diff --git a/plot_scatterplot.py b/plot_scatterplot.py
index 9478e636f954941854e9c404d9519fdca390eae9..b4d43781e4bd1fc52d57cf24b85a147107ff3d81 100755
--- a/plot_scatterplot.py
+++ b/plot_scatterplot.py
@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
 # vim: set fileencoding=<utf-8> :
-"""This script reads data from "tidy" files and makes plots out of
-it, at the same scale.
+"""This script reads data from "tidy" files and makes a scatter plot out of it.
 It also outputs a table containing the plotted data points."""
 
 import argparse
@@ -91,12 +90,19 @@ class Scatterplot:
                  y_column,
                  labels,
                  extra_cols=None):
+        # usecols can be a callable to filter column names:
+        # If callable, the callable function will be evaluated against the
+        # column names, returning names where the callable function evaluates
+        # to True.
         if extra_cols is None:
             x_usecols = ["gene", x_column].__contains__
             y_usecols = ["gene", y_column].__contains__
         else:
             x_usecols = ["gene", x_column, *extra_cols].__contains__
             y_usecols = ["gene", y_column, *extra_cols].__contains__
+        # The columns containing the data to plot might have the same name
+        # in the two tables.
+        # We rename them to x and y for simplicity.
         x_data = pd.read_csv(
             x_input_file, sep="\t", index_col="gene", usecols=x_usecols).rename(
                 columns={x_column: "x"})
@@ -115,6 +121,7 @@ class Scatterplot:
         self.data = pd.merge(
             x_data, y_data,
             left_index=True, right_index=True, validate="one_to_one")
+        # Compute a classifier column (to be used to colour points)
         if extra_cols is not None:
             extra_cols = list(concat((
                 [colname] if colname in self.data.columns
@@ -148,7 +155,7 @@ class Scatterplot:
     def plot_maker(self, grouping=None, group2colour=None, **kwargs):
         """Builds a plotting function that can colour dots based on them
         belonging to a group defined by *grouping*."""
-        def plot_lfclfc_scatter():
+        def plotting_function():
             """Generates the scatterplot, returns its legend so that
             *save_plot* can include it in the bounding box."""
             # fig, axis = plot_scatter(
@@ -297,9 +304,11 @@ class Scatterplot:
             # TODO: force ticks to be integers
             # Return a tuple of "extra artists",
             # to correctly define the bounding box
-        return plot_lfclfc_scatter
+        return plotting_function
 
-    def save_plot(self, outfile, grouping=None, group2colour=None, **kwargs):
+    def save_plot(self, outfile,
+                  grouping=None, group2colour=None,
+                  **kwargs):
         """Creates the plotting function and transmits it for execution
         to the function that really does the saving."""
         if grouping is None and self.grouping_col is not None: