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ComplexHeatmap學習筆記和總結

ComplexHeatmap學習筆記和總結

聲明：

本文檔是在學習ComplexHeatmap和測試例子過程中，相關方法的小結，友善回顧檢視，快速實作資料可視化，了解錯誤之處，歡迎批評指正

1. ComplexHeatmap總覽

ComplexHeatmap軟體包主要用來展現熱圖，常說的熱圖包括，body和components(title， dendrograms, matrix names 和熱圖注釋（放在熱圖旁，該可根據注釋組合不同的複雜圖形）。

heatmap body部分可以根據行和列分割。

整體上講，這個包類似于ggplot，layout操作，不同track和使用+組合，此外可以将注釋圖形直接加入主圖中。

ComplexHeatmap學習筆記和總結

2. 單個熱圖

2.1 input 格式

單個熱圖和pheatmap功能較一緻，且二者可以相容使用

輸入是一個數值矩陣，行和列名可以使用rownames和colnames分别命名

library(circlize)

col_fun = colorRamp2(c(-2, 0, 2), c(“green”, “white”, “red”)) # 矩陣的值會映射到-2和2之間，需要根據實際資料進行調整

colorRamp2() 如果使用同一個mapping color， 允許比較，可以清晰的看出不同處理的資料差異

對于連續型資料，可以提供顔色向量，eg: colorRamp2(seq(min(mat), max(mat), length = 10), rev(rainbow(10))), 會自動mapping，但是會受到離群值影響較大，discrete values（數字或字元）需要提供一個顔色向量

Heatmap(mat, name = "mat", col = col_fun)
Heatmap(mat, name = "mat", col = col_fun, column_title = "mat")
Heatmap(mat/4, name = "mat", col = col_fun, column_title = "mat/4")
Heatmap(abs(mat), name = "mat", col = col_fun, column_title = "abs(mat)")
           
**discrete values：**
discrete_mat = matrix(sample(letters[1:4], 100, replace = TRUE), 10, 10)
colors = structure(1:4, names = letters[1:4])
Heatmap(discrete_mat, name = "mat", col = colors,
    column_title = "a discrete character matrix")

pheatmap 介紹：

pheatmap(test, legend = FALSE)，如下常用參數和作用

border_color： cells 格子的顔色，顔色值

border：邏輯值，是否顯示邊框

show_rownames和show_colnames： 邏輯值，是否顯示名字

display_numbers：邏輯，是否顯示數字

number_format：格式調整

cellwidth 和cellheight： cell的寬和高調整，調整cells的大小

**annotation_col：添加列注釋

annotation_row： 添加行注釋，注意row和col要和矩陣一緻，需要加個對應的rownames和colnames

annotation_colors：各個注釋水準的顔色對應清單, 清單每個元素 var=c(注釋水準="")

gaps_row 和gaps_col：數值向量，提供需要gap的索引，cluster_rows 和cluster_cols需要設定False

fontsize：數值，字型大小

scale：對row進行歸一化

**labels_row：**重點顯示幾個基因名字，類似Mark annotaion, #labels_row = c("", “”, “”, “”, “”, “”, “”, “”, “”, “”, “”, “”, “”, “”, “”,

“”, “”, “Il10”, “Il15”, “Il1b”)

angle_col = 45

fontsize_row和fontsize_col：row和col字型的大小

pheatmap

2.2 顔色選取和相關參數介紹

預設linearly interpolated in LAB color space，但是可以根據資料使用colorRamp2()調整。

f1 = colorRamp2(seq(min(mat), max(mat), length = 3), c("blue", "#EEEEEE", "red"))
f2 = colorRamp2(seq(min(mat), max(mat), length = 3), c("blue", "#EEEEEE", "red"), space = "RGB")
Heatmap(mat, name = "mat1", col = f1, column_title = "LAB color space")
Heatmap(mat, name = "mat2", col = f2, column_title = "RGB color space")

border/border_gp 和 rect_gp 分别控制heatmap body 和 cell區域

border可以是邏輯值T或顔色向量，border_gp是一個gpar對象，個人了解這個gpar（grid::gpar()）類似于R 的par 和 html style 可以設定相關的屬性

