Mercurial > repos > glogobyte > viztool

changeset 14:e51ebc767701 draft

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Deleted selected files
author glogobyte
date Wed, 28 Oct 2020 07:34:44 +0000
parents 41de387b3982
children 6db3bd727fde
files viz_ultra.py
diffstat 1 files changed, 0 insertions(+), 271 deletions(-) [+]
line wrap: on
line diff
--- a/viz_ultra.py	Wed Oct 28 07:32:14 2020 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,271 +0,0 @@
-import argparse
-from functions import *
-from viz_graphs import *
-import sys
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-import matplotlib.font_manager as font_manager
-import time
-from multiprocessing import Process, Queue, Lock, Pool, Manager, Value
-
-
-##################################################################################################################################################################################################################
-
-def top_diff(miRNA_info, number,flag,l):
-
-    Kind=[]
-
-    miRNA_info.sort(key = lambda x: abs(x[1]),reverse=True)
-    miRNA_info = miRNA_info[:number]
-    miRNA_info.sort(key = lambda x: x[0])
-
-    for x in miRNA_info:
-        if x[1] > 0:
-           Kind.append(True)
-        elif x[1] < 0:
-           Kind.append(False)
-        else:
-           Kind.append("Zero")
-
-    top_miRNA = {"Names": [x[0] for x in miRNA_info],
-                  "Log2FC": [x[1] for x in miRNA_info],
-                  "Kind": Kind};
-
-    df_miRNA = pd.DataFrame(data=top_miRNA)
-    df_miRNA = df_miRNA.sort_values(by=['Names'])
-    if df_miRNA.empty==False:
-     h1=df_miRNA.plot.barh(x= 'Names',y='Log2FC',color=df_miRNA.Kind.map({True: 'g', False: 'r', 'Zero':'k'})) 
-     figure = plt.gcf()  # get current figure
-     figure.set_size_inches(5, 12) # set figure's size manually to your full screen (32x18)
-     up_reg = mpatches.Patch(color='green', label='Upregulated')
-     down_reg = mpatches.Patch(color='red', label='Downregulated')
-     font = font_manager.FontProperties(weight='bold', style='normal')
-     l3 = plt.legend(handles=[up_reg,down_reg],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
-     h1.set_ylabel(" ", fontsize=3, fontweight='bold')
-     h1.set_xlabel("Log2FC", fontsize=12, fontweight='bold')
-     plt.axvline(x=0, color="k")
-
-     plt.grid(axis='y', linewidth=0.2)
-     plt.grid(axis='x', linewidth=0.2)
-     if flag=='t':
-        plt.savefig('tem.png', bbox_inches='tight', dpi=300)
-     if flag=='nt':
-        plt.savefig('non.png', bbox_inches='tight', dpi=300)
-
-####################################################################################################################################################################################################################
-
-def unique(sequence):
-    seen = set()
-    return [x for x in sequence if not (x in seen or seen.add(x))]
-
-###########################################################################################################################################################################################################################################################################
-
-def top_scatter_non(matures,isoforms,non_temp,uni_names,number):
-
-    mat_names=[]
-    mat_log2fc=[]
-
-    iso_names=[]
-    iso_log2fc=[]
-
-    non_temp_names=[]
-    non_temp_log2fc=[]
-
-    count=0
-    for x in uni_names:
-        flag = False
-        if count<number:
-          for y in matures:
-            if x in y[0]:
-               mat_log2fc.append(y[1])
-               mat_names.append(x)
-               flag=True
-          for y in isoforms:
-            if x in y[0]:
-               iso_log2fc.append(y[1])
-               iso_names.append(x)
-               flag=True
-          for y in non_temp:
-            if x in y[0]:
-               non_temp_log2fc.append(y[1])
-               non_temp_names.append(x)
-               flag=True
-          if flag==True:
-             count+=1
-
-    mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
-    iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
