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-<!--

-%\VignetteEngine{knitr}

-%\VignetteIndexEntry{9. More Examples}

-

-More Examples of Making Complex Heatmaps

-========================================

-

-**Author**: Zuguang Gu ( z.gu@dkfz.de )

-

-**Date**: `r Sys.Date()`

-

-

-```{r global_settings, echo = FALSE, message = FALSE}

-library(markdown)

-

-library(knitr)

-knitr::opts_chunk$set(

-    error = FALSE,

-    tidy  = FALSE,

-    message = FALSE,

-    fig.align = "center",

-    fig.width = 5,

-    fig.height = 5)

-options(markdown.HTML.stylesheet = "custom.css")

-

-options(width = 100)

-```

-

-In the supplementaries of [the ComplexHeatmap paper](http://bioinformatics.oxfordjournals.org/content/early/2016/05/20/bioinformatics.btw313.abstract), there are four comprehensive examples which are applied

-on real-world high-throughput datasets. [The examples can be found here.](http://jokergoo.github.io/supplementary/ComplexHeatmap-supplementary1-4/index.html)

-

-Also [my blog](http://jokergoo.github.io/blog.html) has some examples and tips for making better complex heatmaps.

-

-

-### Add more information for gene expression matrix

-

-Heatmaps are very popular to visualize gene expression matrix. 

-Rows in the matrix correspond to genes and more information on these genes can be attached after the expression

-heatmap.

-

-In following example, the big heatmap visualize relative expression for genes, then the next is the absolute expression.

-Also gene length and gene type (i.e. protein coding or lincRNA) are visualized.

-

-

-```{r expression_example, fig.width = 10, fig.height = 8}

-library(ComplexHeatmap)

-library(circlize)

-

-expr = readRDS(paste0(system.file(package = "ComplexHeatmap"), "/extdata/gene_expression.rds"))

-mat = as.matrix(expr[, grep("cell", colnames(expr))])

-base_mean = rowMeans(mat)

-mat_scaled = t(apply(mat, 1, scale))

-

-type = gsub("s\\d+_", "", colnames(mat))

-ha = HeatmapAnnotation(df = data.frame(type = type))

-

-Heatmap(mat_scaled, name = "expression", km = 5, col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")),

-    top_annotation = ha, top_annotation_height = unit(4, "mm"), 

-    show_row_names = FALSE, show_column_names = FALSE) +

-Heatmap(base_mean, name = "base_mean", show_row_names = FALSE, width = unit(5, "mm")) +

-Heatmap(expr$length, name = "length", col = colorRamp2(c(0, 1000000), c("white", "orange")),

-    heatmap_legend_param = list(at = c(0, 200000, 400000, 60000, 800000, 1000000), 

-                                labels = c("0kb", "200kb", "400kb", "600kb", "800kb", "1mb")),

-    width = unit(5, "mm")) +

-Heatmap(expr$type, name = "type", width = unit(5, "mm"))

-```

-

-### Visualize genomic regions and other correspondance

-

-Following example visualizes correlation between methylation and expression, as well as other annotation information (data are randomly generated). In the heatmap, each row corresponds to a differentially methylated regions (DMRs). 

-From left to right, heatmaps are:

-

-1. methylation for each DMR (by rows) in samples.

-2. direction of the methylation (one column heatmap), i.e. is methylation hyper in tumor or hypo?

-3. expression for the genes that are associated with corresponding DMRs (e.g. closest gene).

-4. significance for the correlation between methylation and expression (-log10(p-value)).

-5. type of genes, i.e. is the gene a protein coding gene or a lincRNA?

-6. annotation to gene models, i.e. is the DMR located in the intragenic region of the corresponding gene or the DMR is intergenic?

-7. distance from the DMR to the TSS of the corresponding gene.

-8. overlapping between DMRs and enhancers (Color shows how much the DMR is covered by the enhancers).

