#' ggplot theme in scAI
#'
#' @return
#' @export
#'
#' @examples
#' @importFrom ggplot2 theme_classic element_rect theme element_blank element_line element_text
scAI_theme_opts <- function() {
theme(strip.background = element_rect(colour = "white", fill = "white")) +
theme_classic() +
theme(panel.border = element_blank()) +
theme(axis.line.x = element_line(color = "black")) +
theme(axis.line.y = element_line(color = "black")) +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank()) +
theme(panel.background = element_rect(fill = "white")) +
theme(legend.key = element_blank()) + theme(plot.title = element_text(size = 10, face = "bold", hjust = 0.5))
}
#' Visualize the inferred biologically relevant factors
#' We plot the heatmap of the three learned low-rank matrices using hierarchical clustering.
#' @param object scAI object
#' @param color.by the name of the variable in object.pData; defining cell groups (not necessary)
#' @param colors.use defined colors of the cell groups
#' @param do.sampling whether perform sampling of loci when generating heatmap of the loci-factor matrix
#'
#' @return
#' @export
#'
#' @examples
#' @importFrom ComplexHeatmap Heatmap HeatmapAnnotation draw
#' @importFrom stats setNames
#' @importFrom grid grid.grabExpr grid.newpage pushViewport grid.draw unit gpar viewport popViewport
lmHeatmap <- function(object, color.by, colors.use = NULL,do.sampling = T ){
H <- as.matrix(object@fit$H)
H <- sweep(H,2,colSums(H),FUN = `/`)
label <- object@pData[[color.by]]
df<- data.frame(group = label); rownames(df) <- colnames(H)
if (is.null(colors.use)) {
colors.use <- scPalette(length(unique(label)))
}
cell.cols.assigned <- setNames(colors.use, unique(as.character(df$group)))
col_annotation <- HeatmapAnnotation(df = df, col = list(group = cell.cols.assigned),annotation_name_side = "left",simple_anno_size = grid::unit(0.2, "cm"))
colormap = structure(rev(brewer.pal(9,"RdBu")))
H = H + runif(length(H), min = -0.5, max = 0.5)*1e-5
ht1 = Heatmap(H,name = "H",
clustering_method_columns = "average",
clustering_distance_columns = "euclidean",
col = colormap,
cluster_rows = FALSE, show_column_names = FALSE, show_row_names = TRUE, row_names_side = "left", row_names_rot = 0,row_names_gp = gpar(fontsize = 10),
width = unit(6, "cm"), height = unit(4, "cm"),
top_annotation = col_annotation,
column_title = "Cell loading matrix",
column_title_gp = gpar(fontsize = 10, fontface = "bold"),
heatmap_legend_param = list(title = "H", at = c(0, 0.5, 1),legend_width = unit(0.0001, "cm"),legend_height = unit(2, "cm"),labels_gp = gpar(font = 6))
)
# heatmap for W1
W1 <- as.matrix(object@fit$W[[1]])
W1 <- sweep(W1,1,rowSums(W1),FUN = `/`)
W1[is.na(W1)] <- 0
colormap = structure(rev(brewer.pal(11,"RdBu")))
W1 = W1 + runif(length(W1), min = -0.5, max = 0.5)*1e-5
ht2 = Heatmap(W1,name = "W1",
clustering_method_rows = "average",
col = colormap,
cluster_columns = FALSE, show_column_names = T, show_row_names = F, column_names_gp = gpar(fontsize = 10),
width = unit(4, "cm"), height = unit(8, "cm"),
column_title = "Gene loading matrix (scRNA-seq)",
column_title_gp = gpar(fontsize = 10, fontface = "bold"),
row_title = "Genes", row_title_rot = 90,row_names_gp = gpar(fontsize = 10),
heatmap_legend_param = list(title = "W1", at = c(0, 0.5, 1),legend_width = unit(0.0001, "cm"),legend_height = unit(2, "cm"),labels_gp = gpar(font = 6))
)
# heatmap for W1
W2 <- as.matrix(object@fit$W[[2]])
W2 <- sweep(W2,1,rowSums(W2),FUN = `/`)
W2[is.na(W2)] <- 0
if (nrow(W2) > 5000 & do.sampling) {
loci.use <- sample(1:nrow(W2), 5000, replace=F)
W2 <- W2[sort(loci.use),]
}
colormap = structure(rev(brewer.pal(9,"Spectral")))
