remove previous version

pipelines/prueba.py

-# Change Hi-C ranges
-text = "hic_order = "   # if any line contains this text, I want to modify the whole line.
-new_text = "hic_order = " + "dddddddd"
-pathRanges = "/home/bscuser/3DMetabolism/pipelines/ranges.py"
-with open(pathRanges,"r") as f:
-    for line in f.readlines():
-        if text in line:
-            line = new_text
-            print(line)
-    f.close()

pipelines/statistics.py

-import os
-import sys
-import time
-import datetime
-import pandas as pd
-import numpy as np
-import pyranges as pr
-from paths import *
-
-
-chromatin_category = sys.argv[1]
-chromatin_bed = sys.argv[2]
-marks_dataset = sys.argv[3]
-
-# lamin places
-intervals = {'InsideLAD':'InsideLAD','250kb':'0kb-250kb', '1000kb':'250kb-1000kb',
-               '2500kb':'1000kb-2500kb', '5000kb':'2500kb-5000kb', 'Center':'Center'}
-LAD_names = list(intervals.keys())
-
-
-# Normalization
-def div_to_val(df, **kwargs):
-    col = kwargs['divisor']
-    df.loc[:, 'Val'] = df['Val'].div(df[col], axis = 0)
-    return df
-
-
-# Get marks path
-marcas = {}
-for rep in os.listdir(os.path.join(selpath, marks_dataset)):
-    marcas[rep] = []
-    for fnam in os.listdir(os.path.join(selpath, marks_dataset, rep)):
-        if not fnam.startswith('H'):
-            continue
-        marcas[rep].append(os.path.join(selpath, marks_dataset, rep, fnam))
-
-
-# Open chromatin bed:
-chromatin_dir = os.path.join(chromatinpath, chromatin_category, chromatin_bed)
-
-for fnam in os.listdir(chromatin_dir):
-    if fnam.endswith('_clean.bed'):
-        file = os.path.join(chromatin_dir, fnam)
-        chromatin_gr = pr.read_bed(file)
-
-
-# Get ovelapped intervals and normalization by ranges or distance
-val_marks = {}
-for mark in marcas.keys():
-    for fnam in marcas[mark]:
-        mark_df = pr.read_bed(fnam)
-        gr = chromatin_gr.count_overlaps(mark_df, overlap_col="Val")
-        if chromatin_category == "transcription":
-            val_marks[mark] = gr.apply(div_to_val, divisor='Score')
-            cols = ['Chromosome', 'Start','End','Genes','Exp','Val','Lamin']
-        else:
-            gr.Lengths = gr.lengths()
-            val_marks[mark] = gr.apply(div_to_val, divisor='Lengths')
-            cols = ['Chromosome', 'Start', 'End', 'Val', 'Lengths', 'Lamin']
-
-
-# This block is dedicated to the construction of a new chromatin bed with all their intervals
-# classified according to the place occupated in chromatin respectively to Lamin B1 and the numbers
-# of histone marks loceted in them for each mark.
-range_LADs = {}
-for mark in marcas.keys():
-
-    range_LADs[mark] = { i : [] for i in LAD_names }
-
-    for ind, win in enumerate(LAD_names):
-
-        results = os.path.join(chromatin_dir, marks_dataset + "_results")
-        val_path = os.path.join(results, 'values')
-        stat_path = os.path.join(results, 'statistics')
-
-        # Create Results folders
-        if not os.path.exists(results):
-            for dir in [results, val_path, stat_path]:
-                os.makedirs(dir)
-
-        # Center is the last step of the analysis
-        if win is not 'Center':
-            fnam_LAD = os.path.join(lamin_b1,'window', "Lamin_B1." + win + ".bed")
-
-            LAD_df = pr.read_bed(fnam_LAD)
-            range_LADs[mark][win] = {'LAD':[], 'noLAD':[]}
-
-            # Marks which ovelapps current chromatin region in each space determinated by the distance to Lamin
-
-            ## Initial stating range
-            if win is 'InsideLAD':
-                print("mark: " + mark + " LAD range: " + win)
-                gr = val_marks[mark].count_overlaps(LAD_df,
-                                                    overlap_col="Lamin_count")
-                df = gr.df
-                df['Lamin'] = "noLAD"
-
-            ## Nexts ranges towards the center
-            else:
-                print("mark: " + mark + " LAD range: " + win)
-                gr = pr.PyRanges(range_LADs[mark][LAD_names[ind-1]]['noLAD'])
-                gr = gr.count_overlaps(LAD_df, overlap_col="Lamin_count")
-                df = gr.df
-
-
-            # Filtering those chromatin region whitn al least one mark in the region that we are checking
-            df.loc[df['Lamin_count'] > 0, 'Lamin'] = intervals[win]
-            df = df.drop('Lamin_count', axis=1)
-
-            # Reset index of dataframe
-            noLAD = df.loc[df['Lamin'] =="noLAD"].reset_index(drop=True)
-            LAD = df.loc[df['Lamin'] ==intervals[win]].reset_index(drop=True)
-
-            # Save the subdataframe for each lamin place and the rest of intervals to be re-checked
-            # for other lamin places
-            range_LADs[mark][win]['noLAD'] = noLAD
-            range_LADs[mark][win]['LAD'] = LAD
-
-        else:
-            print("mark: " + mark + " LAD range: " + win)
-            df = range_LADs[mark][LAD_names[ind-1]]['noLAD']
-            df['Lamin'] = win
-            range_LADs[mark][win] = df
-
-
-# Concat all subdataframes in a unique datrame with al the classfied chromatin regions
-for mark in marcas.keys():
-    if chromatin_category == 'transcription':
-        dfVal = pd.DataFrame(columns=['Chromosome', 'Start', 'End',
-                                      'Genes', 'Exp', 'Val', 'Lamin'])
-    else:
-        dfVal= pd.DataFrame(columns=['Chromosome', 'Start', 'End',
-                                      'Val', 'Lengths', 'Lamin'])
-
-    dfStat = pd.DataFrame(columns=['Mark', 'Lamin', 'Mean', 'Std'])
-
-    for win in LAD_names:
-        if win is not 'Center':
-            df = range_LADs[mark][win]['LAD']
-            df.columns = cols
-            dfVal = pd.concat([dfVal,df])
-        else:
-            df = range_LADs[mark][win]
-            df.columns = cols
-            dfVal = pd.concat([dfVal,df])
-
-    # Statistics: mean and std by lamin space
-    # pd.set_option('display.float_format', lambda x: '%.5f' % x)
-    with open(os.path.join(stat_path, mark + "_stats.bed"),'w') as f:
-        for win in LAD_names:
-            val_by_range = dfVal.loc[dfVal['Lamin'] == intervals[win], 'Val']
-            mean_by_range = val_by_range.mean()
-            std_by_range = val_by_range.std()
-
-            print(mark, win, "mean: " + str(mean_by_range), "std: " + str(std_by_range))
-            f.write('{}\t{}\t{}\t{}\n'.format(mark, intervals[win], mean_by_range, std_by_range))
-
-    # Sort values and reset the dataframe index
-    dfVal= dfVal.sort_values(['Chromosome', 'Start', 'End'],
-                        ascending=True).reset_index(drop = True)
-
-    # Save statistics
-    dfVal.to_csv(os.path.join(val_path, mark + "_val.bed"),
-                header=False, index= False,  sep='\t')