-

-

-```{r, fig.width = 10, fig.height = 8, echo = FALSE, results = "hide"}

-library(circlize)

-library(RColorBrewer)

-

-lt = readRDS(paste0(system.file(package = "ComplexHeatmap"), "/extdata/meth.rds"))

-list2env(lt, envir = environment())

-

-ha = HeatmapAnnotation(df = data.frame(type = c(rep("Tumor", 10), rep("Control", 10))), 

-    col = list(type = c("Tumor" = "red", "Control" = "blue")))

-ha2 = HeatmapAnnotation(df = data.frame(type = c(rep("Tumor", 10), rep("Control", 10))), 

-    col = list(type = c("Tumor" = "red", "Control" = "blue")), show_legend = FALSE)

-

-# column order of the methylation matrix which will be assigned to the expressio matrix

-column_tree = hclust(dist(t(meth)))

-

-ht_list = 

-    Heatmap(meth, name = "methylation", col = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red")),

-        cluster_columns = column_tree, top_annotation = ha, column_names_gp = gpar(fontsize = 8), km = 5, 

-        column_title = "Methylation", column_title_gp = gpar(fontsize = 10), 

-        row_title_gp = gpar(fontsize = 10)) +

-    Heatmap(direction, name = "direction", col = c("hyper" = "red", "hypo" = "blue"), 

-        column_names_gp = gpar(fontsize = 8)) +

-    Heatmap(expr[, column_tree$order], name = "expression", col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")),

-        cluster_columns = FALSE, top_annotation = ha2, column_names_gp = gpar(fontsize = 8), 

-        column_title = "Expression", column_title_gp = gpar(fontsize = 10)) +

-    Heatmap(cor_pvalue, name = "-log10(cor_p)", col = colorRamp2(c(0, 2, 4), c("white", "white", "red")), 

-        column_names_gp = gpar(fontsize = 8)) +

-    Heatmap(gene_type, name = "gene type", col = brewer.pal(length(unique(gene_type)), "Set1"), 

-        column_names_gp = gpar(fontsize = 8)) +

-    Heatmap(anno, name = "anno_gene", col = brewer.pal(length(unique(anno)), "Set2"), 

-        column_names_gp = gpar(fontsize = 8)) +

-    Heatmap(dist, name = "dist_tss", col = colorRamp2(c(0, 10000), c("black", "white")), 

-        column_names_gp = gpar(fontsize = 8)) +

-    Heatmap(enhancer, name = "anno_enhancer", col = colorRamp2(c(0, 1), c("white", "orange")), 

-        cluster_columns = FALSE, column_names_gp = gpar(fontsize = 8), column_title = "Enhancer", 

-        column_title_gp = gpar(fontsize = 10))

-

-ht_global_opt(heatmap_legend_title_gp = gpar(fontsize = 8, fontface = "bold"), 

-              heatmap_legend_labels_gp = gpar(fontsize = 8))

-draw(ht_list, newpage = FALSE, column_title = "Correspondence between methylation, expression and other genomic features", 

-    column_title_gp = gpar(fontsize = 12, fontface = "bold"), heatmap_legend_side = "bottom")

-invisible(ht_global_opt(RESET = TRUE))

-```

-

-

-## Combine pvclust and heatmap

-

-**pvclust** package provides a robust way to test the stability of the clustering

-by random sampling from original data. Here you can organize the heatmap by the clustering

-returned from `pvclust()`.

-

-```{r}

-library(ComplexHeatmap)

-

-library(MASS)

-library(pvclust)

-data(Boston)

-boston.pv <- pvclust(Boston, nboot=100)

-plot(boston.pv)

-```

-

-Since by default `pvclust` clusters columns by 'correlation' method, we scale columns for

-`Boston` data set to see the relative trend.

