From 8c613d61abe01c2bb7d558bc8740e8855ac70877 Mon Sep 17 00:00:00 2001
From: Venkat Malladi <Venkat.Malladi@utsouthwestern.edu>
Date: Tue, 6 Jun 2017 11:12:09 -0500
Subject: [PATCH] Analysis for the Histone centered matrix.

---
 Histone_TFSEE/box_plot_cluster_1.png          |  Bin 28395 -> 11282 bytes
 Histone_TFSEE/box_plot_cluster_1_tfs_fpkm.png |  Bin 31327 -> 0 bytes
 Histone_TFSEE/box_plot_cluster_2.png          |  Bin 28819 -> 10789 bytes
 .../box_plot_cluster_2_enhancers_rpkm.png     |  Bin 0 -> 11003 bytes
 Histone_TFSEE/box_plot_cluster_2_tfs_fpkm.png |  Bin 0 -> 12066 bytes
 Histone_TFSEE/box_plot_cluster_3.png          |  Bin 27894 -> 10655 bytes
 .../box_plot_cluster_3_enhancers_rpkm.png     |  Bin 0 -> 11159 bytes
 Histone_TFSEE/box_plot_cluster_3_tfs_fpkm.png |  Bin 29283 -> 11823 bytes
 Histone_TFSEE/cluster_2_enhancers.csv         | 1376 ++++++++++++++
 .../{cluster_1_tfs.csv => cluster_2_tfs.csv}  |    5 -
 Histone_TFSEE/cluster_3_enhancers.csv         | 1641 +++++++++++++++++
 Histone_TFSEE/cluster_3_tfs.csv               |    7 +
 Histone_TFSEE/clustering_tfs.csv              |  302 +--
 Histone_TFSEE/k_means_clustering.png          |  Bin 146730 -> 78562 bytes
 Histone_TFSEE/matrix_analysis.py              |  263 +--
 15 files changed, 3253 insertions(+), 341 deletions(-)
 delete mode 100644 Histone_TFSEE/box_plot_cluster_1_tfs_fpkm.png
 create mode 100644 Histone_TFSEE/box_plot_cluster_2_enhancers_rpkm.png
 create mode 100644 Histone_TFSEE/box_plot_cluster_2_tfs_fpkm.png
 create mode 100644 Histone_TFSEE/box_plot_cluster_3_enhancers_rpkm.png
 create mode 100644 Histone_TFSEE/cluster_2_enhancers.csv
 rename Histone_TFSEE/{cluster_1_tfs.csv => cluster_2_tfs.csv} (79%)
 create mode 100644 Histone_TFSEE/cluster_3_enhancers.csv

