Louis BECQUEY / biorseo

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Louis BECQUEY
2269484f 1 parent 1d43ff8f

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Showing 1 changed file with 138 additions and 131 deletions
......@@ -1167,6 +1167,10 @@ print("==> %s ARN were predicted with all methods successful." % is_all(len(x_no
test = stats.friedmanchisquare(*x_noPK_fully)
print("Friedman test without PK: H0 = 'The position parameter of all distributions is equal', p-value = ", test.pvalue)
# ==> No they are not, but none does better, no need to test one further.
test = stats.wilcoxon(x_noPK_fully[1], x_noPK_fully[2])
print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of RNA-MoIP and RawA are equal', p-value = ", test.pvalue)
test = stats.wilcoxon(x_noPK_fully[1], x_noPK_fully[3])
print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of RNA-MoIP and RawB are equal', p-value = ", test.pvalue)
# ================= Statistics (with pseudoknots) ========================
......@@ -1260,154 +1264,157 @@ test = stats.wilcoxon(x_PK_fully[0], x_PK_fully[11])
print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dD are equal', p-value = ", test.pvalue)
# ================== Print results for application cases =====================
labels = ["Biokop","Biokop","RawA","RawB","BayesPairingA","BayesPairingB","BayesPairingC","BayesPairingD","JAR3DA","JAR3DB","JAR3DC","JAR3DD","BGSUBayesPairingA","BGSUBayesPairingB","BGSUBayesPairingC","BGSUBayesPairingD"]
print("RNAsubopt",":",x_noPK[0])
print("RNA-MOIP",":",x_noPK[1])
for data, name in zip(x_PK, labels):
print(name,":",data)
labels = ["RNAsubopt","Biokop\t", "RNA MoIP\t","RawA\t","RawB\t","BayesPairingA","BayesPairingB","BayesPairingC","BayesPairingD","JAR3DA\t","JAR3DB\t","JAR3DC\t","JAR3DD\t","BGSUBPairingA","BGSUBPairingB","BGSUBPairingC","BGSUBPairingD"]
for r in RNAcontainer:
print("\n",r.header_,"\nTrue structure:\t", r.true2d)
for m, name in zip([r.rnasubopt, r.biokop, r.rnamoip,
r.biorseoRawA,
r.biorseoRawB,
r.biorseoBayesPairA,
r.biorseoBayesPairB,
r.biorseoBayesPairC,
r.biorseoBayesPairD,
r.biorseoBGSUJAR3DA,
r.biorseoBGSUJAR3DB,
r.biorseoBGSUJAR3DC,
r.biorseoBGSUJAR3DD,
r.biorseoBGSUBayesPairA,
r.biorseoBGSUBayesPairB,
r.biorseoBGSUBayesPairC,
r.biorseoBGSUBayesPairD ], labels):
print(name+":\t",m.best_pred)
# # ================= PLOTS OF RESULTS =======================================
# merge = [ x_PK_fully[0], # Biokop
# x_noPK_fully[0], # RNA subopt
# x_noPK_fully[1], # RNA MoIP
# x_noPK_fully[2], x_PK_fully[2], #biorseoRawA
# x_noPK_fully[3], x_PK_fully[3], #biorseoRawB
# x_noPK_fully[4], x_PK_fully[4], #biorseoBayesPairA
# x_noPK_fully[5], x_PK_fully[5], #biorseoBayesPairB
# x_noPK_fully[6], x_PK_fully[6], #biorseoBayesPairC
# x_noPK_fully[7], x_PK_fully[7], #biorseoBayesPairD
# x_noPK_fully[8], x_PK_fully[8], #biorseoBGSUJAR3DA
# x_noPK_fully[9], x_PK_fully[9], #biorseoBGSUJAR3DB
# x_noPK_fully[10], x_PK_fully[10], #biorseoBGSUJAR3DC
