def plot_spearman_matrix(self,
omic1=OMIC.transcriptomic,
omic2=OMIC.proteomic,
var_names1=MARKER_ADT_GENE.values(),
var_names2=MARKER_ADT_GENE.keys(),
is_marker_pairs=True,
title='',
return_figure=False):
r""" Plot correlation matrix between omic1 and omic2
"""
n_var1 = self.get_n_var(omic1)
n_var2 = self.get_n_var(omic2)
corr = self.get_correlation(omic1, omic2)
spearman = np.zeros(shape=(n_var1, n_var2), dtype=np.float64)
for i1, i2, p, s in corr:
spearman[i1, i2] = s
return self._plot_heatmap_matrix(
matrix=spearman,
figname="Spearman",
omic1=omic1,
omic2=omic2,
var_names1=var_names1,
var_names2=var_names2,
is_marker_pairs=is_marker_pairs,
title=title,
return_figure=return_figure,
)
def plot_pearson_matrix(self,
omic1=OMIC.transcriptomic,
omic2=OMIC.proteomic,
var_names1=MARKER_ADT_GENE.values(),
var_names2=MARKER_ADT_GENE.keys(),
is_marker_pairs=True,
title='',
return_figure=False):
r""" Plot correlation matrix between omic1 and omic2
Arguments:
omic1, omic2 : instance of OMIC.
With `omic1` represent the x-axis, and `omic2` represent the y-axis.
markers : a List of String (optional)
a list of `omic1` variable that should be most coordinated to `omic2`
"""
n_var1 = self.get_n_var(omic1)
n_var2 = self.get_n_var(omic2)
corr = self.get_correlation(omic1, omic2)
pearson = np.zeros(shape=(n_var1, n_var2), dtype=np.float64)
for i1, i2, p, s in corr:
pearson[i1, i2] = p
return self._plot_heatmap_matrix(
matrix=pearson,
figname="Pearson",
omic1=omic1,
omic2=omic2,
var_names1=var_names1,
var_names2=var_names2,
is_marker_pairs=is_marker_pairs,
title=title,
return_figure=return_figure,
)
def plot_importance_matrix(self,
omic1=OMIC.transcriptomic,
omic2=OMIC.proteomic,
var_names1=MARKER_ADT_GENE.values(),
var_names2=MARKER_ADT_GENE.keys(),
is_marker_pairs=True,
title='',
return_figure=False):
r""" Showing importance heatmap matrix """
return self._plot_heatmap_matrix(
matrix=self.get_importance_matrix(omic1, omic2),
figname="Importance",
omic1=omic1,
omic2=omic2,
var_names1=var_names1,
var_names2=var_names2,
is_marker_pairs=is_marker_pairs,
title=title,
return_figure=return_figure,
)
def plot_mutual_information(self,
omic1=OMIC.transcriptomic,
omic2=OMIC.proteomic,
var_names1=MARKER_ADT_GENE.values(),
var_names2=MARKER_ADT_GENE.keys(),
is_marker_pairs=True,
title='',
return_figure=False):
r""" Plot estimated mutual information between each variable pair in
omic1 and omic2. """
return self._plot_heatmap_matrix(
matrix=self.get_mutual_information(omic1, omic2),
figname="MutualInfo",
omic1=omic1,
omic2=omic2,
var_names1=var_names1,
var_names2=var_names2,
is_marker_pairs=is_marker_pairs,
title=title,
return_figure=return_figure,
)
def _plot_heatmap_matrix(self,
matrix,
figname,
omic1=OMIC.transcriptomic,
omic2=OMIC.proteomic,
var_names1=MARKER_ADT_GENE.values(),
var_names2=MARKER_ADT_GENE.keys(),
is_marker_pairs=True,
title='',
return_figure=False):
omic1 = OMIC.parse(omic1)
omic2 = OMIC.parse(omic2)
if isinstance(var_names1, string_types) and var_names1 == 'auto':
var_names1 = omic1.markers
if isinstance(var_names2, string_types) and var_names2 == 'auto':
var_names2 = omic2.markers
if var_names1 is None or var_names2 is None:
is_marker_pairs = False
names1 = self.get_var_names(omic1)
names2 = self.get_var_names(omic2)
om1_idx = {j: i for i, j in enumerate(names1)}
om2_idx = {j: i for i, j in enumerate(names2)}
assert matrix.shape == (len(names1), len(names2)), \
(f"Given OMIC {omic1.name}({len(names1)} variables) and "
f"OMIC {omic2.name}({len(names2)} variables) "
f"mistmach matrix shape {matrix.shape}")
## filter the variables
if is_marker_pairs:
pairs = [(v1, v2) for v1, v2 in zip(var_names1, var_names2)
if v1 in om1_idx and v2 in om2_idx]
var_names1 = [i for i, _ in pairs]
var_names2 = [i for _, i in pairs]
if var_names1 is not None:
names1 = np.array([i for i in var_names1 if i in om1_idx])
matrix = matrix[[om1_idx[i] for i in names1]]
if var_names2 is not None:
names2 = np.array([i for i in var_names2 if i in om2_idx])
matrix = matrix[:, [om2_idx[i] for i in names2]]
## find the best diagonal match
if is_marker_pairs:
ids2 = list(range(len(names2)))
else:
ids2 = search.diagonal_linear_assignment(matrix, nan_policy=0)
matrix = matrix[:, ids2]
names2 = names2[ids2].tolist()
names1 = names1.tolist()
n1 = len(names1)
n2 = len(names2)
## helper for marking the marker
def _mark(ax):
# row is yaxis and col is xaxis
for y, row in enumerate(matrix):
# sort descending order
order = np.argsort(row)[::-1]
x = order[0]
ax.text(x + 0.02,
y + 0.03,
s=f"{matrix[y, x]:.2f}",
horizontalalignment='center',
verticalalignment='center',
fontsize=32 / np.log1p(max(n1, n2)),
color='magenta',
alpha=0.8,
weight='regular')
## plotting
styles = dict(cmap="bwr",
xticklabels=names2,
yticklabels=names1,
xlabel=omic2.name,
ylabel=omic1.name,
gridline=0.1,
fontsize=10,
cbar=True)
width = min(25, matrix.shape[1] / 1.2)
fig = plt.figure(figsize=(width,
width * matrix.shape[0] / matrix.shape[1]))
_mark(
vs.plot_heatmap(
matrix,
**styles,
ax=None,
title=f"[{figname}_x:{omic2.name}_y:{omic1.name}]{title}"))
with catch_warnings_ignore(UserWarning):
fig.tight_layout(rect=[0.0, 0.02, 1.0, 0.98])
## store and return
if return_figure:
return fig
self.add_figure(f"{figname.lower()}_{omic1.name}_{omic2.name}", fig)
return self