R语言学习 - 热图简化

data_ori <- "Grp_1;Grp_2;Grp_3;Grp_4;Grp_5
a;6.6;20.9;100.1;600.0;5.2
b;20.8;99.8;700.0;3.7;19.2
c;100.0;800.0;6.2;21.4;98.6
d;900;3.3;20.3;101.1;10000"

data <- read.table(text=data_ori, header=T, row.names=1, sep=";", quote="")

  Grp_1 Grp_2 Grp_3 Grp_4   Grp_5
a   6.6  20.9 100.1 600.0     5.2
b  20.8  99.8 700.0   3.7    19.2
c 100.0 800.0   6.2  21.4    98.6
d 900.0   3.3  20.3 101.1 10000.0

pheatmap::pheatmap(data, filename="pheatmap_1.pdf")

pheatmap::pheatmap(data, scale="row", filename="pheatmap_1.pdf")

pheatmap::pheatmap(data, scale="row", cluster_rows=FALSE, cluster_cols=FALSE, filename="pheatmap_1.pdf")

row_anno = data.frame(type=c("TF","Enzyme","Enzyme","TF"), class=c("clu1","clu1","clu2","clu2"), row.names=rownames(data))
row_anno

    type class
a     TF  clu1
b Enzyme  clu1
c Enzyme  clu2
d     TF  clu2

col_anno = data.frame(grp=c("A","A","A","B","B"), size=1:5, row.names=colnames(data))
col_anno

      grp size
Grp_1   A    1
Grp_2   A    2
Grp_3   A    3
Grp_4   B    4
Grp_5   B    5

pheatmap::pheatmap(data, scale="row", 
cluster_rows=FALSE, 
annotation_col=col_anno,
annotation_row=row_anno,
filename="pheatmap_1.pdf")

# <bias> values larger than 1 will give more color for high end. 
# Values between 0-1 will give more color for low end.
pheatmap::pheatmap(data, scale="row", 
cluster_rows=FALSE, 
annotation_col=col_anno,
annotation_row=row_anno,
color=colorRampPalette(c('green','yellow','red'), bias=1)(50),
filename="pheatmap_1.pdf")

# tab键分割，每列不加引号
write.table(data, file="heatmap_data.xls", sep="\t", row.names=T, col.names=T,quote=F)
# 如果看着第一行少了ID列不爽，可以填补下
system("sed -i '1 s/^/ID\t/' heatmap_data.xls")

write.table(row_anno, file="heatmap_row_anno.xls", sep="\t", row.names=T, col.names=T,quote=F)
write.table(col_anno, file="heatmap_col_anno.xls", sep="\t", row.names=T, col.names=T,quote=F)

# -f: 指定输入的矩阵文件
# -d：指定是否计算Z-score，<none> (否), <row> (按行算), <col> (按列算)
# -P: 行注释文件
# -Q: 列注释文件
ct@ehbio:~/$ sp_pheatmap.sh -f heatmap_data.xls -d row -P heatmap_row_anno.xls -Q heatmap_col_anno.xls

# -f: 指定输入的矩阵文件
# -d：指定是否计算Z-score，<none> (否), <row> (按行算), <col> (按列算)
# -P: 行注释文件
# -Q: 列注释文件
# -u: 设置宽度，单位是inch
# -v: 设置高度，单位是inch
ct@ehbio:~/$ sp_pheatmap.sh -f heatmap_data.xls -d row -P heatmap_row_anno.xls -Q heatmap_col_anno.xls -u 8 -v 12

# -A: 0, X轴标签选择0度
# -C: 自定义颜色，注意引号的使用，最外层引号与内层引号不同，引号之间无交叉
# -T: 指定给定的颜色的类型；如果给的是vector (如下面的例子), 则-T需要指定为vector; 否则结果会很怪异，只有俩颜色。
# -t: 指定图形的题目，注意引号的使用；参数中包含空格或特殊字符等都要用引号引起来作为一个整体。
ct@ehbio:~/$ sp_pheatmap.sh -f heatmap_data.xls -d row -P heatmap_row_anno.xls -Q heatmap_col_anno.xls -u 8 -v 12 -A 0 -C 'c("white", "blue")' -T vector -t "Heatmap of gene expression profile"

***CREATED BY Chen Tong (chentong_biology@163.com)***

----Matrix file--------------
Name    T0_1    T0_2    T0_3    T4_1    T4_2
TR19267|c0_g1|CYP703A2    1.431    0.77    1.309    1.247    0.485
TR19612|c1_g3|CYP707A1    0.72    0.161    0.301    2.457    2.794
TR60337|c4_g9|CYP707A1    0.056    0.09    0.038    7.643    15.379
TR19612|c0_g1|CYP707A3    2.011    0.689    1.29    0    0
TR35761|c0_g1|CYP707A4    1.946    1.575    1.892    1.019    0.999
TR58054|c0_g2|CYP707A4    12.338    10.016    9.387    0.782    0.563
TR14082|c7_g4|CYP707A4    10.505    8.709    7.212    4.395    6.103
TR60509|c0_g1|CYP707A7    3.527    3.348    2.128    3.257    2.338
TR26914|c0_g1|CYP710A1    1.899    1.54    0.998    0.255    0.427
----Matrix file--------------

----Row annorarion file --------------
------1. At least two columns--------------
------2. The first column should be the same as the first column in
         matrix (order does not matter)--------------
Name    Clan    Family
TR19267|c0_g1|CYP703A2    CYP71    CYP703
TR19612|c1_g3|CYP707A1    CYP85    CYP707
TR60337|c4_g9|CYP707A1    CYP85    CYP707
TR19612|c0_g1|CYP707A3    CYP85    CYP707
TR35761|c0_g1|CYP707A4    CYP85    CYP707
TR58054|c0_g2|CYP707A4    CYP85    CYP707
TR14082|c7_g4|CYP707A4    CYP85    CYP707
TR60509|c0_g1|CYP707A7    CYP85    CYP707
TR26914|c0_g1|CYP710A1    CYP710    CYP710
----Row annorarion file --------------

----Column annorarion file --------------
------1. At least two columns--------------
------2. The first column should be the same as the first row in
---------matrix (order does not matter)--------------
Name    Sample
T0_1    T0
T0_2    T0
T0_3    T0
T4_1    T4
T4_2    T4
----Column annorarion file --------------

Usage:

sp_pheatmap.sh options

Function:

This script is used to do heatmap using package pheatmap.

