def plot_volcano(self):
"""
.. plot::
:include-source:
from sequana.rnadiff import RNADiffResults
from sequana import sequana_data
r = RNADiffResults(sequana_data("rnadiff/rnadiff_onecond_1"))
r.plot_volcano()
"""
d1 = self.df.query("padj>0.05")
d2 = self.df.query("padj<=0.05")
fig = pylab.figure()
pylab.plot(d1.log2FoldChange, -np.log10(d1.padj), marker="o",
alpha=0.5, color="r", lw=0)
pylab.plot(d2.log2FoldChange, -np.log10(d2.padj), marker="o",
alpha=0.5, color="k", lw=0)
pylab.grid(True)
pylab.xlabel("fold change")
pylab.ylabel("log10 adjusted p-value")
m1 = abs(min(self.df.log2FoldChange))
m2 = max(self.df.log2FoldChange)
limit = max(m1,m2)
pylab.xlim([-limit, limit])
y1,y2 = pylab.ylim()
pylab.ylim([0,y2])
pylab.axhline(-np.log10(0.05), lw=2, ls="--", color="r", label="pvalue threshold (0.05)")
def plot_idr_vs_peaks(self, filename=None, savefig=False):
# global_idr is actually -log10(idr)
pylab.clf()
X1 = pylab.linspace(0, self.threshold, 100)
X2 = pylab.linspace(self.threshold, 1, 100)
# convert global idr to proba
df1 = self.df.query("idr<@self.threshold")
df2 = self.df.query("idr>[email protected]")
pylab.plot([sum(df1['idr'] < x) for x in X1], X1, '-', color='r', lw=2)
shift = len(df1)
pylab.plot([shift + sum(df2['idr'] < x) for x in X2],
X2,
"-",
color='k',
lw=2)
pylab.xlabel('Number of significant peaks')
pylab.ylabel('IDR')
pylab.axhline(0.05, color='b', ls='--')
pylab.axvline(self.N_significant_peaks, color='b', ls='--')
if savefig:
pylab.savefig(filename)
def plot_polymerase_per_barcode(self, fontsize=12, unbarcoded=True):
"""Number Of Polymerase Reads Per Barcode"""
PR = self.df_barcoded["Polymerase Reads"].sum()
data = self.df_barcoded['Polymerase Reads'].sort_values(
ascending=False).values
pylab.plot([int(x) for x in range(1,
len(data) + 1)],
data,
label="barcodes")
pylab.axhline(data.mean(), color="r", label="average")
try:
if unbarcoded is True:
unbar = self.df_not_barcoded['Polymerase Reads'].iloc[0]
pylab.axhline(unbar, color="k", ls="--", label="not barcoded")
except:
pass
pylab.xlabel("Barcode Rank Order", fontsize=fontsize)
pylab.ylabel("Counts of Reads", fontsize=fontsize)
pylab.title("Total Polymerase count: {}".format(PR))
pylab.legend()
pylab.ylim(ymin=0)
try:
pylab.tight_layout()
except:
pass
def plot_percentage_null_read_counts(self):
"""
Bars represent the percentage of null counts in each samples.
The dashed horizontal line represents the percentage of
feature counts being equal to zero across all samples.
.. plot::
:include-source:
from sequana.rnadiff import RNADiffResults
from sequana import sequana_data
r = RNADiffResults(sequana_data("rnadiff/rnadiff_onecond_1"))
r.plot_percentage_null_read_counts()
"""
N = len(self.sample_names)
data = (self.df[self.sample_names]==0).sum()
data = data / len(self.df) * 100
all_null = (self.df[self.sample_names].sum(axis=1) == 0).sum()
pylab.clf()
pylab.bar(range(N), data)
pylab.axhline(all_null / len(self.df) * 100, lw=2, ls="--", color="k")
pylab.xticks(range(N), self.sample_names)
pylab.xlabel("Sample")
def scatter_length_cov_gc(self, min_length=200, min_cov=10):
pylab.clf()
pylab.scatter(self.df.length, self.df['cov'], c=self.df.GC)
pylab.loglog()
pylab.axvline(min_length, lw=2, c="r", ls='--')
