Update README.md

INTRODUCTION and INSTALL finished

README.md

@@ -2,7 +2,7 @@ README for PyLOH 1.0
 ======================
 
 
-Introduction
+INTRODUCTION
 ============
 
 Estimation of tumor purity is a crucial step in understanding
@@ -22,13 +22,49 @@ cancer sequencing data to disambiguate multiple compatible
 solutions. We also introduce a novel visualization method 
 "BAF heat map" to to characterize the cluster pattern of LOH.
 
-Install
+
+
+INSTALL
 =======
 
+
+
 Prerequisites
 -------------
+* Although not mandatory, Linux system is recommended.
+
+* Python (2.7). [Python 2.7.3](http://www.python.org/download/releases/2.7.3/) is recommended.
+
+* [Numpy](http://www.numpy.org/)(>=1.6.1). You can download the source of Numpy from [here](http://sourceforge.net/projects/numpy/files/).
+
+* [Scipy](http://www.scipy.org/)(>=0.10). You can download the source of Scipy from [here](http://sourceforge.net/projects/scipy/files/).
+
+* [Pysam](https://code.google.com/p/pysam/)(>=0.7). To install Pysam, you also need to install [Cython](http://cython.org/) first. 
+
+* [matplotlib](http://matplotlib.org/)(>=1.2.0) is required to plot BAF heat map.
+
+
+Altough not required by PyLOH, [samtools](http://samtools.sourceforge.net/) can be useful for creating bam, bam index and fasta index 
+files which are required by the pysam module of PyLOH. 
+
+Install from source
+-------------------
+Download the compressed source file PyLOH-*.tar.gz and do as follows:
+
+```
+$ tar -xzvf PyLOH-*.tar.gz
+$ cd PyLOH-*
+$ python setup.py install
+```
+
+If you prefer to install PyLOH other than the default directory, you can also use this command:
+```
+$ python setup.py install --prefix /home/yili/
+```
 
-Python 2.7 are required to run PyLOH 1.0, [Python 2.7.3](http://www.python.org/download/releases/2.7.3/) is recommended. 
+There are also `config/` and `bin/` folders under PyLOH-*. The `config/` folder contains example priors and the `bin/` folder contains useful 
+utilities, such as the R code to run [BICseq](http://compbio.med.harvard.edu/Supplements/PNAS11.html) and the python script to convert
+BICseq results to BED file. You can copy these two folders somewhere easily accessible.