decodebiology

m.row.sum<- cbind(m1, rowSums(m1))
o1<- rev(order(m.row.sum[,602]))

m.row.sum<- m.row.sum[o1,]

bk = unique(c(seq(-0.1,3, length=100),seq(3,10.35,length=100)))

hmcols<- colorRampPalette(c("white","red"))(length(bk)-1)

pheatmap( m.row.sum[,1:601], cluster_rows = F, cluster_cols = F, col= hmcols, breaks = bk, legend=FALSE, show_rownames=FALSE, show_colnames=FALSE)

decodebiology

#The X axis is -3 kb to 3 kb around TSS.

#It turns out that the heatmap.2 function use default hclust ( Hierachical Clustering) to cluster the #matrix.
#alternatively, we can use K-means clustering to cluster the data and to see what's the pattern look like.

km<- kmeans(m1,2)   # determine how many cluster you want, I specify 2 here

m1.kmeans<- cbind(m1, km$cluster)   # combine the cluster with the matrix

dim(m1.kmeans)

decodebiology

# This R script is to generate the TF or histone modification heatmap
# at certain genomic features (TSS, enhancers) from the ChIP-seq data
# the input matrix is got from Homer software. alternative to R, use cluster3 to cluster, and visualize by # java Treeviewer
# generate the matrix by Homer: annotatePeaks.pl peak_file.txt hg19 -size 6000 -hist 10  -ghist -d TF1/ # > outputfile_matrix.txt
# see several posts for heatmap:
# http://davetang.org/muse/2010/12/06/making-a-heatmap-with-r/
# http://www.r-bloggers.com/r-using-rcolorbrewer-to-colour-your-figures-in-r/
# 08/20/13 by Tommy Tang

# it is such a simple script but took me several days to get it work...I mean the desired

decodebiology

# Bland-Altman plot R function.
# Author: jmmateos@mce.hggm.es
baplot <- function(m1, m2, ...) {
    # m1 and m2 are the measurements
    means <- (m1 + m2) / 2
    diffs <- m1 - m2
    mdiff <- mean(diffs)
    sddiff <- sd(diffs)
    
    # Compute the figure limits