-

-```{r}

-Boston_scaled = apply(Boston, 2, scale)

-Heatmap(Boston_scaled, cluster_columns = boston.pv$hclust, heatmap_legend_param = list(title = "Boston"))

-```

-

-## Make a same plot as heatmap()

-

-```{r}

-set.seed(123)

-mat = matrix(rnorm(100), 10)

-heatmap(mat, col = topo.colors(50))

-```

-

-Compare to the native `heatmap()`, `Heatmap()` can give more accurate interpolation

-for colors for continous values.

-

-```{r}

-Heatmap(mat, col = topo.colors(50), color_space = "sRGB",

-    row_dend_width = unit(2, "cm"), 

-    column_dend_height = unit(2, "cm"), row_dend_reorder = TRUE,

-    column_dend_reorder = TRUE)

-```

-

-## The measles vaccine heatmap

-

-Following code reproduces the heatmap introduced [here](https://biomickwatson.wordpress.com/2015/04/09/recreating-a-famous-visualisation/) and [here](https://benjaminlmoore.wordpress.com/2015/04/09/recreating-the-vaccination-heatmaps-in-r/).

-

-```{r, fig.width = 10, fig.height = 8}

-mat = readRDS(paste0(system.file("extdata", package = "ComplexHeatmap"), "/measles.rds"))

-ha1 = HeatmapAnnotation(dist1 = anno_barplot(colSums(mat), bar_width = 1, gp = gpar(col = NA, fill = "#FFE200"), 

-    border = FALSE, axis = TRUE))

-ha2 = rowAnnotation(dist2 = anno_barplot(rowSums(mat), bar_width = 1, gp = gpar(col = NA, fill = "#FFE200"), 

-    border = FALSE, which = "row", axis = TRUE), width = unit(1, "cm"))

-ha_column = HeatmapAnnotation(cn = function(index) {

-    year = as.numeric(colnames(mat))

-    which_decade = which(year %% 10 == 0)

-    grid.text(year[which_decade], which_decade/length(year), 1, just = c("center", "top"))

-})

-Heatmap(mat, name = "cases", col = colorRamp2(c(0, 800, 1000, 127000), c("white", "cornflowerblue", "yellow", "red")),

-    cluster_columns = FALSE, show_row_dend = FALSE, rect_gp = gpar(col= "white"), show_column_names = FALSE,

-    row_names_side = "left", row_names_gp = gpar(fontsize = 10),

-    column_title = 'Measles cases in US states 1930-2001\nVaccine introduced 1961',

-    top_annotation = ha1, top_annotation_height = unit(1, "cm"),

-    bottom_annotation = ha_column, bottom_annotation_height = grobHeight(textGrob("1900"))) + ha2

-

-decorate_heatmap_body("cases", {

-    i = which(colnames(mat) == "1961")

-    x = i/ncol(mat)

-    grid.lines(c(x, x), c(0, 1), gp = gpar(lwd = 2))

-    grid.text("Vaccine introduced", x, unit(1, "npc") + unit(5, "mm"))

-})

-```

-

-## What if my annotation name is too long?

-

-There is no space allocated for annotation name, but when the annotation name is too long,

-you can add paddings of the whole plot to give empty spaces for the annotation names.

-

-```{r, fig.width = 7}

-ha = HeatmapAnnotation(df = data.frame(a_long_long_long_annotation_name = runif(10)),

-    show_legend = FALSE)

-ht = Heatmap(matrix(rnorm(100), 10), name = "foo", top_annotation = ha)

-# because the default width for row cluster is 1cm

-padding = unit.c(unit(2, "mm"), grobWidth(textGrob("a_long_long_long_annotation_name")) - unit(1, "cm"),

-    unit(c(2, 2), "mm"))

-draw(ht, padding = padding)

-decorate_annotation("a_long_long_long_annotation_name", {

-    grid.text("a_long_long_long_annotation_name", 0, 0.5, just = "right")

-})

-```

-

-

-## Session info

-

-```{r}

-sessionInfo()