diff --git a/Histone_TFSEE/box_plot_cluster_1.png b/Histone_TFSEE/box_plot_cluster_1.png
index 844b80ae04ca40a707631345f695250fcd7a9381..a703a9d4bd2d0e1b2621b99fffab5c6716e5dd5d 100644
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diff --git a/Histone_TFSEE/box_plot_cluster_3.png b/Histone_TFSEE/box_plot_cluster_3.png
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diff --git a/Histone_TFSEE/cluster_2_enhancers.csv b/Histone_TFSEE/cluster_2_enhancers.csv
new file mode 100644
index 0000000..8ad0172
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+++ b/Histone_TFSEE/cluster_2_enhancers.csv
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diff --git a/Histone_TFSEE/cluster_1_tfs.csv b/Histone_TFSEE/cluster_2_tfs.csv
similarity index 79%
rename from Histone_TFSEE/cluster_1_tfs.csv
rename to Histone_TFSEE/cluster_2_tfs.csv
index bfa7397..1d530d8 100644
--- a/Histone_TFSEE/cluster_1_tfs.csv
+++ b/Histone_TFSEE/cluster_2_tfs.csv
@@ -1,8 +1,6 @@
 symbol,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10
 E2F6,12.765,12.05,13.85,15.7,13.92
-GATA4,0.08,55.85,60.32,168.535,57.74
 GLI2,17.815,4.495,1.7,0.37,1.075
-IRF1,2.89,2.39,0.305,3.11,2.56
 KLF1,0.32,0.805,1.18,0.59,0.45
 KLF12,3.72,4.185,8.88,8.085,9.125
 KLF16,17.695,18.915,10.375,6.245,6.515
@@ -11,7 +9,6 @@ MTF1,2.85,3.065,4.065,5.195,4.16
 NR2C2,11.36,11.285,12.59,10.985,11.54
 NRF1,12.775,12.645,15.66,15.51,16.51
 PLAG1,3.42,3.9,4.71,6.5,4.775
-RARA,6.195,10.955,11.025,33.47,26.135
 RREB1,10.315,17.075,10.515,12.59,14.675
 RXRA,13.38,31.955,56.265,27.41,34.875
 SMAD2,33.815,63.24,29.905,33.295,33.98
@@ -19,7 +16,5 @@ SMAD4,54.035,47.595,59.69,60.76,66.16
 SP2,6.75,6.92,10.8,8.075,12.81
 SP3,16.795,14.03,30.47,22.055,22.605
 SP8,1.075,2.045,0.515,0.295,0.05
-STAT2,14.685,26.38,24.935,48.685,37.59
 TCF3,136.215,124.305,134.515,121.675,163.045
 ZIC3,27.06,95.46,4.98,0.695,0.085
-ZNF740,7.14,14.92,27.79,28.23,20.005
diff --git a/Histone_TFSEE/cluster_3_enhancers.csv b/Histone_TFSEE/cluster_3_enhancers.csv
new file mode 100644
index 0000000..6d2500c
--- /dev/null
+++ b/Histone_TFSEE/cluster_3_enhancers.csv
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diff --git a/Histone_TFSEE/cluster_3_tfs.csv b/Histone_TFSEE/cluster_3_tfs.csv
index a6cac7f..ab3d3c1 100644
--- a/Histone_TFSEE/cluster_3_tfs.csv
+++ b/Histone_TFSEE/cluster_3_tfs.csv
@@ -1,18 +1,25 @@
 symbol,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10
+AHR,1.735,0.75,2.285,6.07,2.955
 EGR1,2.3,1.69,1.2,7.685,6.465
 EHF,0.0,0.0,0.035,16.115,6.33
 ELF1,6.8,11.92,9.41,29.9,29.37
+ELF3,2.21,3.685,3.88,52.51,13.54
 ELF4,0.89,0.73,2.49,0.68,3.03
 FOXP1,9.56,5.68,48.0,80.06,80.08
+GATA4,0.08,55.85,60.32,168.535,57.74
 GLIS3,0.075,0.315,0.16,1.19,10.685
 ID4,1.555,1.32,84.935,8.975,47.94
 INSM1,0.1,0.02,0.6,6.33,30.37
+IRF1,2.89,2.39,0.305,3.11,2.56
 KLF4,2.385,0.28,0.21,2.195,0.88
 KLF5,2.025,21.66,6.495,23.005,47.25
 NHLH1,0.155,0.16,0.235,0.675,0.67
 NR2F1,0.0,0.0,0.26,82.77,53.655
 PPARG,0.07,0.215,1.295,1.865,2.08
+RARA,6.195,10.955,11.025,33.47,26.135
 SMAD3,18.595,12.95,10.205,20.925,35.53
 SOX3,3.33,0.01,1.255,4.555,0.885
+STAT2,14.685,26.38,24.935,48.685,37.59
 TFAP2A,0.01,0.19,2.715,0.39,0.52
 ZFX,5.035,5.13,5.505,9.14,11.425
+ZNF740,7.14,14.92,27.79,28.23,20.005
diff --git a/Histone_TFSEE/clustering_tfs.csv b/Histone_TFSEE/clustering_tfs.csv
index 28585e8..ce0e248 100644
--- a/Histone_TFSEE/clustering_tfs.csv
+++ b/Histone_TFSEE/clustering_tfs.csv
@@ -1,169 +1,169 @@
 ,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10,cluster
-AHR,0.119485788748,-0.617000119631,0.0919738733256,1.75810131217,0.132926965972,1
-ARNT,0.0664606139072,0.296568115173,-0.418291573839,-0.575948422012,-0.250500715249,1
-ASCL2,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-BACH1,0.117256217236,-0.0885845039036,-0.00579228033837,-0.00988937752512,-0.611176690012,1
-CEBPA,-0.0768409437018,-0.617000119631,-0.1622005782,-0.062115163307,-0.371969903489,1
-CREB1,-0.0996116072585,-0.404077544825,-0.471264974621,-0.575948422012,-0.59354638216,1
-CREB3,-0.348473913473,-0.470648284666,-0.514857150651,-0.372963562635,-0.378242434521,1
-CREB3L1,-0.326840833995,-0.617000119631,-0.333165796186,-0.10972902678,-0.488063479862,1
-DDIT3,-0.403290568058,-0.165108054814,-0.514857150651,-0.360437158981,-0.532314121331,1
-DMBX1,-0.467935560061,-0.617000119631,-0.0805922255734,-0.575948422012,-0.611176690012,1
-E2F4,0.0939936591441,0.502049030447,-0.0506767946301,-0.575948422012,-0.380661824854,1
