diff --git a/GRO_seq_TFSEE/box_plot_cluster_1_enhancers_rpkm.png b/GRO_seq_TFSEE/box_plot_cluster_1_enhancers_rpkm.png
new file mode 100644
index 0000000000000000000000000000000000000000..e63f973d2b73e3deb52669d58ed48df38e6de545
Binary files /dev/null and b/GRO_seq_TFSEE/box_plot_cluster_1_enhancers_rpkm.png differ
diff --git a/GRO_seq_TFSEE/box_plot_cluster_1_genes_fpkm.png b/GRO_seq_TFSEE/box_plot_cluster_1_genes_fpkm.png
new file mode 100644
index 0000000000000000000000000000000000000000..501d0b51c98d9b4ac2ba61813e2d03ae00f013f0
Binary files /dev/null and b/GRO_seq_TFSEE/box_plot_cluster_1_genes_fpkm.png differ
diff --git a/GRO_seq_TFSEE/box_plot_cluster_1_tfs_fpkm.png b/GRO_seq_TFSEE/box_plot_cluster_1_tfs_fpkm.png
new file mode 100644
index 0000000000000000000000000000000000000000..c6f6c819995d27f1cc510a67429c16a44433a61b
Binary files /dev/null and b/GRO_seq_TFSEE/box_plot_cluster_1_tfs_fpkm.png differ
diff --git a/GRO_seq_TFSEE/box_plot_cluster_2_enhancers_rpkm.png b/GRO_seq_TFSEE/box_plot_cluster_2_enhancers_rpkm.png
new file mode 100644
index 0000000000000000000000000000000000000000..6ba65f8d2d39e8d7a4e7814718d802c206dd0e9b
Binary files /dev/null and b/GRO_seq_TFSEE/box_plot_cluster_2_enhancers_rpkm.png differ
diff --git a/GRO_seq_TFSEE/box_plot_cluster_2_genes_fpkm.png b/GRO_seq_TFSEE/box_plot_cluster_2_genes_fpkm.png
new file mode 100644
index 0000000000000000000000000000000000000000..ae90d969345b0fb504d320656c20a2bc1d516e3d
Binary files /dev/null and b/GRO_seq_TFSEE/box_plot_cluster_2_genes_fpkm.png differ
diff --git a/GRO_seq_TFSEE/box_plot_cluster_2_tfs_fpkm.png b/GRO_seq_TFSEE/box_plot_cluster_2_tfs_fpkm.png
new file mode 100644
index 0000000000000000000000000000000000000000..ffefd75a21462575b44c70b8b2015f6f4a3bbcad
Binary files /dev/null and b/GRO_seq_TFSEE/box_plot_cluster_2_tfs_fpkm.png differ
diff --git a/GRO_seq_TFSEE/box_plot_cluster_3_genes_fpkm.png b/GRO_seq_TFSEE/box_plot_cluster_3_genes_fpkm.png
new file mode 100644
index 0000000000000000000000000000000000000000..9fdeed63f8203922f59fb9a6b1f5dd7f732cef4e
Binary files /dev/null and b/GRO_seq_TFSEE/box_plot_cluster_3_genes_fpkm.png differ
diff --git a/GRO_seq_TFSEE/box_plot_cluster_4_genes_fpkm.png b/GRO_seq_TFSEE/box_plot_cluster_4_genes_fpkm.png
new file mode 100644
index 0000000000000000000000000000000000000000..55233bb88f0239cf0b41fcc84a34aac7a9ae49fb
Binary files /dev/null and b/GRO_seq_TFSEE/box_plot_cluster_4_genes_fpkm.png differ
diff --git a/GRO_seq_TFSEE/closest_genes.py b/GRO_seq_TFSEE/closest_genes.py
index 320acf93953a44243610b00994d91eda2c2c9751..4bcce561b229212b5b1b53a550bb785de3ead7c0 100644
--- a/GRO_seq_TFSEE/closest_genes.py
+++ b/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'])
diff --git a/GRO_seq_TFSEE/closest_genes.sh b/GRO_seq_TFSEE/closest_genes.sh
index 0db00295bbcf31923ad46d2fa5348e24343cb0af..f39a2c70e6c2bb19046e52cd23806a5e63cf7e10 100644
--- a/GRO_seq_TFSEE/closest_genes.sh
+++ b/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
diff --git a/GRO_seq_TFSEE/cluster1_enriched_tfs.png b/GRO_seq_TFSEE/cluster1_enriched_tfs.png
new file mode 100644
index 0000000000000000000000000000000000000000..159d9447513158c1e0e498527d677bf6e92b73c3
Binary files /dev/null and b/GRO_seq_TFSEE/cluster1_enriched_tfs.png differ
diff --git a/GRO_seq_TFSEE/cluster2_enriched_tfs.png b/GRO_seq_TFSEE/cluster2_enriched_tfs.png
new file mode 100644
index 0000000000000000000000000000000000000000..3ca68a132a738e0c25171c36a7ec411f18a76806
Binary files /dev/null and b/GRO_seq_TFSEE/cluster2_enriched_tfs.png differ
diff --git a/GRO_seq_TFSEE/cluster_1_enhancers.csv b/GRO_seq_TFSEE/cluster_1_enhancers.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5328c1a485d60b9db7d822bfdda89f8ecd3c72ef
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_1_enhancers.csv