-```

new file mode 100755

@@ -0,0 +1,225 @@

+<!--

+%\VignetteEngine{knitr}

+%\VignetteIndexEntry{9. More Examples}

+-->

+

+More Examples of Making Complex Heatmaps

+========================================

+

+**Author**: Zuguang Gu ( z.gu@dkfz.de )

+

+**Date**: `r Sys.Date()`

+

+-------------------------------------------------------------

+

+```{r global_settings, echo = FALSE, message = FALSE}

+library(markdown)

+

+library(knitr)

+knitr::opts_chunk$set(

+    error = FALSE,

+    tidy  = FALSE,

+    message = FALSE,

+    fig.align = "center",

+    fig.width = 5,

+    fig.height = 5)

+options(markdown.HTML.stylesheet = "custom.css")

+

+options(width = 100)

+```

+

+In the supplementaries of [the ComplexHeatmap paper](http://bioinformatics.oxfordjournals.org/content/early/2016/05/20/bioinformatics.btw313.abstract), there are four comprehensive examples which are applied

+on real-world high-throughput datasets. [The examples can be found here.](http://jokergoo.github.io/supplementary/ComplexHeatmap-supplementary1-4/index.html)

+

+Also [my blog](http://jokergoo.github.io/blog.html) has some examples and tips for making better complex heatmaps.

+

+

+### Add more information for gene expression matrix

+

+Heatmaps are very popular to visualize gene expression matrix. 

+Rows in the matrix correspond to genes and more information on these genes can be attached after the expression

+heatmap.

+

+In following example, the big heatmap visualize relative expression for genes, then the next is the absolute expression.

+Also gene length and gene type (i.e. protein coding or lincRNA) are visualized.

+

+

+```{r expression_example, fig.width = 10, fig.height = 8}

+library(ComplexHeatmap)

+library(circlize)

+

+expr = readRDS(paste0(system.file(package = "ComplexHeatmap"), "/extdata/gene_expression.rds"))

+mat = as.matrix(expr[, grep("cell", colnames(expr))])

+base_mean = rowMeans(mat)

+mat_scaled = t(apply(mat, 1, scale))

+

+type = gsub("s\\d+_", "", colnames(mat))

+ha = HeatmapAnnotation(df = data.frame(type = type))

+

+Heatmap(mat_scaled, name = "expression", km = 5, col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")),

+    top_annotation = ha, top_annotation_height = unit(4, "mm"), 

+    show_row_names = FALSE, show_column_names = FALSE) +

+Heatmap(base_mean, name = "base_mean", show_row_names = FALSE, width = unit(5, "mm")) +

+Heatmap(expr$length, name = "length", col = colorRamp2(c(0, 1000000), c("white", "orange")),

+    heatmap_legend_param = list(at = c(0, 200000, 400000, 60000, 800000, 1000000), 

+                                labels = c("0kb", "200kb", "400kb", "600kb", "800kb", "1mb")),

+    width = unit(5, "mm")) +

+Heatmap(expr$type, name = "type", width = unit(5, "mm"))

+```

+

+### Visualize genomic regions and other correspondance

+

+Following example visualizes correlation between methylation and expression, as well as other annotation information (data are randomly generated). In the heatmap, each row corresponds to a differentially methylated regions (DMRs). 

+From left to right, heatmaps are:

+

+1. methylation for each DMR (by rows) in samples.

+2. direction of the methylation (one column heatmap), i.e. is methylation hyper in tumor or hypo?

+3. expression for the genes that are associated with corresponding DMRs (e.g. closest gene).

+4. significance for the correlation between methylation and expression (-log10(p-value)).

+5. type of genes, i.e. is the gene a protein coding gene or a lincRNA?

+6. annotation to gene models, i.e. is the DMR located in the intragenic region of the corresponding gene or the DMR is intergenic?

+7. distance from the DMR to the TSS of the corresponding gene.

+8. overlapping between DMRs and enhancers (Color shows how much the DMR is covered by the enhancers).

