analysis/GRO_seq_TFSEE/closest_genes.py

+import pandas as pd
+import numpy as np
+import csv
+import matplotlib.pyplot as plt
+import seaborn as sns
+import scipy
+
+# Find nearest genes
+
+# Grab TF FPKM levels
+fpkm = pd.read_table("rna.tsv")
+gene_names_mapping = pd.read_csv("../gencode.v19.annotation_protein_coding_ids.txt",names=['gene_id', 'symbol'])
+fpkm_symbol = fpkm.merge(gene_names_mapping)
+fpkm_symbol = fpkm.set_index(['gene_id'])
+
+# Enhancers
+enhancers_universe = pd.DataFrame.from_csv("GRO-seq_enhancers.bed", sep="\t", header=None, index_col=3)
+
+
+# Read in cluster 4 enhancers
+cluster_4 = pd.DataFrame.from_csv("cluster_4_enhancers.csv", sep=",", header=0, index_col=0)
+
+# Choose enhacners exprssed in cluster 4
+enhancers_universe_cluster_4 = enhancers_universe.loc[cluster_4.index.values]
+enhancers_universe_cluster_4.to_csv("cluster_4_enhancers_locations.bed", sep="\t",header=None, index=False)
+
+
+# Read in nearest genes
+genes_id = pd.DataFrame.from_csv("cluster_4_genes.txt", sep="\t", header=None, index_col=None)
+
+needed_rows = [row for row in fpkm_symbol.index if row in genes_id[0].values]
+cluster4_genes_expressed = fpkm_symbol.loc[needed_rows]
+
+
+# col_colors
+plt.style.use('classic')
+colors = ["#FFD66F","#2E6A44","#862743", "#4FA6C7", "#3398CC"]
+medianprops = dict(linestyle='-', linewidth=2, color='black')
+box = cluster4_genes_expressed.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
+plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 5)
+plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 5)
+
+for patch, color in zip(box['boxes'], colors):
+    patch.set_facecolor(color)
+
+plt.tick_params(axis='y', direction='out')
+plt.tick_params(axis='x', direction='out')
+plt.tick_params(top='off', right='off')
+plt.grid(b=False)
+plt.ylim((-5,60))
+plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
+plt.savefig('box_plot_cluster_4_genes_fpkm.png')
+plt.clf()
+
+# Cluster tfs 0.05
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D0'],cluster4_genes_expressed['ES_D2'])
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D0'],cluster4_genes_expressed['ES_D5'])
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D0'],cluster4_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D0'],cluster4_genes_expressed['ES_D10'])
+
+
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D2'],cluster4_genes_expressed['ES_D5'])
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D2'],cluster4_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D2'],cluster4_genes_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D5'],cluster4_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D5'],cluster4_genes_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster4_genes_expressed['ES_D7'],cluster4_genes_expressed['ES_D10'])
+
+
+# Read in cluster 3 enhancers
+cluster_3 = pd.DataFrame.from_csv("cluster_3_enhancers.csv", sep=",", header=0, index_col=0)
+
+# Choose enhacners exprssed in cluster 3
+enhancers_universe_cluster_3 = enhancers_universe.loc[cluster_3.index.values]
+enhancers_universe_cluster_3.to_csv("cluster_3_enhancers_locations.bed", sep="\t",header=None, index=False)
+
+
+# Read in nearest genes
+genes_id = pd.DataFrame.from_csv("cluster_3_genes.txt", sep="\t", header=None, index_col=None)
+
+needed_rows = [row for row in fpkm_symbol.index if row in genes_id[0].values]
+cluster3_genes_expressed = fpkm_symbol.loc[needed_rows]
+
+
+# col_colors
+plt.style.use('classic')
+colors = ["#FFD66F","#2E6A44","#862743", "#4FA6C7", "#3398CC"]
+medianprops = dict(linestyle='-', linewidth=2, color='black')
+box = cluster3_genes_expressed.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
+plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 5)
+plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 5)
+
+for patch, color in zip(box['boxes'], colors):
+    patch.set_facecolor(color)
+
+plt.tick_params(axis='y', direction='out')
+plt.tick_params(axis='x', direction='out')
+plt.tick_params(top='off', right='off')
+plt.grid(b=False)
+plt.ylim((-5,50))
+plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
+plt.savefig('box_plot_cluster_3_genes_fpkm.png')
+plt.clf()
+
+# Cluster tfs 1 e-4
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D0'],cluster3_genes_expressed['ES_D2'])
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D0'],cluster3_genes_expressed['ES_D5'])
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D0'],cluster3_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D0'],cluster3_genes_expressed['ES_D10'])
+
+
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D2'],cluster3_genes_expressed['ES_D5'])
