#!/bin/bash

#set -x

usage()

{

cat <<EOF

${txtcyn}

***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--------------

----Column annotation 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:

$0 options${txtrst}

${bldblu}Function${txtrst}:

This script is used to do heatmap using package pheatmap.

The parameters for logical variable are either TRUE or FALSE.

${txtbld}OPTIONS${txtrst}:

-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.)${bldred}[NECESSARY]${txtrst}

-t Title of picture[${txtred}Default empty title${txtrst}]

[Scatter plot of horizontal and vertical variable]

-H Cluster tree shown in horizontal format.

${bldred}Default FALSE meaning vertical tree, accept TRUE${txtrst}

-c Clustering method, Default "complete".

Accept "ward.D", "ward.D2","single", "average" (=UPGMA),

"mcquitty" (=WPGMA), "median" (=WPGMC) or "centroid" (=UPGMC)

-C Color vector.

${bldred}Default pheatmap_default. Aceept a vector containing

multiple colors such as <c("white", "blue")> or

a R function generating a list of colors.${txtrst}

-I Clustering distance method for cols.

${bldred}Default 'correlation', accept 'euclidean',

"manhattan", "maximum", "canberra", "binary", "minkowski". ${txtrst}

-L First get log-value, then do other analysis.

Accept an R function log2 or log10.

${bldred}[Default FALSE]${txtrst}

-d Scale the data or not for clustering and visualization.

[Default 'none' means no scale, accept 'row', 'column' to

scale by row or column.]

-Q A file to specify col-annotation.[${txtred}Default NA${txtrst}]

-u The width of output picture.[${txtred}Default 20${txtrst}]

-v The height of output picture.[${txtred}Default 20${txtrst}]

-E The type of output figures.[${txtred}Default pdf, accept

eps/ps, tex (pictex), png, jpeg, tiff, bmp, svg and wmf)${txtrst}]

-r The resolution of output picture.[${txtred}Default 300 ppi${txtrst}]

-F Font size [${txtred}Default 14${txtrst}]

-e Execute or not[${bldred}Default TRUE${txtrst}]

-i Install the required packages[${bldred}Default FALSE${txtrst}]

Example: sp_hcluster_gg.sh -f matrix.pearson.xls -Q cluster.txt

EOF

}

file=''

title=''

horizontal='FALSE'

clustering_distance_cols='correlation'

clustering_method='complete'

legend_breaks='NA'

color_vector='colorRampPalette(rev(brewer.pal(n=7, name="RdYlBu")))(100)'

width=''

label=''

logv='FALSE'

kclu='NA'

scale='none'

execute='TRUE'

ist='FALSE'

legend=' '

na_color='grey'

uwid=20

vhig=20

res=300

fontsize=14

ext='pdf'

colormodel='srgb'

xcol='green'

ycol='red'

mcol='yellow'

mid_value_use='FALSE'

mid_value='Inf'

xtics='TRUE'

xtics_angle=270

ytics='TRUE'

gradient=1

givenSepartor=''

gradientC="'green','yellow','red'"

generateNA='FALSE'

digits='FALSE'

annotation_row='NA'

annotation_col='NA'

preprocess='TRUE'

while getopts "hf:t:a:A:b:H:R:c:D:p:I:L:d:k:u:v:E:r:F:P:Q:x:y:M:Z:X:s:m:N:Y:G:C:O:e:i:" OPTION

do

case $OPTION in

h)

echo "Help mesage"

usage

exit 1

;;

f)

file=$OPTARG

;;

t)

title=$OPTARG

;;

a)

xtics=$OPTARG

;;

A)

xtics_angle=$OPTARG

;;

b)

ytics=$OPTARG

;;

H)

horizontal=$OPTARG

;;

R)

cluster_rows=$OPTARG

;;

c)

clustering_method=$OPTARG

;;

D)

clustering_distance_rows=$OPTARG

;;

I)

clustering_distance_cols=$OPTARG

;;

p)

preprocess=$OPTARG

;;

L)

logv=$OPTARG

;;

P)

annotation_row=$OPTARG

;;

Q)

annotation_col=$OPTARG

;;

d)

scale=$OPTARG

;;

k)

kclu=$OPTARG

;;

u)

uwid=$OPTARG

;;

v)

vhig=$OPTARG

;;

E)

ext=$OPTARG

;;

r)

res=$OPTARG

;;

