x='PC1', y='PC2', color=group)) +

-        geom_point(aes_string()) +

-        geom_text_repel(aes_string(label='section')) +

-        theme_bw() +

-        theme(legend.position='none')

-      return(g)

+        if(all(c('PC1','PC2') %in% names(colData(object))))

+        {

+            g <- ggplot(data.frame(colData(object)), aes_string(x='PC1', y='PC2', color=group)) +

+                geom_point(aes_string()) +

+                geom_text_repel(aes_string(label='section')) +

+                theme_bw() +

+                theme(legend.position='none')

+            return(g)

+        }

+        else

+        {

+            cat("Function 'PCA' must be run before plotting PC embeddings.\n")

+        }

     }

     else

     {

-      cat("Function 'PCA' must be run before plotting PC embeddings.\n")

+        cat("Unknown embeddings!\n")

     }

-  }

-  else

-  {

-    cat("Unknown embeddings!\n")

-  }

 }

 #' Embedding plot for genes

@@ -186,75 +186,75 @@ EmbedPlot <- function(object, group='section', method='TSNE')

 #' GeneEmbedPlot(zh, peak_genes["gene"])

 GeneEmbedPlot <- function(object, gene.df, group='center', method='TSNE')

 {

-  if(group %in% names(gene.df))

-    gene.df[[group]] <- as.factor(gene.df[[group]])

-  if(method == 'TSNE')

-  {

-    if(all(c('TSNE1','TSNE2') %in% names(rowData(object))))

+    if(group %in% names(gene.df))

+        gene.df[[group]] <- as.factor(gene.df[[group]])

+    if(method == 'TSNE')

     {

-      gene.df$TSNE1 <- rowData(object)[as.character(gene.df$gene), 'TSNE1']

-      gene.df$TSNE2 <- rowData(object)[as.character(gene.df$gene), 'TSNE2']

-      g <- ggplot(gene.df, aes_string(x='TSNE1', y='TSNE2')) +

-        theme_bw()

+        if(all(c('TSNE1','TSNE2') %in% names(rowData(object))))

+        {

+            gene.df$TSNE1 <- rowData(object)[as.character(gene.df$gene), 'TSNE1']

+            gene.df$TSNE2 <- rowData(object)[as.character(gene.df$gene), 'TSNE2']

+            g <- ggplot(gene.df, aes_string(x='TSNE1', y='TSNE2')) +

+                theme_bw()

-      if(group %in% names(gene.df))

-        g <- g + geom_point(aes_string(color=group))

-      else

-        g <- g + geom_point()

+            if(group %in% names(gene.df))

+                g <- g + geom_point(aes_string(color=group))

+            else

+                g <- g + geom_point()

-      return(g)

+            return(g)

+        }

+        else

+        {

+            cat("Function 'TSNE' must be run for input genes before plotting TSNE embeddings.\n")

+        }

     }

-    else

-    {

-      cat("Function 'TSNE' must be run for input genes before plotting TSNE embeddings.\n")

-    }

-  }

-  else if(method == 'UMAP')

-  {

-    if(all(c('UMAP1','UMAP2') %in% names(rowData(object))))

+    else if(method == 'UMAP')

     {

-      gene.df$UMAP1 <- rowData(object)[as.character(gene.df$gene), 'UMAP1']

-      gene.df$UMAP2 <- rowData(object)[as.character(gene.df$gene), 'UMAP2']

-      g <- ggplot(gene.df, aes_string(x='UMAP1', y='UMAP2')) +

-        theme_bw()

+        if(all(c('UMAP1','UMAP2') %in% names(rowData(object))))

+        {

+            gene.df$UMAP1 <- rowData(object)[as.character(gene.df$gene), 'UMAP1']

+            gene.df$UMAP2 <- rowData(object)[as.character(gene.df$gene), 'UMAP2']

+            g <- ggplot(gene.df, aes_string(x='UMAP1', y='UMAP2')) +

+                theme_bw()

-      if(group %in% names(gene.df))

-        g <- g + geom_point(aes_string(color=group))

-      else

-        g <- g + geom_point()

+            if(group %in% names(gene.df))

+                g <- g + geom_point(aes_string(color=group))

+            else

+                g <- g + geom_point()

-      return(g)

-    }

-    else

-    {

-      cat("Function 'UMAP' must be run for input genes before plotting UMAP embeddings.\n")

+            return(g)

+        }

+        else

+        {

+            cat("Function 'UMAP' must be run for input genes before plotting UMAP embeddings.\n")

+        }

     }

-  }

-  else if(method == 'PCA')

-  {

-    if(all(c('PC1','PC2') %in% names(rowData(object))))

+    else if(method == 'PCA')

     {

-      gene.df$PC1 <- rowData(object)[as.character(gene.df$gene), 'PC1']