W2 = W2 + runif(length(W2), min = -0.5, max = 0.5)*1e-5
ht3 = Heatmap(W2,name = "W2",
clustering_method_rows = "average",
col = colormap,
cluster_columns = FALSE, show_column_names = T, show_row_names = F, column_names_gp = gpar(fontsize = 10),
width = unit(4, "cm"), height = unit(8, "cm"),
column_title = "Locus loading matrix (scATAC-seq)",
column_title_gp = gpar(fontsize = 10, fontface = "bold"),
row_title = "Loci", row_title_rot = 90,row_names_gp = gpar(fontsize = 10),
heatmap_legend_param = list(title = "W2", at = c(0, 0.5, 1),legend_width = unit(0.0001, "cm"),legend_height = unit(2, "cm"),labels_gp = gpar(font = 6))
)
gb_ht1 = grid::grid.grabExpr(draw(ht1))
gb_ht2 = grid::grid.grabExpr(draw(ht2))
gb_ht3 = grid::grid.grabExpr(draw(ht3))
grid::grid.newpage()
grid::pushViewport(viewport(x = 0.2,y = 1, width = 0.5, height = 0.3, just = c("left", "top")))
grid::grid.draw(gb_ht1)
grid::popViewport()
grid::pushViewport(viewport(x = 0.1, y = 0.1, width = 0.2, height = 0.5, just = c("left", "bottom")))
grid::grid.draw(gb_ht2)
grid::popViewport()
grid::pushViewport(viewport(x = 0.5, y = 0.1, width = 0.2, height = 0.5, just = c("left", "bottom")))
grid::grid.draw(gb_ht3)
grid::popViewport()
}
#' visualize cells in 2D-dimensional space
#'
#' @param object scAI object
#' @param cell_coords 2D embedding coordinates of cells
#' @param color.by the name of the variable in pData, defining cell groups, cells are colored based on the labels
#' @param labels.order defining the factor level of cell groups
#' @param colors.use defining the color for each cell group
#' @param brewer.use use RColorBrewer palette instead of default ggplot2 color
#' @param xlabel label of x-axis
#' @param ylabel label of y-axis
#' @param title main title of the plot
#' @param label.size font size of the legend
#' @param cell.size size of the dots
#' @param font.size font size
#' @param do.label label the cluster in 2D space
#' @param show.legend whether show the legend
#' @param show.axes whether show the axes
#'
#' @return ggplot2 object with 2D plot
#' @export
#'
#' @examples
#' @importFrom ggplot2 ggplot geom_point aes scale_color_manual facet_wrap element_text theme guides element_blank element_rect geom_line
#' @importFrom ggrepel geom_text_repel
#' @importFrom dplyr %>% summarize
#' @importFrom RColorBrewer brewer.pal
#' @importFrom grDevices colorRampPalette
#' @importFrom stats median
cellVisualization <- function(object, cell_coords, color.by, labels.order = NULL, colors.use = NULL, brewer.use = FALSE,
xlabel = "UMAP1", ylabel = "UMAP2", title = NULL,
label.size = 4, cell.size = 0.3, font.size = 10, do.label = F, show.legend = T, show.axes = T) {
labels <- object@pData[[color.by]]
if (is.null(labels.order) == FALSE) {
labels <- factor(labels, levels = labels.order)
} else if (class(labels) != "factor") {
labels <- as.factor(labels)
}
df <- data.frame(x = cell_coords[, 1], y = cell_coords[, 2], group = labels)
gg <- ggplot(data = df, aes(x, y)) +
geom_point(aes(colour = labels), size = cell.size) + scAI_theme_opts() +
theme(text = element_text(size = 10)) + labs(title = title, x = xlabel, y = ylabel) +
guides(colour = guide_legend(override.aes = list(size = label.size))) +
theme(legend.title = element_blank())
numCluster = length(unique((labels)))
if (is.null(colors.use)) {
colors <- scPalette(numCluster)
names(colors) <- levels(labels)
gg <- gg + scale_color_manual(values = colors)
if (brewer.use) {
if (numCluster < 9) {
colors <- RColorBrewer::brewer.pal(numCluster, "Set1")
} else {
colors <- grDevices::colorRampPalette(RColorBrewer::brewer.pal(9, "Set1"))(numCluster)
}
names(colors) <- levels(labels)
