Commit ed111f22 authored by Blaise Li's avatar Blaise Li
Browse files

Scatterplot tweaking. Code homogeneization.

parent f2d5d8f0
......@@ -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"
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):
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)
(status, colour) = group2colour
row_indices = data.index.intersection(grouping)
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,
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")
......@@ -420,7 +423,10 @@ def plot_scatter(data,
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)
x=x_column, y=y_column,
s=2, c="black", alpha=0.15, edgecolors='none',
if regression:
linreg = linregress(data[[x_column, y_column]].dropna())
a = linreg.slope
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