+

+

+```{r, fig.width = 10, fig.height = 8, echo = FALSE, results = "hide"}

+library(circlize)

+library(RColorBrewer)

+

+lt = readRDS(paste0(system.file(package = "ComplexHeatmap"), "/extdata/meth.rds"))

+list2env(lt, envir = environment())

+

+ha = HeatmapAnnotation(df = data.frame(type = c(rep("Tumor", 10), rep("Control", 10))), 

+    col = list(type = c("Tumor" = "red", "Control" = "blue")))

+ha2 = HeatmapAnnotation(df = data.frame(type = c(rep("Tumor", 10), rep("Control", 10))), 

+    col = list(type = c("Tumor" = "red", "Control" = "blue")), show_legend = FALSE)

+

+# column order of the methylation matrix which will be assigned to the expressio matrix

+column_tree = hclust(dist(t(meth)))

+

+ht_list = 

+    Heatmap(meth, name = "methylation", col = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red")),

+        cluster_columns = column_tree, top_annotation = ha, column_names_gp = gpar(fontsize = 8), km = 5, 

+        column_title = "Methylation", column_title_gp = gpar(fontsize = 10), 

+        row_title_gp = gpar(fontsize = 10)) +

+    Heatmap(direction, name = "direction", col = c("hyper" = "red", "hypo" = "blue"), 

+        column_names_gp = gpar(fontsize = 8)) +

+    Heatmap(expr[, column_tree$order], name = "expression", col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")),

+        cluster_columns = FALSE, top_annotation = ha2, column_names_gp = gpar(fontsize = 8), 

+        column_title = "Expression", column_title_gp = gpar(fontsize = 10)) +

+    Heatmap(cor_pvalue, name = "-log10(cor_p)", col = colorRamp2(c(0, 2, 4), c("white", "white", "red")), 

+        column_names_gp = gpar(fontsize = 8)) +

+    Heatmap(gene_type, name = "gene type", col = brewer.pal(length(unique(gene_type)), "Set1"), 

+        column_names_gp = gpar(fontsize = 8)) +

+    Heatmap(anno, name = "anno_gene", col = brewer.pal(length(unique(anno)), "Set2"), 

+        column_names_gp = gpar(fontsize = 8)) +

+    Heatmap(dist, name = "dist_tss", col = colorRamp2(c(0, 10000), c("black", "white")), 

+        column_names_gp = gpar(fontsize = 8)) +

+    Heatmap(enhancer, name = "anno_enhancer", col = colorRamp2(c(0, 1), c("white", "orange")), 

+        cluster_columns = FALSE, column_names_gp = gpar(fontsize = 8), column_title = "Enhancer", 

+        column_title_gp = gpar(fontsize = 10))

+

+ht_global_opt(heatmap_legend_title_gp = gpar(fontsize = 8, fontface = "bold"), 

+              heatmap_legend_labels_gp = gpar(fontsize = 8))

+draw(ht_list, newpage = FALSE, column_title = "Correspondence between methylation, expression and other genomic features", 

+    column_title_gp = gpar(fontsize = 12, fontface = "bold"), heatmap_legend_side = "bottom")

+invisible(ht_global_opt(RESET = TRUE))

+```

+

+

+## Combine pvclust and heatmap

+

+**pvclust** package provides a robust way to test the stability of the clustering

+by random sampling from original data. Here you can organize the heatmap by the clustering

+returned from `pvclust()`.

+

+```{r}

+library(ComplexHeatmap)

+

+library(MASS)

+library(pvclust)

+data(Boston)

+boston.pv <- pvclust(Boston, nboot=100)

+plot(boston.pv)

+```

+

+Since by default `pvclust` clusters columns by 'correlation' method, we scale columns for

+`Boston` data set to see the relative trend.