gg <- gg + scale_color_manual(values = colors)
}
} else {
gg <- gg + scale_color_manual(values = colors.use)
}
if (do.label) {
centers <- df %>% dplyr::group_by(group) %>% dplyr::summarize(x = median(x = x), y = median(x = y))
gg <- gg + ggrepel::geom_text_repel(data = centers, mapping = aes(x, y, label = group), size = label.size)
}
if (!show.legend) {
gg <- gg + theme(legend.position = "none")
}
if (!show.axes) {
gg <- gg + theme_void()
}
gg
}
#' Generate colors from a customed color palette
#'
#' @param n number of colors
#'
#' @return A color palette for plotting
#' @importFrom grDevices colorRampPalette
#'
#' @export
#'
scPalette <- function(n) {
colorSpace <- c(
'#E41A1C',
'#377EB8',
'#4DAF4A',
'#984EA3',
'#F29403',
'#F781BF',
'#BC9DCC',
'#A65628',
'#54B0E4',
'#222F75',
'#1B9E77',
'#B2DF8A',
'#E3BE00',
'#FB9A99',
'#E7298A',
'#910241',
'#00CDD1',
'#A6CEE3',
'#CE1261',
'#5E4FA2',
'#8CA77B',
'#00441B',
'#DEDC00',
'#B3DE69',
'#8DD3C7',
'#999999'
)
if (n <= length(colorSpace)) {
colors <- colorSpace[1:n]
} else {
colors <- grDevices::colorRampPalette(colorSpace)(n)
}
return(colors)
}
#' Ranking the features (genes/loci) and show the top markers in each factor
#'
#' @param object scAI object
#' @param assay define an assay to show, e.g., assay = "RNA"
#' @param factor.show a set of factors to show
#' @param feature.show a vector of the features that are labeled on the plot
#' @param ncol number of columns in plot
#' @param feature.show.names instead of the default name in feature.show, one can show the manual feature name such as the enriched motif
#' @param top.p showing the features in top ranking
#' @param features.diff a table includes the differential features, returned from identifyfactorMakrers.R
#' @param ylabel ylabel shown on the y-axis
#'
#' @return
#' @export
#'
#' @examples
featureRankingPlot <- function(object, assay, factor.show = NULL, ncol = NULL, feature.show = NULL, feature.show.names = NULL, top.p = 0.5, features.diff = NULL, ylabel = "Weight") {
W <- object@fit$W[[assay]]
features <- rownames(W)
if (!is.null(factor.show)) {
W <- W[, factor.show]
}
K = ncol(W)
W <- sweep(W,1,rowSums(W),FUN = `/`)
W[is.na(W)] <- 0
Wg <- vector("list", K)
for (i in 1:K) {
W_order <- sort(W[,i],decreasing=F, index.return = T)
features_ordered <- features[W_order$ix]
if (!is.null(features.diff)) {
features.diffi <- as.character(features.diff$features[features.diff$factors == i])
}else {
features.diffi <- as.character(features)
}
if (!is.null(feature.show)) {
features.diffi <- intersect(features.diffi, feature.show)
}
idx <- match(features.diffi, features_ordered)
data_show <- matrix(0, nrow(W), 1); data_show[idx] <- 1
if (!is.null(top.p) & top.p < 1) {
idx_bottom <- seq_len(floor((1-top.p)*nrow(W))); data_show[idx_bottom] <- 0
}
Wg[[i]] <- cbind(Weight = as.numeric(W_order$x), factor = colnames(W)[i], Ranking = seq_len(nrow(W)), Show = as.numeric(data_show), Genes = features_ordered)
}
data <- Wg[[1]]
for (i in 2:K) {
data <- rbind(data, Wg[[i]])
}
df <- as.data.frame(data, stringsAsFactors=FALSE)
colnames(df) <- c("Weight", "factor", "Ranking", "Show","Genes")
df$factor <- paste('Factor',df$factor, sep = " ")
df$Weight <- as.numeric(as.character(df$Weight))
df$Ranking <- as.numeric(as.character(df$Ranking))
df$Show <- as.numeric(as.character(df$Show))
if (!is.null(feature.show.names)) {
idx <- which(df$Genes %in% feature.show)
df$Genes[idx] <- feature.show.names
}
data_topFeature = df[df$Show == 1,]
gg <- ggplot(df, aes(Ranking, Weight)) +
geom_line(colour = "grey80",size = 1) + facet_wrap(~ factor, ncol = ncol, scales = "free")+
scAI_theme_opts()+