@@ -0,0 +1,1806 @@
+,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10
+SSP_1000402,2.77974886067,6.98099752094,0.625299743749,1.30300634531,1.11953876127
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+SSP_10022,1.95257178795,2.29881295234,0.0014083327562,0.0116006924225,0.00501972791202
+SSP_10023,1.95540321027,2.29791622818,0.0014083327562,0.0116006924225,0.00501972791202
+SSP_10024,1.95115607679,2.29983777994,0.0014083327562,0.0116006924225,0.00501972791202
+SSP_10025,1.95115607679,2.30114443513,0.0014083327562,0.0116006924225,0.00501972791202
+SSP_10026,1.95115607679,2.29791622818,0.0014083327562,0.0116006924225,0.00501972791202
+SSP_10027,1.95152539275,2.29908586838,0.0036739115379,0.0142666900599,0.00501972791202
+SSP_1002936,3.15774374012,5.13374576845,0.0521083119793,0.0153294864154,2.87881395755
+SSP_10038,48.094539493,56.6672273525,0.0347388746528,0.204393152205,0.123819955163
+SSP_10039,9.01772615495,10.6294094046,0.00651353899743,0.0383237160384,0.0232162415931
+SSP_10040,9.01809547091,10.6273508378,0.00877911777914,0.0409897136759,0.0232162415931
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diff --git a/GRO_seq_TFSEE/cluster_1_enhancers_locations.bed b/GRO_seq_TFSEE/cluster_1_enhancers_locations.bed
new file mode 100644
index 0000000000000000000000000000000000000000..3692bfa704e5dde030c5c6802ebbee80b795e5ba
--- /dev/null
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diff --git a/GRO_seq_TFSEE/cluster_1_genes.txt b/GRO_seq_TFSEE/cluster_1_genes.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3ac14fc0579b2fdfc0537443c21831c1e47577d5
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_1_genes.txt
@@ -0,0 +1,822 @@
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diff --git a/GRO_seq_TFSEE/cluster_1_tfs.csv b/GRO_seq_TFSEE/cluster_1_tfs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..40c8fa62c214785c6ca6d8e6f331202657fe7683
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_1_tfs.csv
@@ -0,0 +1,65 @@
+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
+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
+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
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diff --git a/GRO_seq_TFSEE/cluster_2_enhancers.csv b/GRO_seq_TFSEE/cluster_2_enhancers.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b856d913d0089d35309cc2c92b702ba0816366d3
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_2_enhancers.csv
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diff --git a/GRO_seq_TFSEE/cluster_2_enhancers_locations.bed b/GRO_seq_TFSEE/cluster_2_enhancers_locations.bed
new file mode 100644
index 0000000000000000000000000000000000000000..db1e0b22721ec53e9d7a2fab78c8ccb4f4060855
--- /dev/null
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diff --git a/GRO_seq_TFSEE/cluster_2_genes.txt b/GRO_seq_TFSEE/cluster_2_genes.txt
new file mode 100644
index 0000000000000000000000000000000000000000..424cf30b91627ea4153fccc91d789f553a8fcf81
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_2_genes.txt
@@ -0,0 +1,593 @@
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diff --git a/GRO_seq_TFSEE/cluster_2_tfs.csv b/GRO_seq_TFSEE/cluster_2_tfs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9bf48a8fda5036afdc8f30c00abbeab3f91ee167
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_2_tfs.csv
@@ -0,0 +1,25 @@
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+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
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+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
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diff --git a/GRO_seq_TFSEE/cluster_3_enhancers_locations.bed b/GRO_seq_TFSEE/cluster_3_enhancers_locations.bed
new file mode 100644