-E2F6,2.44465013541,1.91332081677,0.308918307377,-0.164581981956,-0.611176690012,0
-EBF1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
+AHR,0.119485788748,-0.617000119631,0.0919738733256,1.75810131217,0.132926965972,2
+ARNT,0.0664606139072,0.296568115173,-0.418291573839,-0.575948422012,-0.250500715249,0
+ASCL2,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+BACH1,0.117256217236,-0.0885845039036,-0.00579228033837,-0.00988937752512,-0.611176690012,0
+CEBPA,-0.0768409437018,-0.617000119631,-0.1622005782,-0.062115163307,-0.371969903489,0
+CREB1,-0.0996116072585,-0.404077544825,-0.471264974621,-0.575948422012,-0.59354638216,0
+CREB3,-0.348473913473,-0.470648284666,-0.514857150651,-0.372963562635,-0.378242434521,0
+CREB3L1,-0.326840833995,-0.617000119631,-0.333165796186,-0.10972902678,-0.488063479862,0
+DDIT3,-0.403290568058,-0.165108054814,-0.514857150651,-0.360437158981,-0.532314121331,0
+DMBX1,-0.467935560061,-0.617000119631,-0.0805922255734,-0.575948422012,-0.611176690012,0
+E2F4,0.0939936591441,0.502049030447,-0.0506767946301,-0.575948422012,-0.380661824854,0
+E2F6,2.44465013541,1.91332081677,0.308918307377,-0.164581981956,-0.611176690012,1
+EBF1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
 EGR1,1.30496770826,0.480386773785,-0.514857150651,1.46883839186,0.736078066801,2
-EGR2,-0.467935560061,-0.617000119631,-0.514857150651,0.676963139439,-0.611176690012,1
-EGR3,-0.467935560061,-0.617000119631,-0.514857150651,0.367419996105,-0.0564313268642,1
-EGR4,-0.467935560061,-0.617000119631,-0.514857150651,1.51602291307,-0.611176690012,1
+EGR2,-0.467935560061,-0.617000119631,-0.514857150651,0.676963139439,-0.611176690012,0
+EGR3,-0.467935560061,-0.617000119631,-0.514857150651,0.367419996105,-0.0564313268642,0
+EGR4,-0.467935560061,-0.617000119631,-0.514857150651,1.51602291307,-0.611176690012,0
 EHF,-0.467935560061,-0.617000119631,-0.514857150651,1.60366494723,1.72229013884,2
 ELF1,-0.30713010762,-0.0287062962388,-0.514857150651,1.3475185769,1.87185887946,2
-ELF3,-0.435144845621,-0.420158495208,-0.514857150651,1.60366494723,0.557823067539,1
+ELF3,-0.435144845621,-0.420158495208,-0.514857150651,1.60366494723,0.557823067539,2
 ELF4,0.0322812650202,-0.0861032359366,1.27632596776,-0.575948422012,1.76570929846,2
-EOMES,-0.467935560061,-0.143343279804,-0.514857150651,-0.575948422012,-0.611176690012,1
-ESR1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-ESR2,-0.467935560061,-0.617000119631,-0.514857150651,-0.0170232532381,0.913617707926,1
-FIGLA,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-FOXC2,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-FOXD3,0.162765455812,0.128402435141,-0.514857150651,-0.575948422012,-0.611176690012,1
-FOXI1,-0.467935560061,-0.259200885884,-0.514857150651,-0.575948422012,-0.611176690012,1
-FOXJ3,0.0179570600309,0.457291473328,0.444748190656,1.03565494111,-0.611176690012,1
-FOXK1,-0.467935560061,0.685785620145,0.30153719891,-0.379251346771,-0.170925729392,1
-FOXO4,-0.378125797316,-0.519607359121,-0.416031175192,-0.575948422012,-0.561841144403,1
-FOXO6,-0.378125797316,-0.486026603364,-0.514857150651,-0.545492121433,-0.603976980356,1
+EOMES,-0.467935560061,-0.143343279804,-0.514857150651,-0.575948422012,-0.611176690012,0
+ESR1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+ESR2,-0.467935560061,-0.617000119631,-0.514857150651,-0.0170232532381,0.913617707926,0
+FIGLA,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+FOXC2,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+FOXD3,0.162765455812,0.128402435141,-0.514857150651,-0.575948422012,-0.611176690012,0
+FOXI1,-0.467935560061,-0.259200885884,-0.514857150651,-0.575948422012,-0.611176690012,0
+FOXJ3,0.0179570600309,0.457291473328,0.444748190656,1.03565494111,-0.611176690012,0
+FOXK1,-0.467935560061,0.685785620145,0.30153719891,-0.379251346771,-0.170925729392,0
+FOXO4,-0.378125797316,-0.519607359121,-0.416031175192,-0.575948422012,-0.561841144403,0
+FOXO6,-0.378125797316,-0.486026603364,-0.514857150651,-0.545492121433,-0.603976980356,0
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diff --git a/Histone_TFSEE/k_means_clustering.png b/Histone_TFSEE/k_means_clustering.png
index 56eea6f9d5bf8a2f30119e5f809d203057e4a311..c5ce38717eb9792b21a5b7ea92f5e342663e9238 100644
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diff --git a/Histone_TFSEE/matrix_analysis.py b/Histone_TFSEE/matrix_analysis.py
index 35b27d3..843454d 100644
--- a/Histone_TFSEE/matrix_analysis.py
+++ b/Histone_TFSEE/matrix_analysis.py
@@ -364,10 +364,10 @@ plt.style.use('classic')
 