theme(text = element_text(size = 10), axis.text.x = element_blank(),axis.ticks.x = element_blank()) +
theme(strip.background = element_rect(fill="grey80")) +
ylab(ylabel) +
geom_point(size = 3, shape = 1, data = data_topFeature) +
ggrepel::geom_text_repel(aes(label = Genes), data = data_topFeature, segment.color = "grey50", segment.alpha = 1,
direction = "y",nudge_x = -150, hjust = 1,size = 3,segment.size = 0.3) # hjust = 1 for right-align
gg
}
#' VscAI visualize the genes, loci and factors that separate cell states on two dimensions alongside the cells
#'
#' @param object scAI object
#' @param gene.use embedded genes
#' @param loci.use embedded loci
#' @param loci.use.names alternative names of embedded loci, e.g, the corresponding motif
#' @param color.by the name of the variable in pData, defining cell groups, cells are colored based on the labels
#' @param labels.order defining the factor level
#' @param colors.use defining the color for each cell group
#' @param brewer.use use RColorBrewer palette instead of default ggplot2 color
#' @param xlabel label of x-axis
#' @param ylabel label of y-axis
#' @param title main title of the plot
#' @param label.size font size of the legend
#' @param cell.size size of the dots
#' @param font.size size of font
#' @param do.label label the cluster in 2D space
#' @param show.legend whether show the legend
#' @param show.axes whether show the axes
#'
#' @return ggplot2 object with 2D plot
#' @export
#'
#' @examples
#' @importFrom ggplot2 guide_legend guides labs element_text theme xlab ylab scale_fill_manual scale_color_manual scale_shape_manual scale_size_manual
VscAIplot <- function(object, gene.use, loci.use, loci.use.names, color.by,
labels.order = NULL, colors.use = NULL, brewer.use = FALSE, xlabel = "VscAI1",
ylabel = "VscAI2", title = NULL, label.size = 3, cell.size = 0.3, font.size = 10,
do.label = T, show.legend = T, show.axes = T) {
cell_coords <- object@embed$VscAI$cells
factor_coords <- object@embed$VscAI$factors
gene_coords <- object@embed$VscAI$genes
loci_coords <- object@embed$VscAI$loci
labels <- object@pData[[color.by]]
if (is.null(labels.order) == FALSE) {
labels <- factor(labels, levels = labels.order)
} else if (class(labels) != "factor") {
labels <- as.factor(labels)
}
df.cell <- data.frame(x = cell_coords[, 1], y = cell_coords[, 2], group = labels)
gg <- ggplot(data = df.cell, aes(x, y)) +
geom_point(aes(colour = labels), size = cell.size) +
scAI_theme_opts() + theme(text = element_text(size = 10)) +
labs(title = title) + xlab(xlabel) + ylab(ylabel) +
guides(colour = guide_legend(override.aes = list(size = 3))) +
guides(fill = guide_legend(title = "Cell groups")) + scale_fill_manual("Cell groups")
numCluster = length(unique((labels)))
if (is.null(colors.use)) {
colors <- scPalette(numCluster)
names(colors) <- levels(labels)
gg <- gg + scale_color_manual(values = colors)
if (brewer.use) {
if (numCluster < 9) {
colors <- RColorBrewer::brewer.pal(numCluster, "Set1")
} else {
colors <- grDevices::colorRampPalette(RColorBrewer::brewer.pal(9, "Set1"))(numCluster)
}
names(colors) <- levels(labels)
gg <- gg + scale_color_manual(values = colors)
}
} else {
gg <- gg + scale_color_manual(values = colors.use)
}
# embedding factors
if (do.label) {
df.factor <- data.frame(factor_coords, label.name = paste0("F", seq_len(length(factor_coords[, 1]))), Embedding = "Factors")
df.features <- df.factor
}
# embedding genes
if (!is.null(gene.use)) {
df.genes <- data.frame(gene_coords[gene.use, ], label.name = gene.use,
Embedding = "Genes")
df.features <- rbind(df.features, df.genes)
}
# embedding loci
if (!is.null(loci.use)) {
df.loci <- data.frame(loci_coords[loci.use, ], label.name = loci.use.names,