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D2'],cluster3_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D2'],cluster3_genes_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D5'],cluster3_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D5'],cluster3_genes_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D7'],cluster3_genes_expressed['ES_D10'])
+
+
+
+# Read in cluster 1 enhancers
+cluster_1 = pd.DataFrame.from_csv("cluster_1_enhancers.csv", sep=",", header=0, index_col=0)
+
+# Choose enhacners exprssed in cluster 1
+enhancers_universe_cluster_1 = enhancers_universe.loc[cluster_1.index.values]
+enhancers_universe_cluster_1.to_csv("cluster_1_enhancers_locations.bed", sep="\t",header=None, index=False)
+
+
+# Read in nearest genes
+genes_id = pd.DataFrame.from_csv("cluster_1_genes.txt", sep="\t", header=None, index_col=None)
+
+needed_rows = [row for row in fpkm_symbol.index if row in genes_id[0].values]
+cluster1_genes_expressed = fpkm_symbol.loc[needed_rows]
+
+
+# col_colors
+plt.style.use('classic')
+colors = ["#FFD66F","#2E6A44","#862743", "#4FA6C7", "#3398CC"]
+medianprops = dict(linestyle='-', linewidth=2, color='black')
+box = cluster1_genes_expressed.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
+plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 5)
+plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 5)
+
+for patch, color in zip(box['boxes'], colors):
+    patch.set_facecolor(color)
+
+plt.tick_params(axis='y', direction='out')
+plt.tick_params(axis='x', direction='out')
+plt.tick_params(top='off', right='off')
+plt.grid(b=False)
+plt.ylim((-5,55))
+plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
+plt.savefig('box_plot_cluster_1_genes_fpkm.png')
+plt.clf()
+
+# Cluster tfs 1 e-4
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D0'],cluster1_genes_expressed['ES_D2'])
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D0'],cluster1_genes_expressed['ES_D5'])
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D0'],cluster1_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D0'],cluster1_genes_expressed['ES_D10'])
+
+
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D2'],cluster1_genes_expressed['ES_D5'])
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D2'],cluster1_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D2'],cluster1_genes_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D5'],cluster1_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D5'],cluster1_genes_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster1_genes_expressed['ES_D7'],cluster1_genes_expressed['ES_D10'])
+
+
+# Read in cluster 2 enhancers
+cluster_2 = pd.DataFrame.from_csv("cluster_2_enhancers.csv", sep=",", header=0, index_col=0)
+
+# Choose enhacners exprssed in cluster 2
+enhancers_universe_cluster_2 = enhancers_universe.loc[cluster_2.index.values]
+enhancers_universe_cluster_2.to_csv("cluster_2_enhancers_locations.bed", sep="\t",header=None, index=False)
+
+
+# Read in nearest genes
+genes_id = pd.DataFrame.from_csv("cluster_2_genes.txt", sep="\t", header=None, index_col=None)
+
+needed_rows = [row for row in fpkm_symbol.index if row in genes_id[0].values]
+cluster2_genes_expressed = fpkm_symbol.loc[needed_rows]
+
+
+# col_colors
+plt.style.use('classic')
+colors = ["#FFD66F","#2E6A44","#862743", "#4FA6C7", "#3398CC"]
+medianprops = dict(linestyle='-', linewidth=2, color='black')
+box = cluster2_genes_expressed.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
+plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 5)
+plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 5)
+
+for patch, color in zip(box['boxes'], colors):
+    patch.set_facecolor(color)
+
+plt.tick_params(axis='y', direction='out')
+plt.tick_params(axis='x', direction='out')
+plt.tick_params(top='off', right='off')
+plt.grid(b=False)
+plt.ylim((-5,50))
+plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
+plt.savefig('box_plot_cluster_2_genes_fpkm.png')
+plt.clf()
+
+# Cluster tfs 1 e-4
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D0'],cluster2_genes_expressed['ES_D2'])
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D0'],cluster2_genes_expressed['ES_D5'])
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D0'],cluster2_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D0'],cluster2_genes_expressed['ES_D10'])
+
+
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D2'],cluster2_genes_expressed['ES_D5'])
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D2'],cluster2_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D2'],cluster2_genes_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D5'],cluster2_genes_expressed['ES_D7'])
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D5'],cluster2_genes_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster2_genes_expressed['ES_D7'],cluster2_genes_expressed['ES_D10'])