 
 #TNSE
-tnse = TSNE(n_components=2,verbose=2,learning_rate=100,perplexity=50)
-tnse_fit = tnse.fit_transform(cell_tf_values)
+tnse = TSNE(n_components=2,verbose=2,learning_rate=200,perplexity=50)
+tnse_fit = tnse.fit_transform(cell_tf_values_std)
 
-k_model = KMeans(n_clusters=3).fit(cell_tf_values)
+k_model = KMeans(n_clusters=3,random_state=1).fit(cell_tf_values_std)
 labels = k_model.labels_
 centroids = k_model.cluster_centers_
 
@@ -375,7 +375,7 @@ centroids = k_model.cluster_centers_
 vis_x = tnse_fit[:, 0]
 vis_y = tnse_fit[:, 1]
 
-plt.scatter(vis_x, vis_y, c=labels, cmap=plt.cm.get_cmap("jet", 3),facecolor='white',marker='o',s=100,linewidth=2)
+plt.scatter(vis_x, vis_y, c=labels, cmap=plt.cm.get_cmap("jet", 3),facecolor='white',marker='o',s=120,linewidth=2)
 #plt.colorbar(ticks=None)
 plt.tick_params(axis='y', direction='out')
 plt.tick_params(axis='x', direction='out')
@@ -385,7 +385,7 @@ plt.clf()
 
