Function imports



In [12]:

    
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
import seaborn as sns
from sys import argv

Pathways and genes of interest from Kiel and Shu



In [13]:

    
gene_dict = {
    'Phosphate': ['VF_A1087', 'VF_A1090', 'VF_1611', 'VF_A1057', 'VF_1610', 'VF_1613', 'VF_1612', 'VF_A1089'],
    'Flagellar': ['VF_1851', 'VF_2079', 'VF_1842', 'VF_2317', 'VF_1843', 'VF_1863', 'VF_1841'],
    'LipidPerox': ['VF_1081', 'VF_1082', 'VF_1083', 'VF_A1049', 'VF_A1050'],
    'LuxOperon': ['VF_A0918', 'VF_A0919', 'VF_A0920', 'VF_A0924', 'VF_A0921', 'VF_A0922', 'VF_A0923', 'VF_A0924'],
    'LuxIregulated': ['VF_A0985', 'VF_1161', 'VF_1162', 'VF_1725', 'VF_A0090', 'VF_A0622', 'VF_A1058'],
    'GGDEFdomain': ['VF_A0879', 'VF_A0343', 'VF_A0342', 'VF_A0476'],

#     'TMAOreductase': ['VF_A0188', 'VF_A0189'],
#     'FatCatabolism': ['VF_0533'],
#     'AminoAcid': ['VF_1585', 'VF_1586', 'VF_A0840'],
#     'PTSsugars': ['VF_A0747', 'VF_A1189', 'VF_A0941', 'VF_A0942'],
#     'NonPTSsugars': ['VF_A0799']
    }

Import data



In [14]:

    
path = 'results_plk_swt_vnt.csv' # argv[8]



In [15]:

    
df = pd.read_csv(path, index_col=0)

Print KO and "blue" (up in plk rel to vnt) or "green" (down in plk rel to vnt) to copy to http://www.genome.jp/kegg/tool/map_pathway2.html (search against ko or vfi)



In [16]:

    
with open("ko_plk_vnt_kiel.txt", "w") as text_file:
    for pathway in gene_dict.keys():
        for gene in gene_dict[pathway]:
            if df['Plk-vs-Vnt_log2fc'][gene] > 0:
                text_file.write('%s #2c6fbb\n' % df['KO_number'][gene]) # medium blue
            else:
                text_file.write('%s #39ad48\n' % df['KO_number'][gene]) # medium green



In [17]:

    
with open("ko_plk_vnt_padj001.txt", "w") as text_file:
    for row in df.iterrows():
        gene = row[0]
        if df['Plk-vs-Swt_padj'][gene] < 0.001:
            if df['Plk-vs-Vnt_log2fc'][gene] > 0:
                text_file.write('%s #2c6fbb\n' % df['KO_number'][gene]) # medium blue
            else:
                text_file.write('%s #39ad48\n' % df['KO_number'][gene]) # medium green



In [18]:

    
with open("ko_plk_vnt_padj001_log2fc3.txt", "w") as text_file:
    for row in df.iterrows():
        gene = row[0]
        if (df['Plk-vs-Swt_padj'][gene] < 0.001) & (df['Plk-vs-Swt_log2fc'][gene] > 3):
            if df['Plk-vs-Vnt_log2fc'][gene] > 0:
                text_file.write('%s #2c6fbb\n' % df['KO_number'][gene]) # medium blue
            else:
                text_file.write('%s #39ad48\n' % df['KO_number'][gene]) # medium green



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