Update code that was not previously saved.

GRO_seq_TFSEE/closest_genes.py

@@ -11,7 +11,7 @@ import scipy
 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_symbol.set_index(['gene_id'])
+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)
@@ -52,7 +52,7 @@ 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 1 e-4
+# 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'])
@@ -78,17 +78,17 @@ enhancers_universe_cluster_3.to_csv("cluster_3_enhancers_locations.bed", sep="\t
 
 
 # Read in nearest genes
-genes_id = pd.DataFrame.from_csv("cluster_4_genes.txt", sep="\t", header=None, index_col=None)
+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]
-cluster4_genes_expressed = fpkm_symbol.loc[needed_rows]
+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 = 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)
+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)
 
@@ -99,23 +99,23 @@ 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((-5,60))
+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_4_genes_fpkm.png')
+plt.savefig('box_plot_cluster_3_genes_fpkm.png')
 plt.clf()
 
 # Cluster tfs 1 e-4
-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(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(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(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(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(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(cluster4_genes_expressed['ES_D7'],cluster4_genes_expressed['ES_D10'])
+scipy.stats.ranksums(cluster3_genes_expressed['ES_D7'],cluster3_genes_expressed['ES_D10'])