 
 
-k_model = KMeans(n_clusters=3,random_state=1).fit(cell_tf_values_std)
+k_model = KMeans(n_clusters=3,random_state=0).fit(cell_tf_values_std)
 labels = k_model.labels_
 centroids = k_model.cluster_centers_
 
@@ -415,7 +415,7 @@ cell_tf_values_std_cluster_3 = cell_tf_values_std_cluster.loc[cell_tf_values_std
 
 # col_colors
 colors = ["#FFD66F","#2E6A44","#862743", "#4FA6C7", "#3398CC"]
-medianprops = dict(linestyle='-', linewidth=4, color='black')
+medianprops = dict(linestyle='-', linewidth=2, color='black')
 
 box = cell_tf_values_std_cluster_1.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)
@@ -428,7 +428,7 @@ 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((-1.5,5.5))
+plt.ylim((-1,1))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
 plt.savefig('box_plot_cluster_1.png')
 plt.clf()
@@ -445,7 +445,7 @@ 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((-1,1))
+plt.ylim((-1.5,5.5))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
 plt.savefig('box_plot_cluster_2.png')
 plt.clf()
@@ -519,13 +519,13 @@ scipy.stats.ranksums(cell_tf_values_std_cluster_3['ES_D5'],cell_tf_values_std_cl
 scipy.stats.ranksums(cell_tf_values_std_cluster_3['ES_D7'],cell_tf_values_std_cluster_3['ES_D10'])
 
 
-# Look at Cluster 4 for expression of TF's
-cluster4_tfs = tf_cell_lines.loc[cell_tf_values_std_cluster_4.index.values]
-cluster4_tfs.to_csv("cluster_4_tfs.csv")
+# 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 = cluster4_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 = 3)
-plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 3)
+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)
@@ -534,37 +534,40 @@ 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((-5,65))
+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_4_tfs_fpkm.png')
+plt.savefig('box_plot_cluster_2_tfs_fpkm.png')
 plt.clf()
 
 # Cluster tfs 1 e-3
-scipy.stats.ranksums(cluster4_tfs['ES_D0'],cluster4_tfs['ES_D2'])
-scipy.stats.ranksums(cluster4_tfs['ES_D0'],cluster4_tfs['ES_D5'])
-scipy.stats.ranksums(cluster4_tfs['ES_D0'],cluster4_tfs['ES_D7'])
-scipy.stats.ranksums(cluster4_tfs['ES_D0'],cluster4_tfs['ES_D10'])
-
-
-scipy.stats.ranksums(cluster4_tfs['ES_D2'],cluster4_tfs['ES_D5'])
-scipy.stats.ranksums(cluster4_tfs['ES_D2'],cluster4_tfs['ES_D7'])
-scipy.stats.ranksums(cluster4_tfs['ES_D2'],cluster4_tfs['ES_D10'])
+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(cluster4_tfs['ES_D5'],cluster4_tfs['ES_D7'])
-scipy.stats.ranksums(cluster4_tfs['ES_D5'],cluster4_tfs['ES_D10'])
 
-scipy.stats.ranksums(cluster4_tfs['ES_D7'],cluster4_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_H3K27ac_values.loc[cluster2_motifs.loc[:, (cluster2_motifs >1).any(axis=0)].columns.values]
+cluster_2_DO = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D0'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D0'] >= 1)].index.values))
+cluster_2_D2 = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D2'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D2'] >= 1)].index.values))
+cluster_2_D5 = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D5'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D5'] >= 1)].index.values))
+cluster_2_expressed_enhancers = list(set(cluster_2_DO) | set(cluster_2_D2) | set(cluster_2_D5))
+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")
 