The parameters for logical variable are either TRUE or FALSE.

OPTIONS:
    -f    Data file (with header line, the first column is the
         rowname, tab seperated. Colnames must be unique unless you
        know what you are doing.)[NECESSARY]
    -t    Title of picture[Default empty title]
        ["Heatmap of gene expression profile"]
    -a    Display xtics. [Default TRUE]
    -A    Rotation angle for x-axis value (anti clockwise)
        [Default 90]
    -b    Display ytics. [Default TRUE]
    -H    Hieratical cluster for columns.
        Default FALSE, accept TRUE
    -R    Hieratical cluster for rows.
        Default TRUE, accept FALSE
    -c    Clustering method, Default "complete". 
        Accept "ward.D", "ward.D2","single", "average" (=UPGMA), 
        "mcquitty" (=WPGMA), "median" (=WPGMC) or "centroid" (=UPGMC)
    -C    Color vector. 
        Default pheatmap_default. 
        Aceept a vector containing multiple colors such as 
        <'c("white", "blue")'> will be transferred 
        to <colorRampPalette(c("white", "blue"), bias=1)(30)>
        or an R function 
        <colorRampPalette(rev(brewer.pal(n=7, name="RdYlBu")))(100)>
        generating a list of colors.

    -T    Color type, a vetcor which will be transferred as described in <-C> [vector] or
           a raw vector [direct vector] or    a function [function (default)].
    -B    A positive number. Default 1. Values larger than 1 will give more color
           for high end. Values between 0-1 will give more color for low end.    
    -D    Clustering distance method for rows.
        Default 'correlation', accept 'euclidean', 
        "manhattan", "maximum", "canberra", "binary", "minkowski". 
    -I    Clustering distance method for cols.
        Default 'correlation', accept 'euclidean', 
        "manhattan", "maximum", "canberra", "binary", "minkowski". 
    -L    First get log-value, then do other analysis.
        Accept an R function log2 or log10. 
        [Default FALSE]
    -d    Scale the data or not for clustering and visualization.
        [Default 'none' means no scale, accept 'row', 'column' to 
        scale by row or column.]
    -m    The maximum value you want to keep, any number larger willl
        be taken as this given maximum value.
        [Default Inf, Optional] 
    -s    The smallest value you want to keep, any number smaller will
        be taken as this given minimum value.
        [Default -Inf, Optional]  
    -k    Aggregate the rows using kmeans clustering. 
        This is advisable if number of rows is so big that R cannot 
        handle their hierarchical clustering anymore, roughly more than 1000.
        Instead of showing all the rows separately one can cluster the
        rows in advance and show only the cluster centers. The number
        of clusters can be tuned here.
        [Default 'NA' which means no
        cluster, other positive interger is accepted for executing
        kmeans cluster, also the parameter represents the number of
        expected clusters.]
    -P    A file to specify row-annotation with format described above.
        [Default NA]
    -Q    A file to specify col-annotation with format described above.
        [Default NA]
    -u    The width of output picture.[Default 20]
    -v    The height of output picture.[Default 20] 
    -E    The type of output figures.[Default pdf, accept
        eps/ps, tex (pictex), png, jpeg, tiff, bmp, svg and wmf)]
    -r    The resolution of output picture.[Default 300 ppi]
    -F    Font size [Default 14]
    -p    Preprocess data matrix to avoid 'STDERR 0 in cor(t(mat))'.
        Lowercase <p>.
        [Default TRUE]
    -e    Execute script (Default) or just output the script.
        [Default TRUE]
    -i    Install the required packages. Normmaly should be TRUE if this is 
        your first time run s-plot.[Default FALSE]

Usage:

s-plot options

Function:

This software is designed to simply the process of plotting and help
researchers focus more on data rather than technology.

Currently, the following types of plot are supported.

#### Bars
s-plot barPlot
s-plot horizontalBar
s-plot multiBar
s-plot colorBar

#### Lines
s-plot lines

#### Dots
s-plot pca
s-plot scatterplot
s-plot scatterplot3d
s-plot scatterplot2
s-plot scatterplotColor
s-plot scatterplotContour
s-plot scatterplotLotsData
s-plot scatterplotMatrix
s-plot scatterplotDoubleVariable
s-plot contourPlot
s-plot density2d

#### Distribution
s-plot areaplot
s-plot boxplot
s-plot densityPlot
s-plot densityHistPlot
s-plot histogram

#### Cluster
s-plot hcluster_gg (latest)
s-plot hcluster
s-plot hclust (depleted)

#### Heatmap
s-plot heatmapS
s-plot heatmapM
s-plot heatmap.2
s-plot pheatmap
s-plot pretteyHeatmap # obseleted
s-plot prettyHeatmap

#### Others
s-plot volcano
s-plot vennDiagram
s-plot upsetView