##Implementation of Arrowhead Algorithm for Domain Annotation

###Input Data Used Input data can be found at http://chromosome.sdsc.edu/mouse/hi-c/download.html

###How to run

python arrowhead.py --input_data <inputfile> --is_normal <y/n> --apply_threshold1 <y/n> --t1 <threshold1_val1> --t2 <threshold1_val2> --t3 <threshold1_val3> --apply_threshold2 <y/n> --t4 <threshold2_val1> --t5 <threshold2_val2>

--is_normal: Is the input matrix normalized

threshold1_val1: Value for variance when applying threshold1

threshold1_val2: Value for Mean(sgn(Ua,b)) when applying threshold1

threshold1_val3: Value for Mean(sgn(La,b)) when applying threshold1

threshold2_val1: Value for Mean(sgn(Ua,b)) when applying threshold2

threshold2_val1: Value for Mean(sgn(La,b)) when applying threshold2

###Sample run

python arrowHead.py --input_data=uij.chr1 --is_normal n --t1 0.2 --t2 0.5 --t3 0.5 --apply_threshold1 y

For performance considerations the implementation calculates only upper triangle values of all the matrices.

On running the script it also produces the heat map for Arrow Head matrix as A.jpg and for the S corner matrix as Scorner.jpg

After the arrowhead.py script is run, we get a file called ScornerData which is the filtered version of Scorner matrix and we need to find the connected components in this matrix.

To find the connected components, run the script connected_components.py

python	connected_components.py