Heatmap(mat, name = "mat", border_gp = gpar(col = "black", lty = 2),
    column_title = "set heatmap borders")
Heatmap(mat, name = "mat", column_title = "I am a column title at the bottom", column_title_side = "bottom")
Heatmap(mat, name = "mat", column_title = "I am a column title", 
    column_title_gp = gpar(fill = "red", col = "white", border = "blue"))

2.3 聚類track

Heatmap(mat, name = "mat", clustering_distance_rows = "pearson",
    column_title = "pre-defined distance method (1 - pearson)")   #pearson
Heatmap(mat, name = "mat", clustering_distance_rows = function(m) dist(m), column_title = "a function that calculates distance matrix")  #距離
Heatmap(mat, name = "mat", clustering_distance_rows = function(x, y) 1 - cor(x, y), column_title = "a function that calculates pairwise distance")  #pairwise distance
#pairwise distance 去除離群值，（0.1，0.9)之間過濾篩選
at_with_outliers = mat
for(i in  1:10) mat_with_outliers[i, i] = 1000
robust_dist = function(x, y) {
    qx = quantile(x, c(0.1, 0.9))
    qy = quantile(y, c(0.1, 0.9))
    l = x > qx[1] & x < qx[2] & y > qy[1] & y < qy[2]
    x = x[l]
    y = y[l]
    sqrt(sum((x - y)^2))
}
Heatmap(mat_with_outliers, name = "mat", 
    col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")),
    clustering_distance_rows = robust_dist,
    clustering_distance_columns = robust_dist,
    column_title = "robust_dist")

cell_fun： 控制cell顯示，聚類方法通過clustering_method_rows 和 clustering_method_columns設定，與**hclust()**方法類似

library(cluster)
Heatmap(mat, name = "mat", cluster_rows = diana(mat),
   cluster_columns = agnes(t(mat)), column_title = "clustering objects")

如果想修改旁邊的系統樹的 style，可以先得到dendrogram對象，通過nodePar 和 edgePar來設定邊和頂點，這個用的不多，

ibrary(dendextend)
row_dend = as.dendrogram(hclust(dist(mat)))
row_dend = color_branches(row_dend, k = 2) # `color_branches()` returns a dendrogram object
Heatmap(mat, name = "mat", cluster_rows = row_dend)

同樣的，row_dend_gp 和 column_dend_gp控制系統樹的設定

控制行和列的顯示順序，使用row_order和column_order，使用factors也可以，自然，行和列聚類關閉才生效。

列和行名顯示位置參數

row_names_side # rowname 顯示

row_dend_side #行進化樹

column_names_side # 列名

column_dend_side # 列進化樹

2.4 熱圖分割 split

控制分割的參數： row_km, row_split, column_km, column_split

row_km and column_km按照均值分割，另外可以設定row_km_repeats和column_km_repeats分别跑多次，最後取個一緻性的分割值，比預設的要小。

Heatmap(mat, name = "mat",  row_km = 2, row_km_repeats = 100,
 column_km = 3, column_km_repeats = 100)

可根據字元向量分割，比較常用，row_split or column_split字元向量或資料框，需要和矩陣的次元一緻。

Heatmap(mat, name = "mat", 
    row_split = rep(c("A", "B"), 9), column_split = rep(c("C", "D"), 12))
    #字元型矩陣
    # split by the first column in `discrete_mat`
Heatmap(discrete_mat, name = "mat", col = 1:4, row_split = discrete_mat[, 1])

slices（subgroups）順序問題，預設是排序的，可以設定cluster_row_slices or cluster_column_slices為False, 這樣順序就按照column_split分割的順序了