pylab.axhline(min_cov, lw=2, c="r", ls='--')
pylab.xlabel("contig length")
pylab.ylabel("contig coverage")
pylab.colorbar(label="GC")
pylab.grid(True)
def plot_volcano_differences(self, mode="all"):
cond1, cond2 = "cond1", "cond2"
labels = [cond1, cond2]
A = self.r1.df.loc[self.r1.gene_lists[mode]]
B = self.r2.df.loc[self.r2.gene_lists[mode]]
AB = set(A.index).intersection(set(B.index))
Aonly = A.loc[set(A.index).difference(set(B.index))]
Bonly = B.loc[set(B.index).difference(set(A.index))]
Acommon = A.loc[AB]
Bcommon = B.loc[AB]
pylab.clf()
pylab.plot(Acommon.log2FoldChange, -np.log10(Acommon.padj), marker="o",
alpha=0.5, color="r", lw=0, label="Common in experiment 1", pickradius=4,
picker=True)
pylab.plot(Bcommon.log2FoldChange, -np.log10(Bcommon.padj), marker="o",
alpha=0.5, color="orange", lw=0, label="Common in experiment 2", pickradius=4,
picker=True)
for x in AB:
a_l = A.loc[x].log2FoldChange
a_p = -np.log10(A.loc[x].padj)
b_l = B.loc[x].log2FoldChange
b_p = -np.log10(B.loc[x].padj)
pylab.plot([a_l, b_l], [a_p, b_p], 'k', alpha=0.5)
pylab.plot(Bonly.log2FoldChange, -np.log10(Bonly.padj), marker="*",
alpha=0.5, color="blue", lw=0, label="In experiment 2 only", pickradius=4,
picker=True)
pylab.plot(Aonly.log2FoldChange, -np.log10(Aonly.padj), marker="*",
alpha=0.5, color="cyan", lw=0, label="In experiment 1 only", pickradius=4,
picker=True)
for name, x in Bonly.iterrows():
x1 = x.log2FoldChange
y1 = -np.log10(x.padj)
x2 = self.r1.df.loc[name].log2FoldChange
y2 = -np.log10(self.r1.df.loc[name].padj)
pylab.plot( [x1,x2], [y1,y2], ls="--", color='r')
for name, x in Aonly.iterrows():
x1 = x.log2FoldChange
y1 = -np.log10(x.padj)
x2 = self.r2.df.loc[name].log2FoldChange
y2 = -np.log10(self.r2.df.loc[name].padj)
pylab.plot( [x1,x2], [y1,y2], ls="-", color='r')
pylab.axhline(1.33, alpha=0.5, ls="--", color="r")
pylab.xlabel("log2 fold Change")
pylab.ylabel("log10 adjusted p-values")
pylab.legend()
pylab.grid(True)
return Aonly, Bonly, Acommon, Bcommon
def plot(self, clf=True):
if clf:
pylab.clf()
M = self.df_shustring.shustring_length.max()
print(M)
M = int(M / 1000) + 1
for i in range(M):
pylab.axhline(i * 1000, ls='--', color='grey')
pylab.plot(self.df_shustring.shustring_length)
pylab.xlabel('position (bp)')
pylab.ylabel('Length of repeats')
pylab.ylim(bottom=0)
def plot(self,
X=[0, 0.1, 0.2, 0.3, .4, .5, .6, .7, .8, .9, .95, .99, .999, 1],
fontsize=16,
label=None):
"""plot percentage of genes covered (y axis) as a function of percentage
of genes covered at least by X percent (x-axis).
"""
icol = self.coverage_column
N = float(len(self.df))
X = np.array(X)
Y = np.array([sum(self.df[icol] > x) / N * 100 for x in X])
if label is None:
pylab.plot(X * 100, Y, "o-")
else:
pylab.plot(X * 100, Y, "o-", label=label)
pylab.xlabel("Gene coverage (%)", fontsize=fontsize)
pylab.ylabel("Percentage of genes covered", fontsize=fontsize)
for this in [25, 50, 75]:
pylab.axhline(this, color="r", alpha=0.5, ls="--")
pylab.axvline(this, color="r", alpha=0.5, ls="--")
def plot_percentage_null_read_counts(self):
"""
Bars represent the percentage of null counts in each samples.
The dashed horizontal line represents the percentage of
feature counts being equal to zero across all samples.