# x_noPK_fully[11], x_PK_fully[11], #biorseoBGSUJAR3DD
# x_noPK_fully[12], x_PK_fully[12], #biorseoBGSUBayesPairA
# x_noPK_fully[13], x_PK_fully[13], #biorseoBGSUBayesPairB
# x_noPK_fully[14], x_PK_fully[14], #biorseoBGSUBayesPairC
# x_noPK_fully[15], x_PK_fully[15], #biorseoBGSUBayesPairD
# ]
# colors = [ 'green', 'blue', 'goldenrod',
# 'darkturquoise', 'darkturquoise',
# 'red', 'red',
# 'firebrick', 'firebrick',
# 'limegreen', 'limegreen',
# 'olive', 'olive',
# 'forestgreen', 'forestgreen',
# 'lime', 'lime',
# 'darkcyan', 'darkcyan',
# 'royalblue', 'royalblue',
# 'navy', 'navy',
# 'limegreen', 'limegreen',
# 'olive', 'olive',
# 'forestgreen', 'forestgreen',
# 'lime', 'lime'
# ]
# labels = [ "Biokop", "RNAsubopt",
# "RNA MoIP",
# "$f_{1A}$",
# "$f_{1B}$",
# "$f_{1A}$",
# "$f_{1B}$",
# "$f_{1C}$",
# "$f_{1D}$",
# "$f_{1A}$",
# "$f_{1B}$",
# "$f_{1C}$",
# "$f_{1D}$",
# "$f_{1A}$",
# "$f_{1B}$",
# "$f_{1C}$",
# "$f_{1D}$"
# ]
# ax = plt.subplot(211)
# ax.tick_params(labelsize=12)
# for y in [ i/10 for i in range(11) ]:
# plt.axhline(y=y, color="grey", linestyle="--", linewidth=1)
# colors = [ 'blue','goldenrod',
# 'red', 'firebrick','limegreen','olive', 'forestgreen', 'lime',
# 'darkturquoise', 'darkcyan', 'royalblue', 'navy', 'limegreen','olive', 'forestgreen', 'lime'
# ]
# bplot = plt.boxplot(x_noPK_fully, vert=True, patch_artist=True, notch=False, whis=[3,97])
# for patch, color in zip(bplot['boxes'], colors):
# patch.set_facecolor(color)
# # plt.axhline(y=0, color="black", linewidth=1)
# # plt.axhline(y=1, color="black", linewidth=1)
# plt.xticks([1.0+i for i in range(16)], labels[1:])
# plt.ylim((0, 1.01))
# plt.ylabel("MCC", fontsize=12)
# plt.subplots_adjust(left=0.05, right=0.95)
# # plt.title("Performance without pseudoknots (%d RNAs included)" % len(x_noPK_fully[0]))
# ax = plt.subplot(212)
# ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False, labelsize=12)
# ax.xaxis.set_label_position('top')
# for y in [ i/10 for i in range(11) ]:
# plt.axhline(y=y, color="grey", linestyle="--", linewidth=1)
# colors = [ 'green','green',
# 'red', 'firebrick','limegreen','olive', 'forestgreen', 'lime',
# 'darkturquoise', 'darkcyan', 'royalblue', 'navy', 'limegreen','olive', 'forestgreen', 'lime'
# ]
# labels = [ "Biokop"]
# bplot = plt.boxplot(x_PK_fully, vert=True, patch_artist=True, notch=False, whis=[3,97])
# for patch, color in zip(bplot['boxes'], colors):
# patch.set_facecolor(color)
# # plt.axhline(y=0, color="black", linewidth=1)
# # plt.axhline(y=1, color="black", linewidth=1)
# plt.xticks([1.0+i for i in range(16)], labels)
# plt.ylim((0, 1.01))
# plt.ylabel("MCC", fontsize=12)
# plt.subplots_adjust(left=0.05, right=0.95)
# # plt.text(6.2,-0.3,"Performance with pseudoknots (%d RNAs included)" % len(x_PK_fully[0]), fontsize=12)
# # ================== Print results for application cases =====================
# labels = ["Biokop","Biokop","RawA","RawB","BayesPairingA","BayesPairingB","BayesPairingC","BayesPairingD","JAR3DA","JAR3DB","JAR3DC","JAR3DD","BGSUBayesPairingA","BGSUBayesPairingB","BGSUBayesPairingC","BGSUBayesPairingD"]
