DLKcat/DeeplearningApproach/Code/analysis/Fig2d_Enzyme_promiscuity_case.py

#!/usr/bin/python
# coding: utf-8

# Author: LE YUAN

import math
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
from scipy import stats
import seaborn as sns
import pandas as pd
from scipy.stats import ranksums


def median(lst):
    sortedLst = sorted(lst)
    lstLen = len(lst)
    index = (lstLen - 1) // 2
   
    if (lstLen % 2):
        return sortedLst[index]
    else:
        return (sortedLst[index] + sortedLst[index + 1])/2.0

def main() :

    with open('../../Data/enzyme_promiscuity/human_aldo-keto-reductase.tsv', 'r') as infile :
        lines = infile.readlines()

        alldata = dict()
        alldata['type'] = list()
        alldata['value'] = list()
        native_substrates = list()
        underground_substrates = list()
        substrate_experimental = dict()
        substrate_predicted = dict()

        for line in lines[1:] :
            data = line.strip().split('\t')
            substrate, experimental, predicted = data[1], float(data[-2]), float(data[-1])
            substrate_experimental[substrate] = experimental
            substrate_predicted[substrate] = predicted 

        # https://stackoverflow.com/questions/613183/how-do-i-sort-a-dictionary-by-value
        sorted_substrate_experimental = sorted(substrate_experimental.items(), key=lambda x: x[1], reverse=True)
        print(sorted_substrate_experimental[:10])

        native_substrates_list = [substrate_value[0] for substrate_value in sorted_substrate_experimental[:6]]
        underground_substrates_list = [substrate_value[0] for substrate_value in sorted_substrate_experimental[-6:]]

        print(native_substrates_list)
        print(underground_substrates_list)
        print(len(native_substrates_list))
        print(len(underground_substrates_list))

        for line in lines[1:] :
            data = line.strip().split('\t')
            substrate, experimental, predicted = data[1], float(data[-2]), float(data[-1])

            if substrate in native_substrates_list :
                native_value = substrate_predicted[substrate]
                native_substrates.append(math.log10(float(native_value)))
                alldata['type'].append('Native')
                alldata['value'].append(math.log10(float(native_value)))

            if substrate in underground_substrates_list :
                underground_value = substrate_predicted[substrate]
                underground_substrates.append(math.log10(float(underground_value)))
                alldata['type'].append('Underground')
                alldata['value'].append(math.log10(float(underground_value)))

    p_value = ranksums(native_substrates, underground_substrates)[1]
    print('The amount of native_substrates:', len(native_substrates))
    print('The amount of underground_substrates:', len(underground_substrates))
    print('The median value of native_substrates on log10: %.4f' % median(native_substrates))
    print('The median value of underground_substrates on log10: %.4f' % median(underground_substrates))
    print('The median value of native_substrates: %.2f' % math.pow(10, median(native_substrates)))
    print('The median value of underground_substrates: %.2f' % math.pow(10, median(underground_substrates)))
    print('P value is: %.4f' % p_value)

    # The amount of native_substrates: 6
    # The amount of underground_substrates: 6
    # The median value of native_substrates on log10: 0.3460
    # The median value of underground_substrates on log10: -1.3495
    # The median value of native_substrates: 2.22
    # The median value of underground_substrates: 0.04
    # P value is: 0.0039

    # Plot the boxplot figures between the Alternative and Preferred
    allData = pd.DataFrame(alldata)
    # print(type(allData))

    plt.figure(figsize=(1.5,1.5))

    # To solve the 'Helvetica' font cannot be used in PDF file
    # https://stackoverflow.com/questions/59845568/the-pdf-backend-does-not-currently-support-the-selected-font
    rc('font',**{'family':'serif','serif':['Helvetica']})
    plt.rcParams['pdf.fonttype'] = 42

    plt.axes([0.12,0.12,0.83,0.83])
    
    plt.tick_params(direction='in')
    plt.tick_params(which='major',length=1.5)
    plt.tick_params(which='major',width=0.4)
    plt.tick_params(which='major',width=0.4)

    # rectangular box plot
    palette = {"Underground": '#2166ac', "Native": '#b2182b'}

    # for ind in allData.index:
    #     allData.loc[ind,'entry'] = '${0}$'.format(allData.loc[ind,'entry'])

    ax = sns.boxplot(data=alldata, x="type", y="value", order = ["Underground", "Native"],
            palette=palette, showfliers=False, linewidth=0.5, width=0.5)  # boxprops=dict(alpha=1.0)

    ax = sns.stripplot(data=alldata, x="type", y="value", order = ["Underground", "Native"], jitter=0.2,
            palette=palette, size=1.8, dodge=True)

    # https://stackoverflow.com/questions/58476654/how-to-remove-or-hide-x-axis-label-from-seaborn-boxplot
    # plt.xlabel(None) will remove the Label, but not the ticks. 
    ax.set(xlabel=None)

    for patch in ax.artists:
        r, g, b, a = patch.get_facecolor()
        patch.set_facecolor((r, g, b, 0.3))

    # print(ax.artists)
    # print(ax.lines)
    # print(len(ax.lines))
    # https://cduvallet.github.io/posts/2018/03/boxplots-in-python
    for i, artist in enumerate(ax.artists):
        # print(i)

        if i % 2 == 0:
            col = '#2166ac'
        else:
            col = '#b2182b'

        # if i % 2 == 0:
        #     col = '#b2182b'
        # else:
        #     col = '#2166ac'

        # This sets the color for the main box
        artist.set_edgecolor(col)

        # Each box has 5 associated Line2D objects (to make the whiskers, fliers, etc.)
        # Loop over them here, and use the same colour as above
        for j in range(i*5,i*5+5):
            # print(j)
            line = ax.lines[j]
            line.set_color(col)
            line.set_mfc(col)
            line.set_mec(col)
    handles = [ax.artists[0], ax.artists[1]]

    # for tick in ax.get_xticklabels() :
    #     tick.set_rotation(30)

    plt.rcParams['font.family'] = 'Helvetica'

    plt.text(-0.02, 1.0, 'p value = 0.0039', fontweight ="normal", fontsize=6)

    plt.ylabel("Predicted $k$$_\mathregular{cat}$ value [log10]", fontname='Helvetica', fontsize=7)

    # plt.xticks(rotation=30,ha='right')
    plt.yticks([-2, -1, 0, 1, 2])

    plt.xticks(fontsize=7)
    plt.yticks(fontsize=6)

    ax.spines['bottom'].set_linewidth(0.5)
    ax.spines['left'].set_linewidth(0.5)
    ax.spines['top'].set_linewidth(0.5)
    ax.spines['right'].set_linewidth(0.5)

    plt.savefig("../../Results/figures/Fig2d.pdf", dpi=400, bbox_inches = 'tight')


if __name__ == '__main__' :
    main()