Complete analysis of ranked clusters.

GRO_seq_TFSEE/closest_genes.py

@@ -119,3 +119,108 @@ scipy.stats.ranksums(cluster3_genes_expressed['ES_D5'],cluster3_genes_expressed[
 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'])

GRO_seq_TFSEE/closest_genes.sh

@@ -4,3 +4,11 @@ bedtools sort -i cluster_4_enhancers_locations.bed | bedtools closest -a - -b  g
 
 #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

GRO_seq_TFSEE/cluster_1_genes.txt

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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
+CEBPB,8.0,5.345,3.44,2.54,2.92
+CEBPD,0.84,0.895,0.365,0.25,0.435
+CEBPE,0.005,0.015,0.01,0.0,0.015
+CEBPG,8.805,9.6,10.255,10.94,10.555
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+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
+GCM2,0.08,0.045,0.035,0.005,0.02
+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
+NFIC,2.17,4.23,4.19,2.34,4.375
+NFIX,0.055,0.025,0.02,0.335,0.205
+NKX2-3,0.0,0.0,4.155,0.005,0.0
+NR1H3,17.065,15.795,9.21,9.02,7.935
+NR2F6,61.655,57.925,41.47,93.865,67.675
+NR4A2,0.06,0.185,0.215,0.595,1.415
+NRF1,12.775,12.645,15.66,15.51,16.51
+PAX5,0.055,0.0,0.03,0.005,0.025
+PAX6,0.145,0.58,0.07,0.135,25.865
+PITX3,0.07,0.115,0.02,0.0,0.01
+RARA,6.195,10.955,11.025,33.47,26.135
+RFX1,10.79,10.71,6.615,9.515,11.335
+RXRB,8.365,11.415,10.39,15.91,18.145
+SIX3,0.005,6.42,0.105,0.015,0.025
+SMAD2,33.815,63.24,29.905,33.295,33.98
+SMAD3,18.595,12.95,10.205,20.925,35.53
+SMAD4,54.035,47.595,59.69,60.76,66.16
+SOX10,2.095,13.225,0.425,0.18,0.285
+SOX11,6.49,3.32,31.61,65.87,13.725
+SOX4,89.935,50.825,149.125,233.1,229.0
+SOX8,4.705,1.955,0.19,0.72,1.02
+SPZ1,0.025,0.0,0.0,0.0,0.0
+SREBF1,19.795,25.405,19.81,19.805,22.11
+SREBF2,96.135,75.58,60.99,36.32,32.135
+STAT3,27.22,8.29,7.995,15.825,16.81
+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
+TFEB,0.88,0.18,0.235,0.5,0.5
+TFEC,0.0,0.0,0.0,0.005,0.005
+TLX1,0.0,0.0,0.015,0.0,0.005
+TP53,103.305,35.18,51.03,48.995,49.785
+USF1,26.51,20.425,28.505,43.945,32.515
+VDR,0.425,0.055,0.095,0.085,0.175
+XBP1,62.435,68.655,96.21,51.89,85.765
+YY1,67.84,55.3,61.2,49.025,62.51
+ZBTB7B,12.2,9.37,10.2,12.825,13.09
+ZEB1,0.035,0.31,0.0,0.155,0.36
+ZIC1,0.115,0.12,0.16,0.07,0.06
+ZIC4,0.02,0.0,0.0,0.025,0.045

GRO_seq_TFSEE/cluster_2_tfs.csv

+symbol,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10
+DMBX1,0.01,0.07,0.485,0.0,0.045
+ELF4,0.89,0.73,2.49,0.68,3.03
+HIC2,8.515,14.585,17.16,15.31,16.66
+ID4,1.555,1.32,84.935,8.975,47.94
+KLF12,3.72,4.185,8.88,8.085,9.125
+MAF,0.595,0.41,5.81,9.175,4.865
+MZF1,8.885,12.365,17.5,26.0,23.355
+NFE2L2,18.59,23.265,27.355,42.5,29.845
+NKX2-8,0.0,0.0,2.84,0.0,0.01
+OTX1,0.28,1.395,6.275,0.635,0.33
+PAX1,0.005,0.005,3.895,1.11,0.035
+PAX9,0.155,0.07,1.1,0.055,0.16
+PRDM1,0.255,33.89,7.5,8.065,0.79
+RARB,0.125,9.755,14.765,36.265,2.48
+REL,0.91,1.47,2.805,3.635,3.23
+RHOXF1,0.075,0.27,0.885,1.405,0.58
+RXRA,13.38,31.955,56.265,27.41,34.875
+RXRG,0.005,5.185,0.59,0.0,0.755
+SNAI2,0.09,0.055,0.565,0.36,0.66
+SP3,16.795,14.03,30.47,22.055,22.605
+TCF3,136.215,124.305,134.515,121.675,163.045
+TEAD3,19.265,15.565,27.705,25.44,35.495
+TFAP2A,0.01,0.19,2.715,0.39,0.52
+ZNF354C,2.225,2.455,4.71,4.635,4.235