pipelines/tendency.py

-import os
-import sys
-import seaborn as sns
-import numpy as np
-import pandas as pd
-from paths import *
-from matplotlib import pyplot as plt
-
-
-chromatin_category = sys.argv[1]
-chromatin_bed = sys.argv[2]
-marks_dataset = sys.argv[3]
-
-
-# Lamin places
-windows = ['InsideLAD', '0kb-250kb', '250kb-1000kb',
-           '1000kb-2500kb', '2500kb-5000kb', 'Center']
-
-marks = os.listdir(os.path.join(selpath, marks_dataset))
-chromatin_dir = os.path.join(chromatinpath, chromatin_category, chromatin_bed)
-results_path = os.path.join(chromatin_dir, marks_dataset + '_results', 'values')
-num_ac = len([m for m in marks if "ac" in m])
-num_met = len([m for m in marks if "me" in m])
-
-norm_data = {}
-for w in windows:
-    norm_data[w] = {}
-    for mark in os.listdir(results_path):
-        mark_path = os.path.join(results_path, mark)
-        dfVal = pd.read_csv(mark_path, sep = "\t", header=None)
-
-        if chromatin_category == "transcription":
-            dfVal.columns = ['Chromosomes','Start','End', 'Genes',
-                             'Exp', 'Val', 'Lamin']
-        else:
-            dfVal.columns = ['Chromosomes','Start','End',
-                             'Val','Lengths','Lamin']
-
-        # Normalization of histone marks number.
-        # Dvide the number of marks in each bed interval by the sum of all marks in all lamin places
-        m = mark.rsplit('_', 1)[0]
-        total = dfVal['Val'].sum()
-        norm_data[w][m] = dfVal.loc[dfVal['Lamin'] == w, 'Val'].div(total) * 1000
-
-
-nn = 0
-# Plot tendency
-plt.figure(figsize=(12, 12))
-for num, what in enumerate(['Acethylation', 'Methylation'], 1):
-    axe = plt.subplot(2,1,num)
-    ys = []
-    these_marks = sorted([m for m in marks if what[:2].lower() in m],
-                         key=lambda x: np.mean(norm_data[windows[-1]][x]))
-    for n, m in enumerate(these_marks):
-
-        tendency = np.mean(norm_data[windows[-1]][m]) - np.mean(norm_data[windows[0]][m])
-        if tendency <= 0:
-            tendency = 0
-
-        plt.plot(range(len(windows)),
-                 [np.mean(norm_data[w][m]) for w in windows], 'o-',
-                 alpha=min(1, 0.1 + 1 * tendency),
-                 color='k', lw=3, zorder=1000)
-        y = np.mean(norm_data[windows[-1]][m])
-        if chromatin_category == 'transcription':
-            y2 = 0.05 + 0.8 * n / len(these_marks)
-            plt.ylim(0, 0.8)
-        else:
-            y2 = 0.05 + 7 * n / len(these_marks)
-            #plt.ylim(0, 6)
-        plt.text(len(windows) - 0.4, y2, m, va='center')
-        plt.plot([len(windows) - 0.9, len(windows) - 0.4], [y, y2], 'k:', lw=1)
-
-
-    _ = plt.xticks(range(len(windows)), windows)
-    ylim = plt.ylim()
-    plt.plot([0, len(windows) - 1], [0] * 2, 'k-')
-    plt.xlim(plt.xlim()[0], len(windows) + 2)
-    plt.title(what)
-    plt.ylabel('Normalized histone marks per CAGE-seq read')
-    axe.spines['right'].set_visible(False)
-    axe.spines['top'].set_visible(False)
-    axe.spines['bottom'].set_visible(False)
-
-plot_path = os.path.join(chromatin_dir, marks_dataset + '_results', 'plot')
-if not os.path.exists(plot_path):
-    os.makedirs(plot_path)
-
-print("Generating plot...")
-plt.savefig(os.path.join(plot_path, 'normalized_marks.png'))
-print("Process finished")