-    non_df = pd.DataFrame(dict(names=non_temp_names, log2fc= non_temp_log2fc))
-
-    iso_df.sort_values(by=['names'])
-    mat_df.sort_values(by=['names'])
-    non_df.sort_values(by=['names'])
-
-    fig, ax = plt.subplots()
-
-    h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red')
-    h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green')
-    h2=ax.scatter(non_df['log2fc'],non_df['names'],edgecolors='k',linewidth=1, marker='o', c='blue')
-
-    l3 = plt.legend([h1,h2,h3],["Reference miRNA","Non-template","Template isomiRs"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
-    plt.axvline(x=0, color="k")
-    plt.grid(axis='y', linewidth=0.2)
-    plt.grid(axis='x', linewidth=0.2)
-    plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
-    plt.yticks(rotation=0,ha="right", fontsize=10)
-    plt.xticks(rotation=0,ha="right", fontsize=10)
-    plt.tight_layout()
-    figure = plt.gcf()  # get current figure
-    figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
-    plt.savefig('a2.png', bbox_inches='tight', dpi=300)
-
-#########################################################################################################################################################################################################################################
-def top_scatter_tem(matures,isoforms,uni_names,number):
-
-    mat_names=[]
-    mat_log2fc=[]
-
-    iso_names=[]
-    iso_log2fc=[]
-
-    count=0
-    for x in uni_names:
-        flag = False
-        if count<number:
-          for y in matures:
-            if x in y[0]:
-               mat_log2fc.append(y[1])
-               mat_names.append(x)
-               flag=True
-          for y in isoforms:
-            if x in y[0]:
-               iso_log2fc.append(y[1])
-               iso_names.append(x)
-               flag=True
-          if flag==True:
-             count+=1
-
-    mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
-    iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
-
-    iso_df.sort_values(by=['names'])
-    mat_df.sort_values(by=['names'])
-
-    fig, ax = plt.subplots()
-
-    h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red')
-    h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green')
-
-    l3 = plt.legend([h1,h3],["Reference miRNA","Template isomiRs"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
-    plt.axvline(x=0, color="k")
-    plt.grid(axis='y', linewidth=0.2)
-    plt.grid(axis='x', linewidth=0.2)
-    plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
-    plt.yticks(rotation=0,ha="right", fontsize=10)
-    plt.xticks(rotation=0,ha="right", fontsize=10)
-    plt.tight_layout()
-    figure = plt.gcf()  # get current figure
-    figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
-    plt.savefig('a2.png', bbox_inches='tight', dpi=300)
-
-
-##############################################################################################################################################################################################################################################
-def preproccess(non_templated,matures,isoforms,log2fc,pval):
-
-       non_temp = [[x[0],float(x[1]),float(x[2])] for x in non_templated if abs(float(x[1]))>log2fc and float(x[2])<pval]
-       mat = [[x[0],float(x[1]),float(x[2])] for x in matures if abs(float(x[1]))>log2fc and float(x[2])<pval]
-       iso = [[x[0],float(x[1]),float(x[2])] for x in isoforms if abs(float(x[1]))>log2fc and float(x[2])<pval]
-       mat_iso = mat+iso
-
-       if not non_temp and not mat and not iso:
-          sys.exit("There aren't entries which meet these criteria")
-
-       mat.sort(key = lambda x: abs(float(x[1])),reverse=True)
-       iso.sort(key = lambda x: abs(float(x[1])),reverse=True)
-       non_temp.sort(key = lambda x: abs(float(x[1])),reverse=True)
-
-       all=mat+iso+non_temp
-       all.sort(key = lambda x: abs(float(x[1])), reverse=True)
-       names=[x[0].split("_")[0] for x in all]
-       uni_names=unique(names)
-
-       diff_non_templated = [[x[0],float(x[1]),float(x[2])] for x in non_templated if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