# print("RNAsubopt",":",x_noPK[0])
# print("RNA-MOIP",":",x_noPK[1])
# for data, name in zip(x_PK, labels):
# print(name,":",data)
# labels = ["RNAsubopt","Biokop\t", "RNA MoIP\t","RawA\t","RawB\t","BayesPairingA","BayesPairingB","BayesPairingC","BayesPairingD","JAR3DA\t","JAR3DB\t","JAR3DC\t","JAR3DD\t","BGSUBPairingA","BGSUBPairingB","BGSUBPairingC","BGSUBPairingD"]
# # for r in RNAcontainer:
# print("\n",r.header_,"\nTrue structure:\t", r.true2d)
# for m, name in zip([r.rnasubopt, r.biokop, r.rnamoip,
# r.biorseoRawA,
# r.biorseoRawB,
# r.biorseoBayesPairA,
# r.biorseoBayesPairB,
# r.biorseoBayesPairC,
# r.biorseoBayesPairD,
# r.biorseoBGSUJAR3DA,
# r.biorseoBGSUJAR3DB,
# r.biorseoBGSUJAR3DC,
# r.biorseoBGSUJAR3DD,
# r.biorseoBGSUBayesPairA,
# r.biorseoBGSUBayesPairB,
# r.biorseoBGSUBayesPairC,
# r.biorseoBGSUBayesPairD ], labels):
# print(name+":\t",m.best_pred)
# ================= PLOTS OF RESULTS =======================================
merge = [ x_PK_fully[0], # Biokop
x_noPK_fully[0], # RNA subopt
x_noPK_fully[1], # RNA MoIP
x_noPK_fully[2], x_PK_fully[2], #biorseoRawA
x_noPK_fully[3], x_PK_fully[3], #biorseoRawB
x_noPK_fully[4], x_PK_fully[4], #biorseoBayesPairA
x_noPK_fully[5], x_PK_fully[5], #biorseoBayesPairB
x_noPK_fully[6], x_PK_fully[6], #biorseoBayesPairC
x_noPK_fully[7], x_PK_fully[7], #biorseoBayesPairD
x_noPK_fully[8], x_PK_fully[8], #biorseoBGSUJAR3DA
x_noPK_fully[9], x_PK_fully[9], #biorseoBGSUJAR3DB
x_noPK_fully[10], x_PK_fully[10], #biorseoBGSUJAR3DC
x_noPK_fully[11], x_PK_fully[11], #biorseoBGSUJAR3DD
x_noPK_fully[12], x_PK_fully[12], #biorseoBGSUBayesPairA
x_noPK_fully[13], x_PK_fully[13], #biorseoBGSUBayesPairB
x_noPK_fully[14], x_PK_fully[14], #biorseoBGSUBayesPairC
x_noPK_fully[15], x_PK_fully[15], #biorseoBGSUBayesPairD
]
colors = [ 'green', 'blue', 'goldenrod',
'darkturquoise', 'darkturquoise',
'red', 'red',
'firebrick', 'firebrick',
'limegreen', 'limegreen',
'olive', 'olive',
'forestgreen', 'forestgreen',
'lime', 'lime',
'darkcyan', 'darkcyan',
'royalblue', 'royalblue',
'navy', 'navy',
'limegreen', 'limegreen',
'olive', 'olive',
'forestgreen', 'forestgreen',
'lime', 'lime'
]
labels = [ "Biokop", "RNAsubopt",
"RNA MoIP",
"$f_{1A}$",
"$f_{1B}$",
"$f_{1A}$",
"$f_{1B}$",
"$f_{1C}$",
"$f_{1D}$",
"$f_{1A}$",
"$f_{1B}$",
"$f_{1C}$",
"$f_{1D}$",
"$f_{1A}$",
"$f_{1B}$",
"$f_{1C}$",
"$f_{1D}$"
]
# plt.show()
ax = plt.subplot(211)
ax.tick_params(labelsize=12)
for y in [ i/10 for i in range(11) ]:
plt.axhline(y=y, color="grey", linestyle="--", linewidth=1)
colors = [ 'blue','goldenrod',
'red', 'firebrick','limegreen','olive', 'forestgreen', 'lime',
'darkturquoise', 'darkcyan', 'royalblue', 'navy', 'limegreen','olive', 'forestgreen', 'lime'
]
bplot = plt.boxplot(x_noPK_fully, vert=True, patch_artist=True, notch=False, whis=[3,97])
for patch, color in zip(bplot['boxes'], colors):
patch.set_facecolor(color)