-cluster4_motifs = motif_enhancers.loc[cell_tf_values_std_cluster_4.index.values]
-cluster4_enhancers = only_rpkm_values.loc[cluster4_motifs.loc[:, (cluster4_motifs != 0).all(axis=0)].columns.values]
-cluster4_enhancers.to_csv("cluster_4_enhancers.csv")
-
-box = cluster4_enhancers.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 = 3)
-plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 3)
+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)
@@ -573,26 +576,27 @@ 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((-5,105))
+plt.ylim((-1,6))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
-plt.savefig('box_plot_cluster_4_enhancers_rpkm.png')
+plt.savefig('box_plot_cluster_2_enhancers_rpkm.png')
 plt.clf()
 
 # Cluster tfs 1 e-4
-scipy.stats.ranksums(cluster4_enhancers['ES_D0'],cluster4_enhancers['ES_D2'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D0'],cluster4_enhancers['ES_D5'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D0'],cluster4_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D0'],cluster4_enhancers['ES_D10'])
+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(cluster4_enhancers['ES_D2'],cluster4_enhancers['ES_D5'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D2'],cluster4_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D2'],cluster4_enhancers['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(cluster4_enhancers['ES_D5'],cluster4_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D5'],cluster4_enhancers['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'])
 
-scipy.stats.ranksums(cluster4_enhancers['ES_D7'],cluster4_enhancers['ES_D10'])
 
 
 # Look at Cluster 3 for expression of TF's
@@ -600,8 +604,8 @@ cluster3_tfs = tf_cell_lines.loc[cell_tf_values_std_cluster_3.index.values]
 cluster3_tfs.to_csv("cluster_3_tfs.csv")
 
 box = cluster3_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 = 3)
-plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 3)
+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)
@@ -610,12 +614,12 @@ 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.ylim((-5,90))
 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-2
 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'])
@@ -635,12 +639,18 @@ scipy.stats.ranksums(cluster3_tfs['ES_D7'],cluster3_tfs['ES_D10'])
 
 
 cluster3_motifs = motif_enhancers.loc[cell_tf_values_std_cluster_3.index.values]
-cluster3_enhancers = only_rpkm_values.loc[cluster3_motifs.loc[:, (cluster3_motifs != 0).all(axis=0)].columns.values]
-cluster3_enhancers.to_csv("cluster_3_enhancers.csv")
+cluster3_enhancers = only_H3K27ac_values.loc[cluster3_motifs.loc[:, (cluster3_motifs >1).any(axis=0)].columns.values]
+cluster_3_D7 = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D7'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D7'] >= 1)].index.values))
+cluster_3_D10 = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D10'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D10'] >= 1)].index.values))
+cluster_3_expressed_enhancers = list(set(cluster_3_D7) | set(cluster_3_D10) )
+needed_rows = [row for row in cluster3_enhancers.index if row in cluster_3_expressed_enhancers]
+cluster3_enhancers_expressed = cluster3_enhancers.loc[needed_rows]
+cluster3_enhancers_expressed.to_csv("cluster_3_enhancers.csv")
 
-box = cluster3_enhancers.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 = 3)
-plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 3)
+
+box = cluster3_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)
@@ -649,140 +659,23 @@ 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((-5,65))
+plt.ylim((-1,6))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
 plt.savefig('box_plot_cluster_3_enhancers_rpkm.png')
 plt.clf()
 