-       diff_matures = [[x[0],float(x[1]),float(x[2])] for x in matures if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
-       diff_isoforms = [[x[0],float(x[1]),float(x[2])] for x in isoforms if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
-
-       diff_matures.sort(key = lambda x: abs(float(x[1])),reverse=True)
-       diff_isoforms.sort(key = lambda x: abs(float(x[1])),reverse=True)
-       diff_non_templated.sort(key = lambda x: abs(float(x[1])),reverse=True)
-
-       return diff_matures,diff_isoforms,diff_non_templated,uni_names,non_temp,mat_iso
-
-##################################################################################################################################################################################################################################################################
-starttime = time.time()
-
-parser = argparse.ArgumentParser()
-parser.add_argument("-in", "--input", help="choose type of analysis", action="store")
-parser.add_argument("-p_value", "--pval", help="choose type of analysis", action="store")
-parser.add_argument("-fc", "--log2fc", help="choose type of analysis", action="store")
-parser.add_argument("-top", "--top_mirnas", help="choose type of analysis", action="store")
-parser.add_argument("-tool_dir", "--tool_directory", help="tool directory path", action="store")
-parser.add_argument("-statistic", "--stat", help="tool directory path", action="store")
-parser.add_argument("-diff_tool", "--tool", help="tool directory path", action="store")
-
-args = parser.parse_args()
-
-l=Lock()
-number = int(args.top_mirnas)
-log2fc = float(args.log2fc)
-pval = float(args.pval)
-
-if args.tool=="2":
-
-   raw_EdgeR = read(args.input,0)
-   EdgeR = [x.rstrip("\n").split("\t") for x in raw_EdgeR]
-   del EdgeR[0]
-
-   if args.stat=="1":
-      non_templated = [[x[0],x[1],x[4]] for x in EdgeR if "__" in x[0] and x[1]!="NA" and x[4]!="NA"]
-      matures = [[x[0],x[1],x[4]] for x in EdgeR if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[4]!="NA"]
-      isoforms = [[x[0],x[1],x[4]] for x in EdgeR if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[4]!="NA"]
-   else:
-      non_templated = [[x[0],x[1],x[5]] for x in EdgeR if "__" in x[0] and x[1]!="NA" and x[5]!="NA"]
-      matures = [[x[0],x[1],x[5]] for x in EdgeR if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[5]!="NA"]
-      isoforms = [[x[0],x[1],x[5]] for x in EdgeR if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[5]!="NA"]
-
-if args.tool=="1":
-
-   raw_Deseq = read(args.input,0)
-   Deseq = [x.rstrip("\n").split("\t") for x in raw_Deseq]
-
-   if args.stat=="1":
-      non_templated = [[x[0],x[2],x[5]] for x in Deseq if "__" in x[0] and x[2]!="NA" and x[5]!="NA"] 
-      matures = [[x[0],x[2],x[5]] for x in Deseq if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[5]!="NA"]
-      isoforms = [[x[0],x[2],x[5]] for x in Deseq if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[5]!="NA"]
-   else:
-      non_templated = [[x[0],x[2],x[6]] for x in Deseq if "__" in x[0] and x[2]!="NA" and x[6]!="NA"] 
-      matures = [[x[0],x[2],x[6]] for x in Deseq if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[6]!="NA"]
-      isoforms = [[x[0],x[2],x[6]] for x in Deseq if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[6]!="NA"]
-
-
-diff_matures,diff_isoforms,diff_non_templated,names,non_temp,mat_iso = preproccess(non_templated,matures,isoforms,log2fc,pval)
-
-if non_templated!=[]:
-   analysis="2"
-   p=[Process(target=top_diff,args=(non_temp,number,"nt",l))]
-   p.extend([Process(target=top_diff,args=(mat_iso,number,"t",l))])
-   p.extend([Process(target=top_scatter_non,args=(diff_matures,diff_isoforms,diff_non_templated,names,number))])
-
-else:
-   analysis="1"
-   p=[Process(target=top_diff,args=(mat_iso,number,"t"))]
-   p.extend([Process(target=top_scatter_tem,args=(diff_matures,diff_isoforms,names,number))])
-
-[x.start() for x in p]
-[x.join() for x in p]
-
-pdf_after_DE(analysis,args.top_mirnas)
-
-print('That took {} seconds'.format(time.time() - starttime))
-