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Add Plotting of Arbitrary Principal Components #51

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be53a08
added capability of plotting arbitrary pca components for pcaplot and…
richy1996 Jul 11, 2022
d870402
updated docs on pcaplot and biplot
richy1996 Jul 11, 2022
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10 changes: 8 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -287,7 +287,7 @@ Scree plot image (screeplot.png will be saved in same directory)
`latest update v2.0.1`

`bioinfokit.visuz.cluster.pcaplot(x, y, z, labels, var1, var2, var3, axlabelfontsize, axlabelfontname,
figtype, r, show, plotlabels, dim, theme)`
figtype, r, show, plotlabels, dim, theme, firstToPlot, secondToPlot, thirdToPlot)`

Parameters | Description
------------ | -------------
Expand All @@ -306,6 +306,9 @@ Parameters | Description
`plotlabels` | Plot labels as defined by labels parameter [True or False][default:True]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
`firstToPlot` | PCA component to plot on the x axis [int (default:0)]
`secondToPlot` | PCA component to plot on the y axis [int (default:1)]
`thirdToPlot` | PCA component to plot on the z axis [int (default:2)]

Returns:

Expand All @@ -319,7 +322,7 @@ PCA loadings plot 2D and 3D image (pcaplot_2d.png and pcaplot_3d.png will be sav

`bioinfokit.visuz.cluster.biplot(cscore, loadings, labels, var1, var2, var3, axlabelfontsize, axlabelfontname,
figtype, r, show, markerdot, dotsize, valphadot, colordot, arrowcolor, valphaarrow, arrowlinestyle, arrowlinewidth,
centerlines, colorlist, legendpos, datapoints, dim, theme)`
centerlines, colorlist, legendpos, datapoints, dim, theme, firstToPlot, secondToPlot, thirdToPlot)`

Parameters | Description
------------ | -------------
Expand Down Expand Up @@ -348,6 +351,9 @@ Parameters | Description
`datapoints`| plot data points on graph [bool (True or False)][default: True]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
`firstToPlot` | PCA component to plot on the x axis [int (default:0)]
`secondToPlot` | PCA component to plot on the y axis [int (default:1)]
`thirdToPlot` | PCA component to plot on the z axis [int (default:2)]

Returns:

Expand Down
92 changes: 47 additions & 45 deletions bioinfokit/visuz.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1795,7 +1795,8 @@ def screeplot(obj="pcascree", axlabelfontsize=9, axlabelfontname="Arial", axxlab

@staticmethod
def pcaplot(x=None, y=None, z=None, labels=None, var1=None, var2=None, var3=None, axlabelfontsize=9,
axlabelfontname="Arial", figtype='png', r=300, show=False, plotlabels=True, dim=(6, 4), theme=None):
axlabelfontname="Arial", figtype='png', r=300, show=False, plotlabels=True, dim=(6, 4), theme=None,
firstToPlot = 0, secondToPlot=1, thirdToPlot=2):
if theme == 'dark':
general.dark_bg()
if x is not None and y is not None and z is None:
Expand All @@ -1805,7 +1806,7 @@ def pcaplot(x=None, y=None, z=None, labels=None, var1=None, var2=None, var3=None
plt.scatter(x[i], y[i])
if plotlabels:
plt.text(x[i], y[i], varnames, fontsize=10)
general.axis_labels("PC1 ({}%)".format(var1), "PC2 ({}%)".format(var2), axlabelfontsize, axlabelfontname)
general.axis_labels("PC{} ({}%)".format(firstToPlot+1, var1), "PC{} ({}%)".format(secondToPlot+1, var2), axlabelfontsize, axlabelfontname)
general.get_figure(show, r, figtype, 'pcaplot_2d', theme)
elif x is not None and y is not None and z is not None:
assert var1 and var2 and var3 and labels is not None, "var1 or var2 or var3 or labels are missing"
Expand All @@ -1816,25 +1817,27 @@ def pcaplot(x=None, y=None, z=None, labels=None, var1=None, var2=None, var3=None
ax.scatter(x[i], y[i], z[i])
if plotlabels:
ax.text(x[i], y[i], z[i], varnames, fontsize=10)
ax.set_xlabel("PC1 ({}%)".format(var1), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_ylabel("PC2 ({}%)".format(var2), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_zlabel("PC3 ({}%)".format(var3), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_xlabel("PC{} ({}%)".format(firstToPlot+1, var1), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_ylabel("PC{} ({}%)".format(secondToPlot+1, var2), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_zlabel("PC{} ({}%)".format(thirdToPlot+1, var3), fontsize=axlabelfontsize, fontname=axlabelfontname)
general.get_figure(show, r, figtype, 'pcaplot_3d', theme)

