def iterate_over_files(thresh="0.05"):
DIR = "/Users/joazofeifa/Lab/TF_predictions/assignments_refined/"
LST = list()
t = 0
for FILE in os.listdir(DIR):
if FILE.split("_")[-1]==thresh and "Puc" not in FILE:
print FILE
G = ec.load(DIR+FILE, test=False)
weighted,unweigthed, IDS, TFS,G,LABELS, colors, colors2,cc = make_adjacency(G, display= False)
LST.append((FILE, (weighted,unweigthed, IDS, TFS,G,LABELS, colors, colors2, cc) ))
if t > 1:
break
t+=1
return dict(LST)
ax.set_xticklabels([labels[x].split("_")[0][:6] for x in xs], minor=False,rotation=45, fontsize=10)
ax.set_yticklabels([labels[x].split("_")[0][:6] for x in ys], minor=False,rotation=45, fontsize=10)
def compares_all(G,ranks, TSS_T):
F = plt.figure(figsize=(15,10))
ax1 = F.add_axes([0.05, 0.15, 0.35, 0.8])
ax1.set_title("Bidirectionals not in Promoters (eRNAs)\nPaused Probability")
ax2 = F.add_axes([0.5, 0.15, 0.35, 0.8])
make_counts(G,ranks,ax1,FILTER1=None, FILTER2=None)
ax2.set_title("Bidirectionals in Promoters (genes)\Paused Probability")
make_counts(G,ranks,ax2,FILTER1=None, FILTER2=None)
cmap = mpl.cm.Blues
norm = mpl.colors.Normalize(vmin=0, vmax=1)
ax3 = F.add_axes([0.9, 0.15, 0.05, 0.8])
cb1 = mpl.colorbar.ColorbarBase(ax3, cmap=cmap,
norm=norm,
orientation='vertical')
plt.show()
if __name__ == "__main__":
TSS_T = at.get_TSS_tree("/Users/joazofeifa/Lab/genome_files/TSS.bed")
TF_RANKS = "/Users/joazofeifa/Lab/EMG/TF_predictions/TF_Ranks.csv"
ranks = load_ranks(TF_RANKS)
eRNA_motif = "/Users/joazofeifa/Lab/TF_predictions/assignments_refined/Allen2014_DMSO2_3-1_0.05"
B = ec.load(eRNA_motif,test=True)
compares_all(B, ranks, TSS_T)
# # print hg.expectation()
# # print "------------------------------"
# print "testing eRNA motif1 only"
# hg = HG([motif1_all, W-motif1_all],eRNA)
# print hg.pmf((motif_1_only,eRNA-motif_1_only))
# print hg.expectation()
# print "------------------------------"
# hg = hypergeom(W, motif1_all,eRNA)
# print hg.pmf((motif_1_only,motif_1_only))
# print hg.mean()
if LOAD:
IN = "/Users/joazofeifa/Lab/TF_predictions/DNase_motif_eRNA_counts.tsv"
A, M, toIDS, fromIDS=load_counts(IN)
compute_pvalues(A, M, toIDS, fromIDS)
if WRITE:
OUT = "/Users/joazofeifa/Lab/TF_predictions/DNase_motif_eRNA_counts.tsv"
eRNA_motif = "/Users/joazofeifa/Lab/TF_predictions/assignments_refined/Allen2014_DMSO2_3-1_0.05"
DNase_motif = "/Users/joazofeifa/Lab/TF_predictions/DNAse_motif_overlap.bed"
B = ec.load(eRNA_motif,test=False)
TFS = dict([(tf.split("_")[0],1) for chrom in B for b in B[chrom] for TF in b.TFS for tf in TF.split(",") ])
D = load_DNAse(DNase_motif, FILTER=TFS, test=False)
label_DNase(B,D, attr="eRNA")
label_DNase(D,B, attr="DNAse")
write_out(D, B, OUT, TFS)
pass