-      gene.df$PC2 <- rowData(object)[as.character(gene.df$gene), 'PC2']

-      g <- ggplot(gene.df, aes_string(x='PC1', y='PC2')) +

-        theme_bw()

+        if(all(c('PC1','PC2') %in% names(rowData(object))))

+        {

+            gene.df$PC1 <- rowData(object)[as.character(gene.df$gene), 'PC1']

+            gene.df$PC2 <- rowData(object)[as.character(gene.df$gene), 'PC2']

+            g <- ggplot(gene.df, aes_string(x='PC1', y='PC2')) +

+                theme_bw()

-      if(group %in% names(gene.df))

-        g <- g + geom_point(aes_string(color=group))

-      else

-        g <- g + geom_point()

+            if(group %in% names(gene.df))

+                g <- g + geom_point(aes_string(color=group))

+            else

+                g <- g + geom_point()

-      return(g)

+            return(g)

+        }

+        else

+        {

+            cat("Function 'PCA' must be run for input genes before plotting PC embeddings.\n")

+        }

     }

     else

     {

-      cat("Function 'PCA' must be run for input genes before plotting PC embeddings.\n")

+        cat("Unknown embeddings!\n")

     }

-  }

-  else

-  {

-    cat("Unknown embeddings!\n")

-  }

 }

 #' Expression heatmap

@@ -291,26 +291,26 @@ GeneEmbedPlot <- function(object, gene.df, group='center', method='TSNE')

 #' ExpHeatmap(zh, peak_genes$gene, size=0)

 ExpHeatmap <- function(object, genes, matrix='scaled', size=5)

 {

-  genes <- rev(as.character(genes))

-  exp_matrix <- GetMatrix(object, matrix)[genes, ]

-  if(matrix == 'normalized')

-    exp_matrix <- log10(exp_matrix + 1)

-  genes_df <- melt(exp_matrix, varnames=c('gene','section'))

-  exp_name <-  c('Log10(Count+1)', 'Log10(Normalized count+1)', 'Scaled expression\n(Z score)')[c('count', 'normalized', 'scaled') == matrix]

+    genes <- rev(as.character(genes))

+    exp_matrix <- GetMatrix(object, matrix)[genes, ]

+    if(matrix == 'normalized')

+        exp_matrix <- log10(exp_matrix + 1)

+    genes_df <- melt(exp_matrix, varnames=c('gene','section'))

+    exp_name <-  c('Log10(Count+1)', 'Log10(Normalized count+1)', 'Scaled expression\n(Z score)')[c('count', 'normalized', 'scaled') == matrix]

-  g <- ggplot(genes_df, aes_string(x='section', y='gene', fill='value')) +

-    geom_raster() +

-    scale_fill_gradient2(low='magenta', mid='black', high='yellow', name=exp_name) +

-    theme(axis.line=element_blank(),

-          axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),

-          axis.ticks=element_blank(),

-          panel.background=element_blank())

-  if(size == 0)

-    g <- g + theme(axis.text.y=element_blank())

-  else

-    g <- g + theme(axis.text.y=element_text(size=size))

+    g <- ggplot(genes_df, aes_string(x='section', y='gene', fill='value')) +

+        geom_raster() +

+        scale_fill_gradient2(low='magenta', mid='black', high='yellow', name=exp_name) +

+        theme(axis.line=element_blank(),

+              axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),

+              axis.ticks=element_blank(),

+              panel.background=element_blank())

+    if(size == 0)

+        g <- g + theme(axis.text.y=element_blank())

+    else

+        g <- g + theme(axis.text.y=element_text(size=size))

-  return(g)

+    return(g)

 }

 #' Correlation heatmap of sections

@@ -345,23 +345,23 @@ ExpHeatmap <- function(object, genes, matrix='scaled', size=5)

 #' CorHeatmap(zh, max.cor=0.3)

 CorHeatmap <- function(object, matrix='scaled', max.cor=0.5, cor.method='pearson')

 {

-  exp_matrix <- GetMatrix(object, matrix=matrix)

-  cor_matrix <- cor(exp_matrix, method=cor.method)

-  cor_matrix[cor_matrix > max.cor] <- max.cor

-  cor_df <- melt(cor_matrix, varnames=c('section1','section2'))

-  # c('Pearson r', 'Kendall τ', 'Spearman ρ')

-  cor_name <- expression("Pearson"~r,"Kendall"~tau,"Spearman"~rho)[c('pearson', 'kendall', 'spearman') == cor.method]

+    exp_matrix <- GetMatrix(object, matrix=matrix)

+    cor_matrix <- cor(exp_matrix, method=cor.method)