-# Cluster tfs 1 e-12
-scipy.stats.ranksums(cluster3_enhancers['ES_D0'],cluster3_enhancers['ES_D2'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D0'],cluster3_enhancers['ES_D5'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D0'],cluster3_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D0'],cluster3_enhancers['ES_D10'])
-
-
-scipy.stats.ranksums(cluster3_enhancers['ES_D2'],cluster3_enhancers['ES_D5'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D2'],cluster3_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D2'],cluster3_enhancers['ES_D10'])
-
-scipy.stats.ranksums(cluster3_enhancers['ES_D5'],cluster3_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D5'],cluster3_enhancers['ES_D10'])
-
-scipy.stats.ranksums(cluster3_enhancers['ES_D7'],cluster3_enhancers['ES_D10'])
-
-
-## Analysis of only RNA-seq
-# 1. Z-score Standardize for each cell line to see important TF's
-scaler = preprocessing.StandardScaler()
-norm = scaler.fit_transform(tf_scaled_ordered.values)
-tf_scaled_std = pd.DataFrame(data=norm, columns=list(tf_scaled_ordered.columns.values), index = tf_scaled_ordered.index )
-
-# Seaborn settings
-sns.axes_style({'image.cmap': u'Blacks','lines.linewidth': 100.0})
-
-# Cluster Heatmap
-sns.set_context("paper")
-hmap = sns.clustermap(tf_scaled_std,xticklabels=True, yticklabels=True, cmap="RdBu_r", method = "complete", metric = "euclidean", figsize=(20, 20), col_colors=sns.color_palette(cell_tf_values_colors))
-plt.setp(hmap.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
-plt.savefig('final_full_cluster_heatmap_rna-seq.png')
-
-labels = [item.get_text() for item in hmap.ax_heatmap.yaxis.get_majorticklabels()]
-labels.reverse()
-with  open("final_full_cluster_heatmap_rna-seq.csv", 'wb') as csv_file:
-    wr = csv.writer(csv_file,dialect='excel',quoting=csv.QUOTE_ALL)
-    for tf in labels:
-        wr.writerow([tf,])
-
-# 2. Reorder based on clustering
-reorder_clustering = tf_scaled_std.columns.values[hmap.dendrogram_col.reordered_ind]
-tf_scaled_std_ordered = tf_scaled_std[reorder_clustering]
-reindex_cluserting = tf_scaled_std.index.values[hmap.dendrogram_row.reordered_ind]
-tf_scaled_std_ordered = tf_scaled_std_ordered.reindex(reindex_cluserting)
-tf_scaled_std_ordered.to_csv("final_full_cluster_z_score-rnaseq.csv", encoding='utf-8')
-
-
-## Analysis of only GRO-seq data
-# 1. Make Score Matrix
-## Enhancers RPKM x Motif Enhancers
-motif_cell_line = motif_enhancers_scaled.dot(rpkm_robust_filtered)
-needed_rows = [row for row in motif_cell_line.index if row in list(tf_scaled.index)]
-motif_cell_line_filtered_tfs = motif_cell_line.loc[needed_rows]
-motif_cell_line_filtered_tfs.columns =  ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10']
-motif_cell_line_filtered_tfs = motif_cell_line_filtered_tfs[reorder]
-
-# 2. Z-score Standardize for each cell line to see important TF's
-scaler = preprocessing.StandardScaler()
-norm = scaler.fit_transform(motif_cell_line_filtered_tfs.values)
-motif_cell_line_filtered_tfs_std = pd.DataFrame(data=norm, columns=list(motif_cell_line_filtered_tfs.columns.values), index = motif_cell_line_filtered_tfs.index )
-
-# Seaborn settings
-sns.axes_style({'image.cmap': u'Blacks','lines.linewidth': 100.0})
-
-# Cluster Heatmap
-sns.set_context("paper")
-hmap = sns.clustermap(motif_cell_line_filtered_tfs_std,xticklabels=True, yticklabels=True, cmap="RdBu_r", method = "complete", metric = "euclidean", figsize=(20, 20), col_colors=sns.color_palette(cell_tf_values_colors))
-plt.setp(hmap.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
-plt.savefig('final_full_cluster_heatmap_gro-seq.png')
-
-labels = [item.get_text() for item in hmap.ax_heatmap.yaxis.get_majorticklabels()]
-labels.reverse()
-with  open("final_full_cluster_heatmap_gro-seq.csv", 'wb') as csv_file:
-    wr = csv.writer(csv_file,dialect='excel',quoting=csv.QUOTE_ALL)
-    for tf in labels:
-        wr.writerow([tf,])
-
-# 3. Reorder based on clustering
-reorder_clustering = motif_cell_line_filtered_tfs_std.columns.values[hmap.dendrogram_col.reordered_ind]
-motif_cell_line_filtered_tfs_std_ordered = motif_cell_line_filtered_tfs_std[reorder_clustering]
-reindex_cluserting = motif_cell_line_filtered_tfs_std.index.values[hmap.dendrogram_row.reordered_ind]
-motif_cell_line_filtered_tfs_std_ordered = motif_cell_line_filtered_tfs_std_ordered.reindex(reindex_cluserting)
-motif_cell_line_filtered_tfs_std_ordered.to_csv("final_full_cluster_z_score-groseq.csv", encoding='utf-8')
-
-## Analysis of only GRO-seq data + RNA-seq
-
-# 1. Make Score Matrix
-## Enhancers RPKM x Motif Enhancers
-motif_cell_line = motif_enhancers_scaled.dot(rpkm_robust_filtered)
-needed_rows = [row for row in motif_cell_line.index if row in list(tf_scaled.index)]
-motif_cell_line_filtered_tfs = motif_cell_line.loc[needed_rows]
-motif_cell_line_filtered_tfs.columns = ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10']
-motif_cell_line_filtered_tfs = motif_cell_line_filtered_tfs[reorder]
-
-# reindex
-tf_scaled_ordered = tf_scaled.reindex(list(motif_cell_line_filtered_tfs.index))
-tf_scaled_ordered = tf_scaled_ordered[reorder]
-
-# 2. .multiply() to to Element-by-element multiplication Score Enhancers by TF
-cell_tf_values = motif_cell_line_filtered_tfs.multiply(tf_scaled_ordered)
-cell_tf_values.columns = ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10']
-cell_tf_values_colors = ["#FFD66F","#2E6A44","#862743", "#4FA6C7", "#3398CC"]
-
-# 3. Z-score Standardize for each cell line to see important TF's
-scaler = preprocessing.StandardScaler()
-norm = scaler.fit_transform(cell_tf_values.values)
-cell_tf_values_std = pd.DataFrame(data=norm, columns=list(cell_tf_values.columns.values), index = cell_tf_values.index )
-
-
-# Seaborn settings
-sns.axes_style({'image.cmap': u'Blacks','lines.linewidth': 100.0})
+# Cluster tfs 1 e-4
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D0'],cluster3_enhancers_expressed['ES_D2'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D0'],cluster3_enhancers_expressed['ES_D2'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D0'],cluster3_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D0'],cluster3_enhancers_expressed['ES_D10'])
 
