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 onpick(event):
thisline = event.artist
self.event = event
label = thisline.get_label()
if label == cond1:
gene_name = A.index[event.ind[0]]
x1 = round(A.loc[gene_name].log2FoldChange,1)
y1 = round(-np.log10(A.loc[gene_name].padj),1)
try:
x2 = round(B.loc[gene_name].log2FoldChange,1)
y2 = round(-np.log10(B.loc[gene_name].padj),1)
except:
x2, y2 = None, None
else:
gene_name = B.index[event.ind[0]]
x1 = round(B.loc[gene_name].log2FoldChange,1)
y1 = round(-np.log10(B.loc[gene_name].padj),1)
try:
x2 = round(A.loc[gene_name].log2FoldChange,1)
y2 = round(-np.log10(A.loc[gene_name].padj),1)
except:
x2, y2 = None, None
try:
if x2 is None:
ax.title.set_text("{} at pos [{},{}]".format(
gene_name,x1,y1))
else:
ax.title.set_text("{} at pos [{},{}] and [{},{}]".format(
gene_name,x1,y1,x2,y2))
except:
print("exception")
ax.title.set_text("")
pylab.draw()
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 imshow_anova_pairs(self, log=True, **kargs):
import scipy.stats
N = len(self.df.columns)
# could use a dataframe straight way ?
res = np.ones((N, N))
for i, col1 in enumerate(self.df.columns):
for j, col2 in enumerate(self.df.columns):
d1 = self.df[col1]
d2 = self.df[col2]
F, P = scipy.stats.f_oneway(*[d1, d2])
res[i][j] = P
df = pd.DataFrame(res, index=self.df.columns, columns=self.df.columns)
#FIXME: may have na, which are set to 1
df = df.fillna(1)
if log == True:
Imshow(-np.log10(df)).plot(**kargs)
else:
Imshow(df).plot(**kargs)
return df
def plot_volcano(
self,
padj=0.05,
add_broken_axes=False,
markersize=4,
limit_broken_line=[20, 40],
plotly=False,
annotations=None,
):
"""
.. plot::
:include-source:
from sequana.rnadiff import RNADiffResults
from sequana import sequana_data
r = RNADiffResults(sequana_data("rnadiff/rnadiff_onecond_1"))
r.plot_volcano()
"""
if plotly:
from plotly import express as px
df = self.df.copy()
if annotations is not None:
try:
df = pd.concat([df, annotations.annotation], axis=1)
except Exception as err:
logger.warning(
f"Could not merge rnadiff table with annotation. Full error is: {err}"
)
df["log_adj_pvalue"] = -pylab.log10(df.padj)
df["significance"] = [
"<{}".format(padj) if x else ">={}".format(padj)
for x in df.padj < padj
]
if "Name" in df.columns:
hover_name = "Name"
elif "gene_id" in df.columns:
hover_name = "gene_id"
elif "locus_tag" in df.columns:
hover_name = "locus_tag"
elif "ID" in df.columns:
hover_name = "ID"
else:
hover_name = None
fig = px.scatter(
df,
x="log2FoldChange",
y="log_adj_pvalue",
hover_name=hover_name,
hover_data=["baseMean"],
log_y=False,
opacity=0.5,
color="significance",
height=600,
labels={"log_adj_pvalue": "log adjusted p-value"},
)
# axes[0].axhline(
# -np.log10(0.05), lw=2, ls="--", color="r", label="pvalue threshold (0.05)"
# i)
# in future version of plotly, a add_hlines will be available. For
# now, this is the only way to add axhline
fig.update_layout(shapes=[
dict(
type="line",
xref="x",
x0=df.log2FoldChange.min(),
x1=df.log2FoldChange.max(),
yref="y",
y0=-pylab.log10(padj),
y1=-pylab.log10(padj),
line=dict(color="black", width=1, dash="dash"),
)
])
return fig
from brokenaxes import brokenaxes
M = max(-pylab.log10(self.df.padj.dropna()))
br1, br2 = limit_broken_line
if M > br1:
if add_broken_axes:
bax = brokenaxes(ylims=((0, br1), (M - 10, M)), xlims=None)
else:
bax = pylab
else:
bax = pylab
d1 = self.df.query("padj>@padj")
d2 = self.df.query("padj<=@padj")
bax.plot(
d1.log2FoldChange,