Embedding = "Loci")
df.features <- rbind(df.features, df.loci)
}
gg <- gg + geom_point(data = df.features, aes(x, y, shape = Embedding, size = Embedding)) +
scale_shape_manual(values = c(1, 16, 5)) +
scale_size_manual(values = c(3, 2, 2)) +
ggrepel::geom_text_repel(data = df.features, aes(label = label.name), size = label.size,
segment.color = "grey50", segment.size = 0.3, box.padding = grid::unit(0.35, "lines"), point.padding = grid::unit(0.2, "lines"))
if (!show.legend) {
gg <- gg + theme(legend.position = "none")
}
if (!show.axes) {
gg <- gg + theme_void()
}
gg
}
#' visualize cells on the 2D space with gene expression or chromatian accessibility overlayed
#'
#' @param object scAI object
#' @param assay define an assay to show, e.g., assay = "RNA"
#' @param feature.use a vector of features
#' @param method dimensional reduction method, e.g., VscAI, tsne, umap
#' @param nCol number of columns of the plot
#' @param xlabel label shown on x-axis
#' @param ylabel label shown on y-axis
#' @param cell.size the size of points (cells)
#' @param show.legend whether show individual legend
#' @param show.legend.combined whether just show one legend
#' @param show.axes whether show the axes
#'
#' @return
#' @export
#'
#' @examples
#' @importFrom ggplot2 guide_colorbar scale_colour_gradientn
featureVisualization <- function(object, assay, feature.use, method = "VscAI", nCol = NULL,
xlabel = "VscAI1", ylabel = "VscAI2", cell.size = 0.3,
show.legend = T, show.legend.combined = F, show.axes = T) {
data <- object@norm.data[[assay]]
feature.use <- intersect(feature.use, rownames(data))
data.use <- data[feature.use, ]
if (is.null(nCol)) {
if (length(feature.use) > 9) {
nCol <- 4
} else {
nCol <- min(length(feature.use), 3)
}
}
if (method == "VscAI") {
cell_coords <- object@embed$VscAI$cells
} else if (method == "tsne") {
cell_coords <- object@embed$tsne
xlabel = "tSNE1"
ylabel = "tSNE2"
} else if (method == "umap") {
cell_coords <- object@embed$umap
xlabel = "UMAP1"
ylabel = "UMAP2"
}
colormap <- colorRampPalette(c("#FFFFEF", "#FFFF00", "#FF0000", "#0A0000"))(64)
colormap[1] <- "#E5E5E5"
df <- data.frame(x = cell_coords[, 1], y = cell_coords[, 2])
numFeature = length(feature.use)
gg <- vector("list", numFeature)
for (i in seq_len(numFeature)) {
feature.name <- feature.use[i]
df$feature.data <- data.use[i, ]
g <- ggplot(data = df, aes(x, y)) +
geom_point(aes(colour = feature.data), size = cell.size) +
scale_colour_gradientn(colours = colormap, guide = guide_colorbar(title = NULL, ticks = T, label = T, barwidth = 0.5), na.value = "lightgrey") +
labs(title = feature.name) + scAI_theme_opts() +
theme(text = element_text(size = 10), legend.key.height = grid::unit(0.15, "in")) + labs(x = xlabel, y = ylabel)
if (!show.legend) {
g <- g + theme(legend.position = "none")
}
if (show.legend.combined & i == numFeature) {
g <- g + theme(legend.position = "right", legend.key.height = grid::unit(0.15, "in"), legend.key.width = grid::unit(0.5, "in"), legend.title = NULL)
}
if (!show.axes) {
g <- g + theme_void()
}
gg[[i]] <- g
}
gg.combined <- cowplot::plot_grid(plotlist = gg, ncol = nCol)
gg.combined
}
#' visualize cells on the 2D space with features overlayed
#'
#' @param object scAI object
#' @param feature.use a vector of features
#' @param feature.scores a matrix containing the feature scores
#' @param method dimensional reduction method, e.g., VscAI, tsne, umap
#' @param colormap RColorbrewer palette to use
#' @param color.direction Sets the order of colours in the scale. If 1, the default, colours are as output by RColorBrewer::brewer.pal(). If -1, the order of colours is reversed.
