Add pymaf.MafFrame.plot_regplot_tmb:

* Add new method :meth:`pymaf.MafFrame.plot_regplot_tmb`.
* Rename :meth:`pyvcf.VcfFrame.plot_regplot` method to 
:meth:`pyvcf.VcfFrame.plot_regplot_tmb` and 
:meth:`pymaf.MafFrame.plot_regplot` method to 
:meth:`pymaf.MafFrame.plot_regplot_gene`.

CHANGELOG.rst

@@ -7,6 +7,8 @@ Changelog
 * Fix bug in :meth:`pyvcf.VcfFrame.pseudophase` method.
 * Add new methods :meth:`pyvcf.VcfFrame.diploidize` and :meth:`pyvcf.gt_diploidize`.
 * Update :meth:`pyvcf.VcfFrame.get_af` method to handle situations where there are multiple records with the same ``REF`` allele.
+* Add new method :meth:`pymaf.MafFrame.plot_regplot_tmb`.
+* Rename :meth:`pyvcf.VcfFrame.plot_regplot` method to :meth:`pyvcf.VcfFrame.plot_regplot_tmb` and :meth:`pymaf.MafFrame.plot_regplot` method to :meth:`pymaf.MafFrame.plot_regplot_gene`.
 
 0.34.0 (2022-06-08)
 -------------------

fuc/api/pymaf.py

@@ -1295,7 +1295,7 @@ def plot_genepair(
 
  return ax
 
- def plot_regplot(
+ def plot_regplot_gene(
  self, af, group_col, a, b, a_size=None, b_size=None, genes=None,
  count=10, to_csv=None, ax=None, figsize=None, **kwargs
  ):
@@ -1353,7 +1353,7 @@ def plot_regplot(
  >>> annot_file = '~/fuc-data/tcga-laml/tcga_laml_annot.tsv'
  >>> mf = pymaf.MafFrame.from_file(maf_file)
  >>> af = common.AnnFrame.from_file(annot_file, sample_col=0)
- >>> mf.plot_regplot(af, 'FAB_classification', 'M1', 'M2')
+ >>> mf.plot_regplot_gene(af, 'FAB_classification', 'M1', 'M2')
  Results for M2 ~ M1:
  R^2 = 0.43
  P = 3.96e-02
@@ -1398,6 +1398,93 @@ def plot_regplot(
 
  return ax
 
+ def plot_regplot_tmb(
+ self, af, subject_col, group_col, a, b, ax=None, figsize=None,
+ **kwargs
+ ):
+ """
+ Create a scatter plot with a linear regression model fit visualizing
+ correlation between TMB in two sample groups.
+
+ The method will automatically calculate and print summary statistics
+ including R-squared and p-value.
+
+ Parameters
+ ----------
+ af : AnnFrame
+ AnnFrame containing sample annotation data.
+ subject_col : str
+ AnnFrame column containing sample subject information.
+ group_col : str
+ AnnFrame column containing sample group information.
+ a, b : str
+ Sample group names.
+ ax : matplotlib.axes.Axes, optional
+ Pre-existing axes for the plot. Otherwise, crete a new one.
+ figsize : tuple, optional
+ Width, height in inches. Format: (float, float).
+ kwargs
+ Other keyword arguments will be passed down to
+ :meth:`seaborn.regplot`.
+
+ Returns
+ -------
+ matplotlib.axes.Axes
+ The matplotlib axes containing the plot.
+
+ See Also
+ --------
+ fuc.api.pyvcf.VcfFrame.plot_regplot_tmb
+ Similar method for the :meth:`fuc.api.pyvcf.VcfFrame` class.
+
+ Examples
+ --------
+
+ .. plot::
+
+ >>> import matplotlib.pyplot as plt
+ >>> from fuc import common, pymaf, pyvcf
+ >>> common.load_dataset('pyvcf')
+ >>> vcf_file = '~/fuc-data/pyvcf/normal-tumor.vcf'
+ >>> annot_file = '~/fuc-data/pyvcf/normal-tumor-annot.tsv'
+ >>> vf = pyvcf.VcfFrame.from_file(vcf_file)
+ >>> af = common.AnnFrame.from_file(annot_file, sample_col='Sample')
+ >>> mf = pymaf.MafFrame.from_vcf(vf)
+ >>> mf.plot_regplot_tmb(af, 'Patient', 'Tissue', 'Normal', 'Tumor')
+ Results for Tumor ~ Normal:
+ R^2 = 0.01
+ P = 7.17e-01
+ >>> plt.tight_layout()
+ """
+ d = self.df.value_counts('Tumor_Sample_Barcode').to_dict()
+
+ df = af.df[af.df[group_col].isin([a, b])][[group_col, subject_col]]
+ df = df.reset_index().pivot(index=subject_col, columns=group_col,
+ values=af.df.index.name)
+
+ def one_row(r):
+ for col in [a, b]:
+ if r[col] in d:
+ r[col] = d[r[col]]
+ else:
+ r[col] = 0
+ return r
+
+ df = df.apply(one_row, axis=1)
+
+ if ax is None:
+ fig, ax = plt.subplots(figsize=figsize)
+
+ sns.regplot(x=a, y=b, data=df, ax=ax, **kwargs)
+
+ # Print summary statistics including R-squared and p-value.
+ results = smf.ols(f'{b} ~ {a}', data=df).fit()
+ print(f"Results for {b} ~ {a}:")
+ print(f'R^2 = {results.rsquared:.2f}')
+ print(f' P = {results.f_pvalue:.2e}')
+
+ return ax
+
  def plot_interactions(
  self, count=10, cmap=None, ax=None, figsize=None, **kwargs
  ):

fuc/api/pyvcf.py

@@ -3520,7 +3520,9 @@ def plot_tmb(
 
  return ax
 
- def plot_regplot(self, a, b, ax=None, figsize=None, **kwargs):
+ def plot_regplot_tmb(
+ self, a, b, ax=None, figsize=None, **kwargs
+ ):
  """
  Create a scatter plot with a linear regression model fit visualizing
  correlation between TMB in two sample groups.
@@ -3545,6 +3547,11 @@ def plot_regplot(self, a, b, ax=None, figsize=None, **kwargs):
  matplotlib.axes.Axes
  The matplotlib axes containing the plot.
 
+ See Also
+ --------
+ fuc.api.pymaf.MafFrame.plot_regplot_tmb
+ Similar method for the :meth:`fuc.api.pymaf.MafFrame` class.
+
  Examples
  --------
 
@@ -3560,7 +3567,7 @@ def plot_regplot(self, a, b, ax=None, figsize=None, **kwargs):
  >>> normal.name = 'Normal'
  >>> tumor = af.df[af.df.Tissue == 'Tumor'].index
  >>> tumor.name = 'Tumor'
- >>> vf.plot_regplot(normal, tumor)
+ >>> vf.plot_regplot_tmb(normal, tumor)
  Results for B ~ A:
  R^2 = 0.01
  P = 7.17e-01