@staticmethod
# adapted from https://stackoverflow.com/questions/39216897/plot-pca-loadings-and-loading-in-biplot-in-sklearn-like-rs-autoplot
def biplot(cscore=None, loadings=None, labels=None, var1=None, var2=None, var3=None, axlabelfontsize=9, axlabelfontname="Arial",
figtype='png', r=300, show=False, markerdot="o", dotsize=6, valphadot=1, colordot='#eba487', arrowcolor='#87ceeb',
valphaarrow=1, arrowlinestyle='-', arrowlinewidth=0.5, centerlines=True, colorlist=None, legendpos='best',
datapoints=True, dim=(6, 4), theme=None):
datapoints=True, dim=(6, 4), theme=None,
firstToPlot = 0, secondToPlot=1, thirdToPlot=2):

if theme == 'dark':
general.dark_bg()
assert cscore is not None and loadings is not None and labels is not None and var1 is not None and var2 is not None, \
"cscore or loadings or labels or var1 or var2 are missing"
if var1 is not None and var2 is not None and var3 is None:
xscale = 1.0 / (cscore[:, 0].max() - cscore[:, 0].min())
yscale = 1.0 / (cscore[:, 1].max() - cscore[:, 1].min())
# zscale = 1.0 / (cscore[:, 2].max() - cscore[:, 2].min())
xscale = 1.0 / (cscore[:, firstToPlot].max() - cscore[:, firstToPlot].min())
yscale = 1.0 / (cscore[:, secondToPlot].max() - cscore[:, secondToPlot].min())
# zscale = 1.0 / (cscore[:, thirdToPlot].max() - cscore[:, thirdToPlot].min())
# colorlist is an array of classes from dataframe column
plt.subplots(figsize=dim)
if datapoints:
Expand All @@ -1848,44 +1851,44 @@ def biplot(cscore=None, loadings=None, labels=None, var1=None, var2=None, var3=N
# for i in range(len(list(unique_class))):
# color_dict[list(unique_class)[i]] = colordot[i]
# color_result = [color_dict[i] for i in colorlist]
s = plt.scatter(cscore[:, 0] * xscale, cscore[:, 1] * yscale, c=color_result_num, cmap=colour_map,
s = plt.scatter(cscore[:, firstToPlot] * xscale, cscore[:, secondToPlot] * yscale, c=color_result_num, cmap=colour_map,
s=dotsize, alpha=valphadot, marker=markerdot)
plt.legend(handles=s.legend_elements()[0], labels=list(unique_class), loc=legendpos)
elif colordot and not isinstance(colordot, (tuple, list)):
# s = plt.scatter(cscore[:, 0] * xscale, cscore[:, 1] * yscale, color=color_result, s=dotsize,
# s = plt.scatter(cscore[:, firstToPlot] * xscale, cscore[:, secondToPlot] * yscale, color=color_result, s=dotsize,
# alpha=valphadot, marker=markerdot)
# plt.legend(handles=s.legend_elements()[0], labels=list(unique_class))
s = plt.scatter(cscore[:, 0] * xscale, cscore[:, 1] * yscale, c=color_result_num, s=dotsize,
s = plt.scatter(cscore[:, firstToPlot] * xscale, cscore[:, secondToPlot] * yscale, c=color_result_num, s=dotsize,
alpha=valphadot, marker=markerdot)
plt.legend(handles=s.legend_elements()[0], labels=list(unique_class), loc=legendpos)
else:
plt.scatter(cscore[:, 0] * xscale, cscore[:, 1] * yscale, color=colordot, s=dotsize,
plt.scatter(cscore[:, firstToPlot] * xscale, cscore[:, secondToPlot] * yscale, color=colordot, s=dotsize,
alpha=valphadot, marker=markerdot)
if centerlines:
plt.axhline(y=0, linestyle='--', color='#7d7d7d', linewidth=1)
plt.axvline(x=0, linestyle='--', color='#7d7d7d', linewidth=1)
# loadings[0] is the number of the original variables
# this is important where variables more than number of observations
for i in range(len(loadings[0])):
plt.arrow(0, 0, loadings[0][i], loadings[1][i], color=arrowcolor, alpha=valphaarrow, ls=arrowlinestyle,
