def testMakeTrinaryData(self):
if IGNORE_TEST:
return
df = transform_data.makeTrinaryData(
df=self.provider.df_normalized)
columns = self.provider.df_normalized.columns
self.assertTrue(helpers.isValidDataFrame(df, columns))
def __init__(self, df_trinary=None):
"""
:param pd.DataFrame df: trinary valued DF (has values -1, 0, 1)
"""
if df_trinary is None:
provider = DataProvider()
provider.do()
df_trinary = transform_data.makeTrinaryData(is_include_nan=False)
self.df_trinary = df_trinary
self.df_group = None # Dataframe describing groups
self.df_gene_group = None # Genes by group
def plotThresholdHeatmap(provider=None,
df=None,
ax=None,
is_plot=True,
min_abs=1.0,
**kwargs):
"""
Plots normalized thresholded expression levels.
:param DataProvider provider:
:param pd.DataFrame df: dataframe to plot with trinary values
:param plt.Axis ax:
:param bool is_plot: shows plot if True
:param float min_abs: minimum absolute for threshold
:param dict kwargs: plotting options
"""
# Data setup
provider = getProvider(provider)
if df is None:
df = provider.df_normalized
df_plot = transform_data.makeTrinaryData(df, min_abs=min_abs)
else:
df_plot = df.copy()
df_stages = provider.df_stage_matrix.copy()
# Plot construct
if ax is None:
plt.figure(figsize=(16, 10))
ax = plt.gca()
if not 'title' in kwargs.keys():
title = "Normalized Data"
else:
title = kwargs["title"]
plot_kwargs = {
cup.XLABEL: "times",
cup.YLABEL: "gene",
cup.TITLE: title,
}
cup.plotTrinaryHeatmap(df_plot, ax=ax, **plot_kwargs, is_plot=False)
columns = df_plot.columns
ax.set_xticks(np.arange(len(columns)))
ax.set_xticklabels(columns)
plotStateTransitions(provider=provider,
ax=ax,
ymax=len(df_plot),
is_plot=False)
if is_plot:
plt.show()
def plotClusteredHeatmap(provider=None, ncluster=5, **kwargs):
"""
Plots a heatmap where categorical axes are grouped with similar values.
:param DataProvider provider:
:param int ncluser: number of clusters
:param dict kwargs: plot parameters
"""
provider = getProvider(provider)
df = transform_data.makeTrinaryData(provider.df_normalized,
is_include_nan=False)
df = util_dataframe.pruneSmallRows(df, min_abs=1.0)
kmeans = KMeans(n_clusters=ncluster, random_state=0).fit(df)
df[CLUSTER] = kmeans.labels_
df = df.sort_values(CLUSTER)
del df[CLUSTER]
df = df.applymap(lambda v: np.nan if np.isclose(v, 0) else v)
plotThresholdHeatmap(provider=provider,
df=df,
title="Clustered Differential Expression",
**kwargs)