#' @param nCol number of columns of the plot
#' @param xlabel label shown on x-axis
#' @param ylabel label shown on y-axis
#' @param cell.size the size of points (cells)
#' @param show.legend whether show individual legend
#' @param show.legend.combined whether just show one legend
#' @param show.axes whether show the axes
#'
#' @return
#' @export
#'
#' @examples
#' @importFrom ggplot2 guide_colorbar scale_color_distiller
featureScoreVisualization <- function(object, feature.use = NULL, feature.scores, method = "VscAI",
colormap = "RdPu", color.direction = 1,
nCol = NULL, xlabel = "VscAI1", ylabel = "VscAI2",
show.axes = T, cell.size = 0.3,
show.legend = T, show.legend.combined = F) {
data.use <- as.matrix(feature.scores[ ,feature.use])
if (is.null(nCol)) {
if (length(feature.use) > 9) {
nCol <- 4
} else {
nCol <- min(length(feature.use), 3)
}
}
if (method == "VscAI") {
cell_coords <- object@embed$VscAI$cells
} else if (method == "tsne") {
cell_coords <- object@embed$tsne
xlabel = "tSNE1"
ylabel = "tSNE2"
} else if (method == "umap") {
cell_coords <- object@embed$umap
xlabel = "UMAP1"
ylabel = "UMAP2"
}
df <- data.frame(x = cell_coords[, 1], y = cell_coords[, 2])
numFeature = length(feature.use)
gg <- vector("list", numFeature)
for (i in seq_len(numFeature)) {
feature.name <- feature.use[i]
df$feature.data <- data.use[ ,i]
g <- ggplot(data = df, aes(x, y)) +
geom_point(aes(colour = feature.data), size = cell.size) +
scale_color_distiller(palette = colormap, direction = color.direction, guide = guide_colorbar(title = NULL, ticks = T, label = T, barwidth = 0.5), na.value = "lightgrey") +
labs(title = feature.name) + scAI_theme_opts() +
theme(text = element_text(size = 10), legend.key.height = grid::unit(0.15, "in")) + labs(x = xlabel, y = ylabel)
if (!show.legend) {
g <- g + theme(legend.position = "none")
}
if (show.legend.combined & i == numFeature) {
g <- g + theme(legend.position = "right", legend.key.height = grid::unit(0.15, "in"), legend.key.width = grid::unit(0.5, "in"), legend.title = NULL)
}
if (!show.axes) {
g <- g + theme_void()
}
gg[[i]] <- g
}
gg.combined <- cowplot::plot_grid(plotlist = gg, ncol = nCol)
gg.combined
}
#' generate a heatmap for the expression of differential features across different cell groups
#'
#' @param object scAI object
#' @param assay define an assay to show, e.g., assay = "RNA"
#' @param feature.use a vector of features to show
#' @param group.by the name of the variable in pData, defining cell groups. cells are grouped together
#' @param color.use colors for the cell clusters
#' @param names.show whether show the feature names
#' @param size.names the font size of the feature names
#' @param use.agg whether use aggregated data
#' @param rescaling whether rescale each feature across all the cells
#'
#' @return
#' @export
#'
#' @examples
#' @importFrom circlize colorRamp2
#' @importFrom ComplexHeatmap Heatmap HeatmapAnnotation
featureHeatmap <- function(object, assay, feature.use, group.by, color.use = NULL, use.agg = TRUE, rescaling = TRUE, names.show = TRUE, size.names = 8) {
if (assay == "RNA") {
data <- object@norm.data[[assay]]
} else {
if (use.agg) {
data <- object@agg.data
} else {
data <- object@norm.data[[assay]]
}
}
groups = object@pData[[group.by]]
feature.use <- feature.use[feature.use %in% rownames(data)]
data.use <- data[feature.use,]
if(rescaling) {
data.use = Matrix::t(scale(Matrix::t(data.use), center = T))
}
data.use <- as.matrix(data.use)
cell.order <- order(groups)
data.use <- data.use[,cell.order]
numCluster <- length(unique(groups))
if (is.null(color.use)) {
color.use <- scPalette(numCluster)
}
colorGate = structure(color.use, names = as.character(levels(groups)))
col_annotation = HeatmapAnnotation(group = sort(groups),col = list(group = colorGate),
annotation_name_side = "left",simple_anno_size = unit(0.2, "cm"))
Heatmap(data.use,name = "zscore",
col = colorRamp2(c(-2, 0, 2), c("#2166ac", "#f7f7f7", "#b2182b"),space = "LAB"),
cluster_rows = FALSE, cluster_columns = FALSE, show_column_names = FALSE,
show_row_names = names.show, row_names_side = "left", row_names_rot = 0,row_names_gp = gpar(fontsize = size.names),
width = unit(6, "cm"),
bottom_annotation = col_annotation,
heatmap_legend_param = list(title = NULL, legend_width = unit(0.0001, "cm"),labels_gp = gpar(font = 6))
)
}
Datasets
Models
