Correlation Network: perturbation analysis

Clone repository

git clone https://github.com/jmrodriguezc/corrNetwork.git

or

wget https://github.com/jmrodriguezc/corrNetwork/archive/master.zip

Execute method

python correlation_network.py -i data/PESA_V1_Zq.xlsx -o data/PESA_V1.corr_net.pearson.csv
python correlation_network.py -i data/PESA_V1_Zq.xlsx -o data/PESA_V1.corr_net.kendall.csv -m kendall
python correlation_network.py -i data/PESA_V1_Zq.xlsx -o data/PESA_V1.corr_net.spearman.csv -m spearman

Filter correlation scores

1. Positive and negative values

awk -F ',' '{if ($3 >= 0){print $0}}' PESA_p2q_DigPar_woPos_Correlations.pearson.csv > PESA_p2q_DigPar_woPos_Correlations.pearson.pos.csv
awk -F ',' '{if ($3 < 0){print $0}}' PESA_p2q_DigPar_woPos_Correlations.pearson.csv > PESA_p2q_DigPar_woPos_Correlations.pearson.neg.csv

2. Filter by score value

awk -F ',' '{if ($3 >= 0.6){print $0}}' PESA_p2q_DigPar_woPos_Correlations.pearson.pos.csv > PESA_p2q_DigPar_woPos_Correlations.pearson.pos.thr_06.csv

3. Delete minus (absolute value)

sed 's/\,\-/\,/' PESA_p2q_DigPar_woPos_Correlations.pearson.neg.csv > PESA_p2q_DigPar_woPos_Correlations.pearson.neg.abs.csv