NetREX and PriorBoost

NetREX

A python tool to reconstruct a gene regulatory network given context-specific expression data and a prior network.

Pre-request

1. Install Anaconda (python3 version) based on https://docs.anaconda.com/anaconda/install/
2. Install package progressbar2

   $ pip install progressbar2

How to use?

Default

$ python ./NetREX.py -e express_file -p prior_file 

-e [expression file name] <Required> expression file format is explained below. 
-p [prior file name] <Required> prior network file format is explained below. 

Advanced

$ python ./NetREX.py -e express_file -p prior_file -k 0.6 0.7 0.8 -t 1.2

-k [a list of percentages] (Optional)"0.6 0.7 0.8" means that NetREX would keep 60%, 70%, and 80% edges in the prior respectively. The final predicted network is the consensus based on networks predicted from those percentages.
-t [a ratio] (Optional)"1.2" means the number of  edges in the output network is 1.2 times to the edges in the prior network. 

Output of NetREX

Two files will be outputed: 1) The predicted network with edge pair format; 2) The predicted network with adjacency matrix format.

Example of use NetREX

Put NetREX.py and prior.txt and expression.txt in the NexREX_Example_Data folder in the same folder. Then run:

$ python ./NetREX.py -e expression.txt -p prior.txt

The rank of each edges will be outputed in "NetREX_PredictedEdgeList.txt" and "NetREX_PredictedNetwork.tsv" as shown in this repo.

Expression file format

A Tab sepeated file with the first colomn stroing the gene names (E1, E2, ...). For example:

E1	0.2508	0.2684	0.2786	0.2878	...
E2	0.3149	0.3323	0.3427	0.3538	...
E3	0.2361	0.2526	0.2616	0.2698	...
E4	0.2530	0.2699	0.2791	0.2910	...
E5	0.2521	0.2693	0.2797	0.2885	...
E6	0.3162	0.3291	0.3390	0.3482	...
E7	0.2866	0.3021	0.3105	00.3175	...

Prior file format

A Tab sepeated file with the first colomn stroing the gene names (E1, E2, ...) and the first row stroing the TF names (M1, M2, ...). The order of gene names in the prior file should be the same to the order in the expression data file. For example:

	M1	M2	M3
E1	0.0	1.0	1.0
E2	1.0	1.0	1.0
E3	1.0	1.0	0.0
E4	1.0	1.0	0.0
E5	1.0	1.0	1.0
E6	1.0	1.0	1.0
E7	1.0	0.0	1.0

PriorBoost

A python tool to compare the explaination power of two different networks. Assume Net1 is the network obtained from a prior-based method and Net2 is a network obtained from a expression-based method.

How to use?

$ python ./PriorBoost.py -e express_file -p Net1_file -b Net2_file 

-e [expression file name] <Required> expression file format is explained above. 
-p [Net1 file name] <Required> Net1 network file format is explained above. Elements in the Network are their ranks. rank 1 is the best.
-b [Net2 file name] <Required> Net2 network file format is explained above. Elements is the weights of each edges. The larger the better.