index 0000000000000000000000000000000000000000..82c324ad6966adf236884105c7ce8190e7a0ab44
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_3_enhancers_locations.bed
@@ -0,0 +1,2465 @@
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diff --git a/GRO_seq_TFSEE/cluster_3_genes.txt b/GRO_seq_TFSEE/cluster_3_genes.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fce39f933c353069b2cb18f65b724f1cdb7dba6b
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_3_genes.txt
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diff --git a/GRO_seq_TFSEE/cluster_4_enhancers_locations.bed b/GRO_seq_TFSEE/cluster_4_enhancers_locations.bed
new file mode 100644
index 0000000000000000000000000000000000000000..2ee15a57675dc9eb8c04aec29fa441457e0cdd20
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_4_enhancers_locations.bed
@@ -0,0 +1,1371 @@
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diff --git a/GRO_seq_TFSEE/cluster_4_genes.txt b/GRO_seq_TFSEE/cluster_4_genes.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7fefca468b41cddf846c8e342f67e952a2ad642c
--- /dev/null
+++ b/GRO_seq_TFSEE/cluster_4_genes.txt
@@ -0,0 +1,661 @@
+ENSG00000002746.10
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diff --git a/GRO_seq_TFSEE/matrix_analysis.py b/GRO_seq_TFSEE/matrix_analysis.py
index 98cdc693b3bc6d8a5d2cd9d768527a0ca5f69769..b1ebb3fda2ee97e3712a2faf1782ce0ae1a1fa59 100644
--- a/GRO_seq_TFSEE/matrix_analysis.py
+++ b/GRO_seq_TFSEE/matrix_analysis.py
@@ -686,7 +686,7 @@ plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7', 'ES_D10'])
plt.savefig('box_plot_cluster_3_tfs_fpkm.png')
plt.clf()
-# Cluster tfs 1 e-3 (NS)
+# Cluster tfs 1 e-3
scipy.stats.ranksums(cluster3_tfs['ES_D0'],cluster3_tfs['ES_D2'])
scipy.stats.ranksums(cluster3_tfs['ES_D0'],cluster3_tfs['ES_D5'])
scipy.stats.ranksums(cluster3_tfs['ES_D0'],cluster3_tfs['ES_D7'])
@@ -745,6 +745,163 @@ scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D5'],cluster3_enhancers_ex
scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D7'],cluster3_enhancers_expressed['ES_D10'])
+# Look at Cluster 1 for expression of TF's
+cluster1_tfs = tf_cell_lines.loc[cell_tf_values_std_cluster_1.index.values]
+cluster1_tfs.to_csv("cluster_1_tfs.csv")
+
+box = cluster1_tfs.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_tfs_fpkm.png')
+plt.clf()
+
+# Cluster tfs 1 e-12 (NS)
+scipy.stats.ranksums(cluster1_tfs['ES_D0'],cluster1_tfs['ES_D2'])
+scipy.stats.ranksums(cluster1_tfs['ES_D0'],cluster1_tfs['ES_D5'])
+scipy.stats.ranksums(cluster1_tfs['ES_D0'],cluster1_tfs['ES_D7'])
+scipy.stats.ranksums(cluster1_tfs['ES_D0'],cluster1_tfs['ES_D10'])
+
+
+scipy.stats.ranksums(cluster1_tfs['ES_D2'],cluster1_tfs['ES_D5'])
+scipy.stats.ranksums(cluster1_tfs['ES_D2'],cluster1_tfs['ES_D7'])
+scipy.stats.ranksums(cluster1_tfs['ES_D2'],cluster1_tfs['ES_D10'])
+
+scipy.stats.ranksums(cluster1_tfs['ES_D5'],cluster1_tfs['ES_D7'])
+scipy.stats.ranksums(cluster1_tfs['ES_D5'],cluster1_tfs['ES_D10'])
+
+scipy.stats.ranksums(cluster1_tfs['ES_D7'],cluster1_tfs['ES_D10'])
+
+
+
+
+cluster1_motifs = motif_enhancers.loc[cell_tf_values_std_cluster_1.index.values]
+cluster1_enhancers = only_rpkm_values.loc[cluster1_motifs.loc[:, (cluster1_motifs >1).any(axis=0)].columns.values]
+cluster_1_expressed_enhancers = np.concatenate((rpkm_ssp[(rpkm_ssp['ES_D0'] >= 0.5) | (rpkm_ssp['ES_D2'] >= 0.5) | (rpkm_ssp['ES_D7'] >= 0.5) | (rpkm_ssp['ES_D10'] >= 0.5)].index.values,rpkm_sunp[(rpkm_sunp['ES_D0'] >= 1) | (rpkm_sunp['ES_D2'] >=1) | (rpkm_sunp['ES_D7'] >= 1) | (rpkm_sunp['ES_D10'] >=1)].index.values))
+needed_rows = [row for row in cluster1_enhancers.index if row in cluster_1_expressed_enhancers]
+cluster1_enhancers_expressed = cluster1_enhancers.loc[needed_rows]
+cluster1_enhancers_expressed.to_csv("cluster_1_enhancers.csv")