+

+```{r}

+Boston_scaled = apply(Boston, 2, scale)

+Heatmap(Boston_scaled, cluster_columns = boston.pv$hclust, heatmap_legend_param = list(title = "Boston"))

+```

+

+## Make a same plot as heatmap()

+

+```{r}

+set.seed(123)

+mat = matrix(rnorm(100), 10)

+heatmap(mat, col = topo.colors(50))

+```

+

+Compare to the native `heatmap()`, `Heatmap()` can give more accurate interpolation

+for colors for continous values.

+

+```{r}

+Heatmap(mat, col = topo.colors(50), color_space = "sRGB",

+    row_dend_width = unit(2, "cm"), 

+    column_dend_height = unit(2, "cm"), row_dend_reorder = TRUE,

+    column_dend_reorder = TRUE)

+```

+

+## The measles vaccine heatmap

+

+Following code reproduces the heatmap introduced [here](https://biomickwatson.wordpress.com/2015/04/09/recreating-a-famous-visualisation/) and [here](https://benjaminlmoore.wordpress.com/2015/04/09/recreating-the-vaccination-heatmaps-in-r/).

+

+```{r, fig.width = 10, fig.height = 8}

+mat = readRDS(paste0(system.file("extdata", package = "ComplexHeatmap"), "/measles.rds"))

+ha1 = HeatmapAnnotation(dist1 = anno_barplot(colSums(mat), bar_width = 1, gp = gpar(col = NA, fill = "#FFE200"), 

+    border = FALSE, axis = TRUE))

+ha2 = rowAnnotation(dist2 = anno_barplot(rowSums(mat), bar_width = 1, gp = gpar(col = NA, fill = "#FFE200"), 

+    border = FALSE, which = "row", axis = TRUE), width = unit(1, "cm"))

+ha_column = HeatmapAnnotation(cn = function(index) {

+    year = as.numeric(colnames(mat))

+    which_decade = which(year %% 10 == 0)

+    grid.text(year[which_decade], which_decade/length(year), 1, just = c("center", "top"))

+})

+Heatmap(mat, name = "cases", col = colorRamp2(c(0, 800, 1000, 127000), c("white", "cornflowerblue", "yellow", "red")),

+    cluster_columns = FALSE, show_row_dend = FALSE, rect_gp = gpar(col= "white"), show_column_names = FALSE,

+    row_names_side = "left", row_names_gp = gpar(fontsize = 10),

+    column_title = 'Measles cases in US states 1930-2001\nVaccine introduced 1961',

+    top_annotation = ha1, top_annotation_height = unit(1, "cm"),

+    bottom_annotation = ha_column, bottom_annotation_height = grobHeight(textGrob("1900"))) + ha2

+

+decorate_heatmap_body("cases", {

+    i = which(colnames(mat) == "1961")

+    x = i/ncol(mat)

+    grid.lines(c(x, x), c(0, 1), gp = gpar(lwd = 2))

+    grid.text("Vaccine introduced", x, unit(1, "npc") + unit(5, "mm"))

+})

+```

+

+## What if my annotation name is too long?

+

+There is no space allocated for annotation name, but when the annotation name is too long,

+you can add paddings of the whole plot to give empty spaces for the annotation names.

+

+```{r, fig.width = 7}

+ha = HeatmapAnnotation(df = data.frame(a_long_long_long_annotation_name = runif(10)),

+    show_legend = FALSE)

+ht = Heatmap(matrix(rnorm(100), 10), name = "foo", top_annotation = ha)

+# because the default width for row cluster is 1cm

+padding = unit.c(unit(2, "mm"), grobWidth(textGrob("a_long_long_long_annotation_name")) - unit(1, "cm"),

+    unit(c(2, 2), "mm"))

+draw(ht, padding = padding)

+decorate_annotation("a_long_long_long_annotation_name", {

+    grid.text("a_long_long_long_annotation_name", 0, 0.5, just = "right")

+})

+```

+

+

+## Session info

+

+```{r}

+sessionInfo()

+```