# plt.axhline(y=0, color="black", linewidth=1)
# plt.axhline(y=1, color="black", linewidth=1)
plt.xticks([1.0+i for i in range(16)], labels[1:])
plt.ylim((0, 1.01))
plt.ylabel("MCC", fontsize=12)
plt.subplots_adjust(left=0.05, right=0.95)
# plt.title("Performance without pseudoknots (%d RNAs included)" % len(x_noPK_fully[0]))
ax = plt.subplot(212)
ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False, labelsize=12)
ax.xaxis.set_label_position('top')
for y in [ i/10 for i in range(11) ]:
plt.axhline(y=y, color="grey", linestyle="--", linewidth=1)
colors = [ 'green','green',
'red', 'firebrick','limegreen','olive', 'forestgreen', 'lime',
'darkturquoise', 'darkcyan', 'royalblue', 'navy', 'limegreen','olive', 'forestgreen', 'lime'
]
labels = [ "Biokop"]
bplot = plt.boxplot(x_PK_fully, vert=True, patch_artist=True, notch=False, whis=[3,97])
for patch, color in zip(bplot['boxes'], colors):
patch.set_facecolor(color)
# plt.axhline(y=0, color="black", linewidth=1)
# plt.axhline(y=1, color="black", linewidth=1)
plt.xticks([1.0+i for i in range(16)], labels)
plt.ylim((0, 1.01))
plt.ylabel("MCC", fontsize=12)
plt.subplots_adjust(left=0.05, right=0.95)
# plt.text(6.2,-0.3,"Performance with pseudoknots (%d RNAs included)" % len(x_PK_fully[0]), fontsize=12)
plt.show()
# # ================== MCC performance ====================================
# # plt.subplot(141)
# RNAs_fully_predicted.sort(key=lambda x: x.rnasubopt.max_mcc)
# x = [
# [ rna.rnasubopt.max_mcc for rna in RNAs_fully_predicted],
# # [ rna.rnamoip.max_mcc for rna in RNAs_fully_predicted],
# [ rna.biorseoRawA.max_mcc for rna in RNAs_fully_predicted],
# [ rna.rnamoip.max_mcc for rna in RNAs_fully_predicted],
# #[ rna.biorseoRawA.max_mcc for rna in RNAs_fully_predicted],
# # [ rna.biorseoRawB.max_mcc for rna in RNAs_fully_predicted],
# [ rna.biokop.max_mcc for rna in RNAs_fully_predicted],
# [ rna.biorseoBGSUJAR3DA.max_mcc for rna in RNAs_fully_predicted]
# #[ rna.biokop.max_mcc for rna in RNAs_fully_predicted],
# #[ rna.biorseoBGSUJAR3DA.max_mcc for rna in RNAs_fully_predicted]
# ]
# colors = ['xkcd:blue','xkcd:red', 'green', 'cyan']
# labels = ["Best RNAsubopt prediction", "Best RawA prediction", "Best Biokop prediction", "Best JAR3DA prediction"]
# colors = ['xkcd:blue','goldenrod']#, 'green', 'cyan']
# labels = ["Best RNAsubopt MCC", "Best RNA-MoIP MCC"]#, "Best Biokop prediction", "Best JAR3DA prediction"]
# for y, col, lab in zip(x, colors, labels):
# x_data = [ i for i in range(len(y)) if y[i]]
# y_data = [ i for i in y if i]
# plt.scatter(x_data, y_data, color=col, label=lab, marker='o', s=2.5)
# plt.axhline(y=0, color='black', linewidth=1)
# plt.axvline(x=0, color='black', linewidth=1)
# plt.xlabel("RNA Strand verified tRNA structures (10 < |nt| < 100)")
# # plt.xlabel("RNA Strand verified tRNA structures (10 < |nt| < 100)")
# plt.ylabel("Mattews Correlation Coefficient")
# plt.title("Performance of the prediction method")
# # plt.title("Performance of the prediction method")
# plt.legend(loc="lower right")
# plt.show()
......