+    cor_matrix[cor_matrix > max.cor] <- max.cor

+    cor_df <- melt(cor_matrix, varnames=c('section1','section2'))

+    # c('Pearson r', 'Kendall τ', 'Spearman ρ')

+    cor_name <- expression("Pearson"~r,"Kendall"~tau,"Spearman"~rho)[c('pearson', 'kendall', 'spearman') == cor.method]

-  g <- ggplot(cor_df, aes_string(x='section1', y='section2', fill='value')) +

-    geom_raster() +

-    scale_fill_gradientn(colors=rgb.tables(10), name=cor_name) +

-    labs(x='', y='') +

-    theme(axis.line=element_blank(),

-          axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),

-          axis.ticks=element_blank(),

-          panel.background=element_blank())

+    g <- ggplot(cor_df, aes_string(x='section1', y='section2', fill='value')) +

+        geom_raster() +

+        scale_fill_gradientn(colors=rgb.tables(10), name=cor_name) +

+        labs(x='', y='') +

+        theme(axis.line=element_blank(),

+              axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),

+              axis.ticks=element_blank(),

+              panel.background=element_blank())

-  return(g)

+    return(g)

 }

 #' Correlation heatmap of genes

@@ -409,53 +409,53 @@ CorHeatmap <- function(object, matrix='scaled', max.cor=0.5, cor.method='pearson

 #'  gene=c("ENSDARG00000002131", "ENSDARG00000003061", "ENSDARG00000076075", "ENSDARG00000076850")))

 GeneCorHeatmap <- function(object, gene.df, group='center', matrix='scaled', size=5, cor.method='pearson')

 {

-  exp_matrix <- GetMatrix(object, matrix=matrix)

-  genes <- as.character(gene.df$gene)

-  cor_matrix <- cor(t(exp_matrix[genes, ]), method=cor.method)

-  cor_df <- melt(cor_matrix, varnames=c('gene1', 'gene2'))

-  cor_name <- expression("Pearson"~r,"Kendall"~tau,"Spearman"~rho)[c('pearson', 'kendall', 'spearman') == cor.method]

-  # plot sidebar if genes are grouped

-  plot_sidebar <- group %in% names(gene.df)

+    exp_matrix <- GetMatrix(object, matrix=matrix)

+    genes <- as.character(gene.df$gene)

+    cor_matrix <- cor(t(exp_matrix[genes, ]), method=cor.method)

+    cor_df <- melt(cor_matrix, varnames=c('gene1', 'gene2'))

+    cor_name <- expression("Pearson"~r,"Kendall"~tau,"Spearman"~rho)[c('pearson', 'kendall', 'spearman') == cor.method]

+    # plot sidebar if genes are grouped

+    plot_sidebar <- group %in% names(gene.df)

-  if(plot_sidebar)

-  {

-    group_genes <- gene.df[[group]]

-    names(group_genes) <- gene.df$gene

-    cor_df$group <- as.factor(group_genes[cor_df$gene1])

-    # Colors for column side bar

-    n_groups <- length(unique(group_genes))

-    color_legend <- rainbow(n_groups)

-    names(color_legend) <- unique(group_genes)

-  }

+    if(plot_sidebar)

+    {

+        group_genes <- gene.df[[group]]

+        names(group_genes) <- gene.df$gene

+        cor_df$group <- as.factor(group_genes[cor_df$gene1])

+        # Colors for column side bar

+        n_groups <- length(unique(group_genes))

+        color_legend <- rainbow(n_groups)

+        names(color_legend) <- unique(group_genes)

+    }

-  g <- ggplot(cor_df, aes_string(x='gene1', y='gene2', fill='value')) +

-    geom_raster() +

-    scale_fill_gradientn(colors=rgb.tables(10), name=cor_name)  +

-    labs(x='', y='') +

-    theme(axis.line=element_blank(),

-          axis.text.y=element_blank(),

-          axis.ticks=element_blank(),

-          panel.background=element_blank(),

-          legend.key=element_blank(),

-          legend.box='horizontal')

-  if(size == 0)

-    g <- g + theme(axis.text.x=element_blank())

-  else

-    g <- g + theme(axis.text.x=element_text(angle=90, hjust=1, size=size))

+    g <- ggplot(cor_df, aes_string(x='gene1', y='gene2', fill='value')) +

+        geom_raster() +

+        scale_fill_gradientn(colors=rgb.tables(10), name=cor_name)  +

+        labs(x='', y='') +

+        theme(axis.line=element_blank(),

+              axis.text.y=element_blank(),

+              axis.ticks=element_blank(),

+              panel.background=element_blank(),

+              legend.key=element_blank(),

+              legend.box='horizontal')

+    if(size == 0)

+        g <- g + theme(axis.text.x=element_blank())

