Scatterplot tweaking. Code homogeneization.

libhts/libhts.py

@@ -376,11 +376,14 @@ def plot_MA(res,
     fig, ax = plt.subplots()
     # Make a column indicating whether the gene is DE or NS
     data = res.assign(is_DE=res.apply(set_de_status, axis=1))
+    x_column = "baseMean"
     if fold_type is None:
-        fold_type = "log2FoldChange"
+        y_column = "log2FoldChange"
+    else:
+        y_column = fold_type
     # First plot the data in grey and black
     for de_status, group in data.groupby("is_DE"):
-        group.plot.scatter(x="baseMean", y=fold_type, s=2, logx=True, c=DE2COLOUR[de_status], label=f"{de_status} ({len(group)})", ax=ax)
+        group.plot.scatter(x=x_column, y=y_column, s=2, logx=True, c=DE2COLOUR[de_status], label=f"{de_status} ({len(group)})", ax=ax)
     if grouping is not None:
         if isinstance(grouping, str):
             # Overlay colours based on the "grouping" column
@@ -388,13 +391,13 @@ def plot_MA(res,
                 group2colour = STATUS2COLOUR
             for status, group in data.groupby(grouping):
                 group.plot.scatter(
-                    x="baseMean", y=fold_type, s=1, logx=True, c=group2colour[status],
+                    x=x_column, y=y_column, s=1, logx=True, c=group2colour[status],
                     label=f"{status} ({len(group)})", ax=ax)
         else:
             (status, colour) = group2colour
             row_indices = data.index.intersection(grouping)
             data.ix[row_indices].plot.scatter(
-                x="baseMean", y=fold_type, s=1, logx=True, c=colour,
+                x=x_column, y=y_column, s=1, logx=True, c=colour,
                 label=f"{status} ({len(row_indices)})", ax=ax)
     ax.axhline(y=1, linewidth=0.5, color="0.5", linestyle="dashed")
     ax.axhline(y=-1, linewidth=0.5, color="0.5", linestyle="dashed")
@@ -403,10 +406,10 @@ def plot_MA(res,
         ax.set_xlim(mean_range)
     if lfc_range is not None:
         (lfc_min, lfc_max) = lfc_range
-        lfc_here_min = getattr(data, fold_type).min()
-        lfc_here_max = getattr(data, fold_type).max()
+        lfc_here_min = getattr(data, y_column).min()
+        lfc_here_max = getattr(data, y_column).max()
         if (lfc_here_min < lfc_min) or (lfc_here_max > lfc_max):
-            warnings.warn(f"Cannot plot {fold_type} data ([{lfc_here_min}, {lfc_here_max}]) in requested range ([{lfc_min}, {lfc_max}])\n")
+            warnings.warn(f"Cannot plot {y_column} data ([{lfc_here_min}, {lfc_here_max}]) in requested range ([{lfc_min}, {lfc_max}])\n")
         else:
             ax.set_ylim(lfc_range)
 
@@ -420,7 +423,10 @@ def plot_scatter(data,
                  y_range=None):
     fig, ax = plt.subplots()
     # First plot the data in grey
-    data.plot.scatter(x=x_column, y=y_column, s=2, c="lightgray", ax=ax)
+    data.plot.scatter(
+        x=x_column, y=y_column,
+        s=2, c="black", alpha=0.15, edgecolors='none',
+        ax=ax)
     if regression:
         linreg = linregress(data[[x_column, y_column]].dropna())
         a = linreg.slope