.. plot::
:include-source:
from sequana.rnadiff import RNADiffResults
from sequana import sequana_data
r = RNADiffResults(sequana_data("rnadiff/rnadiff_onecond_1"))
r.plot_percentage_null_read_counts()
"""
N = len(self.sample_names)
data = (self.df[self.sample_names]==0).sum()
data = data / len(self.df) * 100
all_null = (self.df[self.sample_names].sum(axis=1) == 0).sum()
colors = []
for sample in self.sample_names:
colors.append(self.colors[self.get_cond_from_sample(sample)])
pylab.clf()
pylab.bar(range(N), data,
color=colors, alpha=1,
zorder=10, lw=1, ec="k", width=0.9)
pylab.axhline(all_null / len(self.df) * 100, lw=2, ls="--", color="k",
zorder=20)
pylab.xticks(range(N), self.sample_names)
pylab.xlabel("Sample")
pylab.ylabel("Proportion of null counts (%)")
pylab.grid(True, zorder=0)
def plot_percentage_null_read_counts(self):
"""Bars represent the percentage of null counts in each samples. The dashed
horizontal line represents the percentage of feature counts being equal
to zero across all samples
.. plot::
:include-source:
from sequana.rnadiff import RNADiffResults
from sequana import sequana_data
r = RNADiffResults(sequana_data("rnadiff/rnadiff_onecond_1"))
r.plot_percentage_null_read_counts()
"""
pylab.clf()
# how many null counts ?
df = (self.counts_raw == 0).sum() / self.counts_raw.shape[0] * 100
df = df.rename("percent_null")
df = pd.concat([self.design_df, df], axis=1)
pylab.bar(df.index,
df.percent_null,
color=df.group_color,
ec="k",
lw=1,
zorder=10)
all_null = (self.counts_raw
== 0).all(axis=1).sum() / self.counts_raw.shape[0]
pylab.axhline(all_null, ls="--", color="black", alpha=0.5)
pylab.xticks(rotation=45, ha="right")
pylab.ylabel("Proportion of null counts (%)")
pylab.grid(True, zorder=0)
pylab.tight_layout()
def plot_RSC(self):
self.df.RSC.plot(kind='bar')
pylab.axhline(0.8, lw=2, color='r', ls='--')
def plot_common_major_counts(self, mode, labels=None,
switch_up_down_cond2=False, add_venn=True, xmax=None,
title="", fontsize=12, sortby="log2FoldChange"):
"""
:param mode: down, up or all
.. plot::
:include-source:
from sequana import sequana_data
from sequana.compare import RNADiffCompare
c = RNADiffCompare(
sequana_data("rnadiff/rnadiff_onecond_1"),
sequana_data("rnadiff/rnadiff_onecond_2"))
c.plot_common_major_counts("down")
"""
#cond1, cond2 = self._get_cond1_cond2()
if labels is None:
labels = ['r1', 'r2']
if mode in ["down"]:
# Negative values !
gl1 = set(self.r1.gene_lists['down'])
gl2 = set(self.r2.gene_lists['down'])
A = self.r1.df.loc[gl1].sort_values(by=sortby)
B = self.r2.df.loc[gl1].sort_values(by=sortby)
else:
gl1 = set(self.r1.gene_lists[mode])
gl2 = set(self.r2.gene_lists[mode])
A = self.r1.df.loc[gl1].sort_values(by=sortby, ascending=False)
B = self.r2.df.loc[gl1].sort_values(by=sortby, ascending=False)
# sometimes, up and down may be inverted as compared to the other
# conditions
N = []
for i in range(1,max(len(A), len(B))):
a = A.iloc[0:i].index
b = B.iloc[0:i].index
n = len(set(b).intersection(set(a)))
N.append(n / i*100)
max_common = len(set(A.index).intersection(set(B.index)))
pylab.clf()
if len(A) > len(B):
pylab.axhline(max_common/len(A)*100, color="r", ls='--', label="min set intersection")
pylab.axvline(len(B), ls="--", color="k", label="rank of minor set")
else:
pylab.axhline(max_common/len(B)*100, color='r', ls='--', label="min set intersect")
pylab.axvline(len(A), ls="--", color="k", label="rank of minor set")
pylab.plot(N)
pylab.xlabel('rank', fontsize=fontsize)
pylab.ylabel('% common features', fontsize=fontsize)
pylab.grid(True)
pylab.ylim([0,100])
if xmax:
pylab.xlim([0, xmax])
else:
pylab.xlim([0, max(len(A),len(B))])
pylab.title(title, fontsize=fontsize)
ax = pylab.gca()
ax2 = ax.twinx()
ax2.plot(A[sortby].values, "orange", label=sortby)
ax2.set_ylabel(sortby)
pylab.legend(loc="lower left")
ax.legend(loc="lower right")
if add_venn:
f = pylab.gcf()
ax = f.add_axes([0.5,0.5,0.35,0.35], facecolor="grey")
if mode=="down":
self.plot_venn_down(ax=ax, title=None, labels=labels,
mode="two_only")
elif mode=="up":
self.plot_venn_up(ax=ax, title=None, labels=labels,
mode="two_only")
elif mode=="all":
self.plot_venn_all(ax=ax, title=None, labels=labels,
mode="two_only")