Heatmap(mat, name = "mat", 
  row_split = rep(LETTERS[1:3], 6),
    column_split = rep(letters[1:6], 4))
Heatmap(mat, name = "mat", row_split = factor(rep(LETTERS[1:3], 6), levels = LETTERS[3:1]),column_split=factor(rep(letters[1:6], 4), levels = letters[6:1]), cluster_row_slices = FALSE, cluster_column_slices = FALSE)

slices的其他屬性，graphic parameters需要和slices的個數一緻

ht_opt$TITLE_PADDING = unit(c(4, 4), "points")
Heatmap(mat, name = "mat", 
    row_km = 2, row_title_gp = gpar(col = c("red", "blue"), font = 1:2),
    row_names_gp = gpar(col = c("green", "orange"), fontsize = c(10, 14)),
    column_km = 3, column_title_gp = gpar(fill = c("red", "blue", "green"), font = 1:3),
    column_names_gp = gpar(col = c("green", "orange", "purple"), fontsize = c(10, 14, 8)))

slices之間的距離：row_gap = unit(5, “mm”)

row_gap = unit(5, “mm”)

邊框： border = TRUE

另外分割時，注釋圖形一起分割，cell_fun分别畫一個cells，layer_fun垂直版本，

small_mat = mat[1:9, 1:9]
col_fun = colorRamp2(c(-2, 0, 2), c("green", "white", "red"))
Heatmap(small_mat, name = "mat", col = col_fun,
    cell_fun = function(j, i, x, y, width, height, fill) {
        grid.text(sprintf("%.1f", small_mat[i, j]), x, y, gp = gpar(fontsize = 10))
})

3. 熱圖注釋

熱圖注釋賦予圖形豐富的内容，可以展示軸相關的row和columns額外的資訊，op_annotation, bottom_annotation, left_annotation 和 right_annotation控制位置參數。參數的值需要是HeatmapAnnotation 類，有HeatmapAnnotation和rowAnnotation() [注釋row] 建構産生，官方文檔說明，rowAnnotation隻是HeatmapAnnotation(…, which = “row”)的特殊情況。

set.seed(123)
mat = matrix(rnorm(100), 10)
rownames(mat) = paste0("R", 1:10)
colnames(mat) = paste0("C", 1:10)
column_ha = HeatmapAnnotation(foo1 = runif(10), bar1 = anno_barplot(runif(10)))
row_ha = rowAnnotation(foo2 = runif(10), bar2 = anno_barplot(runif(10)))
Heatmap(mat, name = "mat", top_annotation = column_ha, right_annotation = row_ha)

注意注釋列，行次元需要和熱圖中資料分别一緻，注釋圖形函數以anno_*形式，可以注釋block，圖檔，points， lines，Barplot, Boxplot, histogram, Density， Text， Mark，分别格式如下：

# blocks
Heatmap(matrix(rnorm(100), 10), name = "mat",
    top_annotation = HeatmapAnnotation(foo = anno_block(gp = gpar(fill = 2:4))),
    column_km = 3)
#images
image_png = sample(dir("IcoMoon-Free-master/PNG/64px", full.names = TRUE), 10)
ha = HeatmapAnnotation(foo = anno_image(image_png))
ha = HeatmapAnnotation(foo = anno_image(image_png, space = unit(3, "mm")))

#points
ha = HeatmapAnnotation(foo = anno_points(matrix(runif(20), nc = 2), 
    pch = 1:2, gp = gpar(col = 2:3)))   #  foo 隻是個名字，可以修改
  # lines
  ha = HeatmapAnnotation(foo = anno_lines(cbind(c(1:5, 1:5), c(5:1, 5:1)), 
    gp = gpar(col = 2:3), add_points = TRUE, pt_gp = gpar(col = 5:6), pch = c(1, 16)))   # 資料可以是一個向量或者矩陣
 # barplot
 ha = HeatmapAnnotation(foo = anno_barplot(1:10, gp = gpar(fill = 1:10)))  #  向量是普通的bar圖
 ha = HeatmapAnnotation(foo = anno_barplot(cbind(1:10, 10:1),                     #矩陣堆疊在一起，stack
    gp = gpar(fill = 2:3, col = 2:3)))
    # boxplot
    ha = HeatmapAnnotation(foo = anno_boxplot(m, height = unit(4, "cm"), 
    gp = gpar(fill = 1:10)))
#Histogram,對于row注釋比較合适，輸入資料類似 anno_boxplot()  [***a matrix or a list***]
‘m = matrix(rnorm(1000), nc = 100)
ha = rowAnnotation(foo = anno_histogram(m))
#Density, 類似Histogram，拟合曲線分布，type參數比較重要，可以取heatmap, violin，資料形式需要數值矩陣