+
+box = cluster1_enhancers_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_1_enhancers_rpkm.png')
+plt.clf()
+
+# Cluster tfs 1 e-4
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D0'],cluster1_enhancers_expressed['ES_D2'])
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D0'],cluster1_enhancers_expressed['ES_D5'])
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D0'],cluster1_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D0'],cluster1_enhancers_expressed['ES_D10'])
+
+
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D2'],cluster1_enhancers_expressed['ES_D5'])
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D2'],cluster1_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D2'],cluster1_enhancers_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D5'],cluster1_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D5'],cluster1_enhancers_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster1_enhancers_expressed['ES_D7'],cluster1_enhancers_expressed['ES_D10'])
+
+# Look at Cluster 2 for expression of TF's
+cluster2_tfs = tf_cell_lines.loc[cell_tf_values_std_cluster_2.index.values]
+cluster2_tfs.to_csv("cluster_2_tfs.csv")
+
+box = cluster2_tfs.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_2_tfs_fpkm.png')
+plt.clf()
+
+# Cluster tfs 1
+scipy.stats.ranksums(cluster2_tfs['ES_D0'],cluster2_tfs['ES_D2'])
+scipy.stats.ranksums(cluster2_tfs['ES_D0'],cluster2_tfs['ES_D5'])
+scipy.stats.ranksums(cluster2_tfs['ES_D0'],cluster2_tfs['ES_D7'])
+scipy.stats.ranksums(cluster2_tfs['ES_D0'],cluster2_tfs['ES_D10'])
+
+
+scipy.stats.ranksums(cluster2_tfs['ES_D2'],cluster2_tfs['ES_D5'])
+scipy.stats.ranksums(cluster2_tfs['ES_D2'],cluster2_tfs['ES_D7'])
+scipy.stats.ranksums(cluster2_tfs['ES_D2'],cluster2_tfs['ES_D10'])
+
+scipy.stats.ranksums(cluster2_tfs['ES_D5'],cluster2_tfs['ES_D7'])
+scipy.stats.ranksums(cluster2_tfs['ES_D5'],cluster2_tfs['ES_D10'])
+
+scipy.stats.ranksums(cluster2_tfs['ES_D7'],cluster2_tfs['ES_D10'])
+
+
+
+
+cluster2_motifs = motif_enhancers.loc[cell_tf_values_std_cluster_2.index.values]
+cluster2_enhancers = only_rpkm_values.loc[cluster2_motifs.loc[:, (cluster2_motifs >1).any(axis=0)].columns.values]
+cluster_2_expressed_enhancers = np.concatenate((rpkm_ssp[(rpkm_ssp['ES_D5'] >= 0.5)].index.values,rpkm_sunp[(rpkm_sunp['ES_D5'] >= 1)].index.values))
+needed_rows = [row for row in cluster2_enhancers.index if row in cluster_2_expressed_enhancers]
+cluster2_enhancers_expressed = cluster2_enhancers.loc[needed_rows]
+cluster2_enhancers_expressed.to_csv("cluster_2_enhancers.csv")
+
+box = cluster2_enhancers_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((-5,80))
+plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7', 'ES_D10'])
+plt.savefig('box_plot_cluster_2_enhancers_rpkm.png')
+plt.clf()
+
+# Cluster tfs 1 e-4
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D0'],cluster2_enhancers_expressed['ES_D2'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D0'],cluster2_enhancers_expressed['ES_D5'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D0'],cluster2_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D0'],cluster2_enhancers_expressed['ES_D10'])
+
+
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D2'],cluster2_enhancers_expressed['ES_D5'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D2'],cluster2_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D2'],cluster2_enhancers_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D5'],cluster2_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D5'],cluster2_enhancers_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D7'],cluster2_enhancers_expressed['ES_D10'])
+
+
## Analysis of only RNA-seq
# 1. Z-score Standardize for each cell line to see important TF's
scaler = preprocessing.StandardScaler()