analysis/GRO_seq_TFSEE/closest_genes.sh

+#Closest genes Cluster 4
+bedtools sort -i cluster_4_enhancers_locations.bed | bedtools closest -a - -b  gencode.v19.annotation_protein_coding_sorted.gtf | cut -f12 | cut -f1 -d ';' | cut -f2 -d ' ' | sort | uniq | sed 's/"//g' > cluster_4_genes.txt
+
+
+#Closest genes Cluster 3
+bedtools sort -i cluster_3_enhancers_locations.bed | bedtools closest -a - -b  gencode.v19.annotation_protein_coding_sorted.gtf | cut -f12 | cut -f1 -d ';' | cut -f2 -d ' ' | sort | uniq | sed 's/"//g' > cluster_3_genes.txt
+
+
+#Closest genes Cluster 1
+bedtools sort -i cluster_1_enhancers_locations.bed | bedtools closest -a - -b  gencode.v19.annotation_protein_coding_sorted.gtf | cut -f12 | cut -f1 -d ';' | cut -f2 -d ' ' | sort | uniq | sed 's/"//g' > cluster_1_genes.txt
+
+
+#Closest genes Cluster 2
+bedtools sort -i cluster_2_enhancers_locations.bed | bedtools closest -a - -b  gencode.v19.annotation_protein_coding_sorted.gtf | cut -f12 | cut -f1 -d ';' | cut -f2 -d ' ' | sort | uniq | sed 's/"//g' > cluster_2_genes.txt

analysis/GRO_seq_TFSEE/cluster1_enriched_tfs.csv

+,Unnamed: 0,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10,cluster,early,late,diff,rank
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+103,RFX1,0.38517802445200006,0.237903767304,-0.9521139055560001,-0.826459696747,-0.61830466648,0,-0.20542064286775,-0.9521139055560001,0.7466932626882501,56.0
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analysis/GRO_seq_TFSEE/cluster2_enriched_tfs.csv

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analysis/GRO_seq_TFSEE/cluster3_enriched_tfs.csv

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analysis/GRO_seq_TFSEE/cluster4_enriched_tfs.csv

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analysis/GRO_seq_TFSEE/cluster_1_tfs.csv

+symbol,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10
+AR,2.645,0.195,1.255,0.755,0.73
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+CEBPD,0.84,0.895,0.365,0.25,0.435
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+CEBPG,8.805,9.6,10.255,10.94,10.555
+DDIT3,3.545,7.775,6.02,10.41,8.37
+EGR2,0.39,0.005,0.005,0.49,0.305
+EGR3,0.02,0.09,0.025,1.28,0.49
+EGR4,0.005,0.005,0.01,0.425,0.205
+ELF3,2.21,3.685,3.88,52.51,13.54
+ESR1,0.045,0.065,0.03,0.205,0.26
+ESR2,0.28,0.155,0.28,0.415,0.68
+FIGLA,0.015,0.0,0.0,0.0,0.0
+FOSL2,1.235,0.795,1.185,50.66,7.785
+FOXC2,0.07,0.005,0.01,0.175,0.32
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+GFI1B,0.0,0.015,0.0,0.0,0.01
+GSC,0.25,204.8,0.555,0.09,0.055
+GSC2,0.1,0.0,0.0,0.0,0.015
+HIC1,0.145,0.165,0.065,0.605,0.385
+KLF14,0.03,0.025,0.0,0.0,0.03
+MAFK,4.18,2.8,3.395,5.395,7.09
+MLX,28.085,30.715,18.125,17.77,22.08
+MTF1,2.85,3.065,4.065,5.195,4.16
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+RXRB,8.365,11.415,10.39,15.91,18.145
+SIX3,0.005,6.42,0.105,0.015,0.025
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+TBX15,0.02,0.015,0.01,0.035,0.005
+TFAP2B,0.0,0.0,0.055,0.015,0.01
+TFE3,29.7,10.12,8.635,18.995,17.13
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