m2 = matrix(rnorm(50*10), nrow = 50)
m = matrix(rnorm(1000), nc = 100)

ha = rowAnnotation(foo = anno_density(m, joyplot_scale = 2, 
    gp = gpar(fill = "#CCCCCC80")))
  ha = rowAnnotation(foo = anno_density(m2, type = "heatmap", width = unit(6, "cm")))
# Mark 注釋，行和列有多個時，需要mark一部分，使用anno_mark()，至少需要兩個參數，at  原始矩陣的索引，labels相應的text
m = matrix(rnorm(1000), nrow = 100)
rownames(m) = 1:100
ha = rowAnnotation(foo = anno_mark(at = c(1:4, 20, 60, 97:100), labels = month.name[1:10]))
Heatmap(m, name = "mat", cluster_rows = FALSE, right_annotation = ha,
    row_names_side = "left", row_names_gp = gpar(fontsize = 4))
Heatmap(m, name = "mat", cluster_rows = FALSE, right_annotation = ha,
    row_names_side = "left", row_names_gp = gpar(fontsize = 4), row_km = 4)

多個注釋的，隻需要name-value寫到HeatmapAnnotation即可

ha = HeatmapAnnotation(foo = 1:10, 
    bar = cbind(1:10, 10:1),
    pt = anno_points(1:10),
    show_legend = c("bar" = FALSE)
)
Heatmap(matrix(rnorm(100), 10), name = "mat", top_annotation = ha)

小結：

注釋同通過anno_*擷取對象，通過 [right|left|bottom|top]_annotation顯示注釋的位置，所有注釋對象可以使用+連接配接

hitlist<-anno1+anno1 draw(hitlist)，和多個下面介紹的多個熱圖組合一樣

4. 熱圖清單操作

熱圖清單操作類似于，上面的注釋，多個熱圖可以組合成一個list，使用draw進行顯示，可以水準黏貼和垂直黏貼，水準黏貼比較常用，便于比較，所有熱圖和對應注釋的的row數量 需要相同，即行數需要相同，

ht1 = Heatmap(mat1, name = "rnorm")
ht2 = Heatmap(mat2, name = "runif")
ht3 = Heatmap(le, name = "letters")
draw(ht_list, row_title = "Three heatmaps, row title", row_title_gp = gpar(col = "red"),
    column_title = "Three heatmaps, column title", column_title_gp = gpar(fontsize = 16))

ht_list =ht1 + ht2 + ht3 #傳回heatmap list，會面可以任意追加 （+）熱圖

組合圖常用調整參數：

  1. size: width = unit(5, “mm”)

  2. 熱圖間Gap: draw(ht_list, ht_gap = unit(1, “cm”))

    3）Row annotations，水準heatmaplist 可以連接配接注釋

ha1 = rowAnnotation(foo = 1:12, bar = anno_barplot(1:12, width = unit(4, "cm")))
ht1 = Heatmap(mat1, name = "rnorm", col = col_rnorm, row_km = 2)
ht1 + ha1

Heatmap(mat1, name = "rnorm", col = col_rnorm, row_km = 2) + 
    rowAnnotation(foo = 1:12) +
    rowAnnotation(bar = anno_barplot(1:12, width = unit(4, "cm")))

4）adding a text annotation row names

ht1 + ha1 + rowAnnotation(rn = anno_text(rownames(mat1), 
    location = unit(0, "npc"), just = "left"))

5)adjust_annotation_extension,是否考慮軸和names之間的自動調整，heatmap name 和heatbody之間的空白區域

其他參數參考官方文檔：

https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html

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