+    else

+        g <- g + theme(axis.text.x=element_text(angle=90, hjust=1, size=size))

-  if(plot_sidebar)

-  {

-    gbuild <- ggplot_build(plot = g)

-    y_range <- diff(x = gbuild$layout$panel_params[[1]]$y.range)

-    y_min <- max(gbuild$layout$panel_params[[1]]$y.range) + 0.01 * y_range

-    y_max <- y_min + 0.03 * y_range

+    if(plot_sidebar)

+    {

+        gbuild <- ggplot_build(plot = g)

+        y_range <- diff(x = gbuild$layout$panel_params[[1]]$y.range)

+        y_min <- max(gbuild$layout$panel_params[[1]]$y.range) + 0.01 * y_range

+        y_max <- y_min + 0.03 * y_range

-    g <- g + geom_point(aes_string(color='group'), alpha=0, shape=15, size=5) +

-      scale_color_manual(name=group, values=color_legend)  +

-      guides(color=guide_legend(override.aes=list(alpha=1))) +

-      annotation_raster(t(color_legend[as.character(group_genes)]), -Inf, Inf, ymin=y_min, ymax=y_max) +

-      coord_cartesian(ylim = c(0, y_max), clip='off')

-  }

+        g <- g + geom_point(aes_string(color='group'), alpha=0, shape=15, size=5) +

+            scale_color_manual(name=group, values=color_legend)  +

+            guides(color=guide_legend(override.aes=list(alpha=1))) +

+            annotation_raster(t(color_legend[as.character(group_genes)]), -Inf, Inf, ymin=y_min, ymax=y_max) +

+            coord_cartesian(ylim = c(0, y_max), clip='off')

+    }

-  return(g)

+    return(g)

 }

new file mode 100644

@@ -0,0 +1,461 @@

+#' Line plot for expression traces

+#'

+#' Plot expression traces for genes across sections in a \code{SummarizedExperiment} object.

+#'

+#' @param object A \code{SummarizedExperiment} object.

+#' @param genes A character vector of gene names for plotting expression traces.

+#' @param matrix Character, must be one of \code{"count"}, \code{"normalized"}, or \code{"scaled"}.

+#' @param facet Logical. Plot the expression trace of each gene in a facet if it is \code{TRUE}.

+#' @param span Numeric, the amount of smoothing for the default loess smoother. Smaller numbers produce wigglier lines, larger numbers produce smoother lines. Set it to 0 for non-smoothing lines.

+#'

+#' @return A \code{ggplot} object.

+#'

+#' @seealso \code{geom_smooth} for plotting smooth lines, \code{facet_wrap} for faceting genes.

+#'

+#' @importFrom ggplot2 ggplot aes_string theme theme_bw geom_smooth geom_line labs facet_wrap

+#' @importFrom SummarizedExperiment rowData colData

+#' @export

+#'

+#' @examples

+#' data(zh.data)

+#' zh <- CreateTomo(zh.data)

+#' zh <- Normalize(zh)

+#' LinePlot(zh,

+#'  c("ENSDARG00000002131", "ENSDARG00000003061", "ENSDARG00000076075", "ENSDARG00000076850"))

+#'

+#' # Do not smooth lines.

+#' LinePlot(zh,

+#'  c("ENSDARG00000002131", "ENSDARG00000003061", "ENSDARG00000076075", "ENSDARG00000076850"), span=0)

+#'

+#' # Plot genes in different facets.

+#' LinePlot(zh,

+#'  c("ENSDARG00000002131", "ENSDARG00000003061", "ENSDARG00000076075", "ENSDARG00000076850"),

+#'  facet=TRUE)

+LinePlot <- function(object, genes, matrix='normalized', facet=FALSE, span=0.3)

+{

+  exp_matrix <- GetMatrix(object, matrix)

+  exp_genes <- subset(exp_matrix, rowData(object)$gene %in% genes)

+  exp_genes_df <- NULL

+  sections <- factor(colData(object)$section, levels=colData(object)$section)

+  for(i in 1:nrow(exp_genes))

+  {

+    exp_gene_df <- data.frame(gene=genes[i], section=sections, value=exp_genes[i,])

+    exp_genes_df <- rbind(exp_genes_df, exp_gene_df)

+  }

+  ylab <- c('Count', 'Normalized count', 'Scaled expression')[c('count', 'normalized', 'scaled') == matrix]

+

+  g <- ggplot(exp_genes_df, aes_string(x='section', y='value', group='gene', color='gene')) +

+    labs(x='', y=ylab) +

+    theme_bw() +

+    theme(legend.position='top', axis.text.x=element_text(angle=90, hjust=1, vjust=0.5))

+

+  if(span > 0)

+    g <- g + geom_smooth(method='loess', span = span, se=F)

+  else