F)

fontsize=$OPTARG

;;

x)

xcol=$OPTARG

;;

y)

ycol=$OPTARG

;;

M)

mcol=$OPTARG

;;

K)

logv_pos=$OPTARG

;;

Z)

mid_value_use=$OPTARG

;;

X)

mid_value=$OPTARG

;;

s)

small=$OPTARG

;;

m)

maximum=$OPTARG

;;

N)

generateNA=$OPTARG

;;

Y)

na_color=$OPTARG

;;

G)

gradient=$OPTARG

;;

C)

color_vector=$OPTARG

;;

O)

givenSepartor=$OPTARG

;;

e)

execute=$OPTARG

;;

i)

ist=$OPTARG

;;

?)

usage

echo "Unknown parameters"

exit 1

;;

esac

done

mid=".hcluster"

if [ -z $file ] ; then

echo 1>&2 "Please give filename."

usage

exit 1

fi

if test "$log" != ''; then

mid=${mid}".$log"

fi

if test "${scale}" == "TRUE"; then

mid=${mid}".scale"

fi

if test "${preprocess}" == "TRUE"; then

/bin/mv -f ${file} ${file}".nostd0"

dealWithSTD0.py -i ${file}".nostd0" >${file}

fi

cat <<END >${file}${mid}.r

if ($ist){

install.packages("ggplot2", repo="http://cran.us.r-project.org")

install.packages("ggdendro", repo="http://cran.us.r-project.org")

install.packages("amap", repo="http://cran.us.r-project.org")

}

library(ggplot2)

library(ggdendro)

library(amap)

if($gradient){

library(RColorBrewer)

}

data <- read.table(file="$file", sep="\t", header=T, row.names=1,

check.names=F, quote="", comment="")

if ("${annotation_col}" != "NA") {

annotation_col <- read.table(file="${annotation_col}", header=T,

row.names=1, sep="\t", quote="", check.names=F, comment="")

levs <- unique(unlist(lapply(annotation_col, unique)))

annotation_col <- data.frame(lapply(annotation_col, factor,

levels=levs), row.names=rownames(annotation_col))

} else {

annotation_col <- NA

}

hc <- hcluster(data)

dhc <- as.dendrogram(hc)

dhc_data <- dendro_data(dhc, type="rectangle")

p <- ggplot(segment(dhc_data)) +

geom_segment(aes(x=x, y=y, xend=xend, yend=yend)) +

theme(axis.line.y=element_blank(),

axis.ticks.y=element_blank(),

axis.text.y=element_blank(),

axis.title.y=element_blank(),

panel.background=element_rect(fill="white"),

panel.grid=element_blank(),

legend.position="top")

if ("${annotation_col}" != "NA") {

if (${horizontal}){

p <- p + geom_text(data=dhc_data\$labels, aes(x,y,label=label,

color=annotation_col[,1]), angle=90, size=2, hjust=0) +

oord_flip() + scale_y_reverse(expand=c(0.2, 0))

}else {

p <- p + geom_text(data=dhc_data\$labels, aes(x,y,label=label,

color=annotation_col[,1]), angle=90, size=2, vjust=0) +

}

} else {

if (${horizontal}){

p <- p + geom_text(data=dhc_data\$labels, aes(x,y,label=label),

angle=90, size=2, hjust=0) +

oord_flip() + scale_y_reverse(expand=c(0.2, 0))

}else {

p <- p + geom_text(data=dhc_data\$labels, aes(x,y,label=label),

angle=90, size=2, vjust=0) +

}

}

if ("${ext}" == "pdf") {

ggsave(p, filename="${file}${mid}.${ext}", dpi=$res, width=$uwid,

height=$vhig, units=c("cm"),colormodel="${colormodel}")

} else {

ggsave(p, filename="${file}${mid}.${ext}", dpi=$res, width=$uwid,

height=$vhig, units=c("cm"))

}

END

if [ "$execute" == "TRUE" ]; then

Rscript ${file}${mid}.r

if [ "$?" == "0" ]; then

/bin/rm -f ${file}${mid}.r

/bin/rm -f Rplots.pdf

fi

fi

if test "${preprocess}" == "TRUE"; then

/bin/mv -f ${file}".nostd0" ${file}

fi

#convert -density 200 -flatten ${file}${mid}.eps ${first}${mid}.png