-np.log10(d1.padj),
marker="o",
alpha=0.5,
color="k",
lw=0,
markersize=markersize,
)
bax.plot(
d2.log2FoldChange,
-np.log10(d2.padj),
marker="o",
alpha=0.5,
color="r",
lw=0,
markersize=markersize,
)
bax.grid(True)
try:
bax.set_xlabel("fold change")
bax.set_ylabel("log10 adjusted p-value")
except:
bax.xlabel("fold change")
bax.ylabel("log10 adjusted p-value")
m1 = abs(min(self.df.log2FoldChange))
m2 = max(self.df.log2FoldChange)
limit = max(m1, m2)
try:
bax.set_xlim([-limit, limit])
except:
bax.xlim([-limit, limit])
try:
y1, _ = bax.get_ylim()
ax1 = bax.axs[0].set_ylim([br2, y1[1] * 1.1])
except:
y1, y2 = bax.ylim()
bax.ylim([0, y2])
bax.axhline(-np.log10(0.05),
lw=2,
ls="--",
color="r",
label="pvalue threshold (0.05)")
return bax
if colors is None:
colors = {}
for sample in self.sample_names:
colors[sample] = self.colors[self.get_cond_from_sample(sample)]
if plotly is True:
assert n_components == 3
variance = p.plot(
n_components=n_components,
colors=colors,
show_plot=False,
max_features=max_features,
)
from plotly import express as px
df = pd.DataFrame(p.Xr)
df.columns = ["PC1", "PC2", "PC3"]
df["names"] = self.sample_names
df["colors"] = [colors[x] for x in self.sample_names]
df["size"] = [10] * len(df)
df[self.condition] = [
self.get_cond_from_sample(sample)
for sample in self.sample_names
]
fig = px.scatter_3d(
df,
x="PC1",
y="PC2",
z="PC3",
color=self.condition,
labels={
"PC1": "PC1 ({}%)".format(round(100 * variance[0], 2)),
"PC2": "PC2 ({}%)".format(round(100 * variance[1], 2)),
"PC3": "PC3 ({}%)".format(round(100 * variance[2], 2)),
},
height=800,
text="names",
)
return fig
else:
variance = p.plot(n_components=n_components,
colors=colors,
max_features=max_features)
return variance
def plot_volcano(self, labels=None):
"""Volcano plot of log2 fold change versus log10 of adjusted p-value
.. 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_volcano()
"""
cond1, cond2 = "cond1", "cond2"
if labels is None:
labels = [cond1, cond2]
A = self.r1.df.loc[self.r1.gene_lists["all"]]
B = self.r2.df.loc[self.r2.gene_lists["all"]]
if cond1 == cond2:
cond1 += "(1)"
cond2 += "(2)"
pylab.clf()
pylab.plot(A.log2FoldChange, -np.log10(A.padj), marker="o",
alpha=0.5, color="r", lw=0, label=labels[0], pickradius=4,
picker=True)
pylab.plot(B.log2FoldChange, -np.log10(B.padj), marker="x",
alpha=0.5, color="k", lw=0, label=labels[1], pickradius=4,
picker=True)
genes = list(A.index) + list(B.index)
pylab.grid(True)
pylab.xlabel("fold change")
pylab.ylabel("log10 adjusted p-value")
pylab.legend(loc="lower right")
ax = pylab.gca()
def onpick(event):
thisline = event.artist
self.event = event
label = thisline.get_label()
if label == cond1:
gene_name = A.index[event.ind[0]]
x1 = round(A.loc[gene_name].log2FoldChange,1)
y1 = round(-np.log10(A.loc[gene_name].padj),1)
try:
x2 = round(B.loc[gene_name].log2FoldChange,1)
y2 = round(-np.log10(B.loc[gene_name].padj),1)
except:
x2, y2 = None, None
else:
gene_name = B.index[event.ind[0]]
x1 = round(B.loc[gene_name].log2FoldChange,1)
y1 = round(-np.log10(B.loc[gene_name].padj),1)
try:
x2 = round(A.loc[gene_name].log2FoldChange,1)
y2 = round(-np.log10(A.loc[gene_name].padj),1)
except:
x2, y2 = None, None
try:
if x2 is None:
ax.title.set_text("{} at pos [{},{}]".format(
gene_name,x1,y1))
else:
ax.title.set_text("{} at pos [{},{}] and [{},{}]".format(
gene_name,x1,y1,x2,y2))
except:
print("exception")
ax.title.set_text("")
pylab.draw()
fig = pylab.gcf()
fig.canvas.mpl_connect('pick_event', onpick)