-# Cluster Heatmap
-sns.set_context("paper")
-hmap = sns.clustermap(cell_tf_values_std,xticklabels=True, yticklabels=True, cmap="RdBu_r", method = "complete", metric = "euclidean", figsize=(20, 20), col_colors=sns.color_palette(cell_tf_values_colors))
-plt.setp(hmap.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
-plt.savefig('final_full_cluster_heatmap_gro_rna.png')
 
-labels = [item.get_text() for item in hmap.ax_heatmap.yaxis.get_majorticklabels()]
-labels.reverse()
-with  open("final_full_cluster_heatmap_gro_rna.csv", 'wb') as csv_file:
-    wr = csv.writer(csv_file,dialect='excel',quoting=csv.QUOTE_ALL)
-    for tf in labels:
-        wr.writerow([tf,])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D2'],cluster3_enhancers_expressed['ES_D5'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D2'],cluster3_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D2'],cluster3_enhancers_expressed['ES_D10'])
 
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D5'],cluster3_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D5'],cluster3_enhancers_expressed['ES_D10'])
 
-# 4. Reorder based on clustering
-reorder_clustering = cell_tf_values_std.columns.values[hmap.dendrogram_col.reordered_ind]
-cell_tf_values_std_ordered = cell_tf_values_std[reorder_clustering]
-reindex_cluserting = cell_tf_values_std.index.values[hmap.dendrogram_row.reordered_ind]
-cell_tf_values_std_ordered = cell_tf_values_std_ordered.reindex(reindex_cluserting)
-cell_tf_values_std_ordered.to_csv("final_full_cluster_z_score_gro_rna.csv", encoding='utf-8')
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D7'],cluster3_enhancers_expressed['ES_D10'])
-- 
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