plt.arrow(0, 0, loadings[firstToPlot][i], loadings[secondToPlot][i], color=arrowcolor, alpha=valphaarrow, ls=arrowlinestyle,
lw=arrowlinewidth)
plt.text(loadings[0][i], loadings[1][i], labels[i])
plt.text(loadings[firstToPlot][i], loadings[secondToPlot][i], labels[i])
# adjust_text(t)
# plt.xlim(min(loadings[0]) - 0.1, max(loadings[0]) + 0.1)
# plt.ylim(min(loadings[1]) - 0.1, max(loadings[1]) + 0.1)
xlimit_max = np.max([np.max(cscore[:, 0]*xscale), np.max(loadings[0])])
xlimit_min = np.min([np.min(cscore[:, 0]*xscale), np.min(loadings[0])])
ylimit_max = np.max([np.max(cscore[:, 1]*yscale), np.max(loadings[1])])
ylimit_min = np.min([np.min(cscore[:, 1]*yscale), np.min(loadings[1])])
# plt.xlim(min(loadings[firstToPlot]) - 0.1, max(loadings[firstToPlot]) + 0.1)
# plt.ylim(min(loadings[secondToPlot]) - 0.1, max(loadings[secondToPlot]) + 0.1)
xlimit_max = np.max([np.max(cscore[:, firstToPlot]*xscale), np.max(loadings[firstToPlot])])
xlimit_min = np.min([np.min(cscore[:, firstToPlot]*xscale), np.min(loadings[firstToPlot])])
ylimit_max = np.max([np.max(cscore[:, secondToPlot]*yscale), np.max(loadings[secondToPlot])])
ylimit_min = np.min([np.min(cscore[:, secondToPlot]*yscale), np.min(loadings[secondToPlot])])
plt.xlim(xlimit_min-0.2, xlimit_max+0.2)
plt.ylim(ylimit_min-0.2, ylimit_max+0.2)
general.axis_labels("PC1 ({}%)".format(var1), "PC2 ({}%)".format(var2), axlabelfontsize, axlabelfontname)
general.axis_labels("PC{} ({}%)".format(firstToPlot+1, var1), "PC{} ({}%)".format(secondToPlot+1, var2), axlabelfontsize, axlabelfontname)
general.get_figure(show, r, figtype, 'biplot_2d', theme)
# 3D
if var1 is not None and var2 is not None and var3 is not None:
xscale = 1.0 / (cscore[:, 0].max() - cscore[:, 0].min())
yscale = 1.0 / (cscore[:, 1].max() - cscore[:, 1].min())
zscale = 1.0 / (cscore[:, 2].max() - cscore[:, 2].min())
xscale = 1.0 / (cscore[:, firstToPlot].max() - cscore[:, firstToPlot].min())
yscale = 1.0 / (cscore[:, secondToPlot].max() - cscore[:, secondToPlot].min())
zscale = 1.0 / (cscore[:, thirdToPlot].max() - cscore[:, thirdToPlot].min())
fig = plt.figure(figsize=dim)
ax = fig.add_subplot(111, projection='3d')
if datapoints:
Expand All @@ -1895,37 +1898,37 @@ def biplot(cscore=None, loadings=None, labels=None, var1=None, var2=None, var3=N
color_result_num = [assign_values[i] for i in colorlist]
if colordot and isinstance(colordot, (tuple, list)):
colour_map = ListedColormap(colordot)
s = ax.scatter(cscore[:, 0]*xscale, cscore[:, 1]*yscale, cscore[:, 2]*zscale, c=color_result_num,
s = ax.scatter(cscore[:, firstToPlot]*xscale, cscore[:, secondToPlot]*yscale, cscore[:, thirdToPlot]*zscale, c=color_result_num,
cmap=colour_map, s=dotsize, alpha=valphadot, marker=markerdot)
plt.legend(handles=s.legend_elements()[0], labels=list(unique_class), loc=legendpos)
elif colordot and not isinstance(colordot, (tuple, list)):
s = ax.scatter(cscore[:, 0]*xscale, cscore[:, 1]*yscale, cscore[:, 2]*zscale, c=color_result_num,
s = ax.scatter(cscore[:, firstToPlot]*xscale, cscore[:, secondToPlot]*yscale, cscore[:, thirdToPlot]*zscale, c=color_result_num,
s=dotsize, alpha=valphadot, marker=markerdot)
plt.legend(handles=s.legend_elements()[0], labels=list(unique_class), loc=legendpos)
else:
ax.scatter(cscore[:, 0] * xscale, cscore[:, 1] * yscale, cscore[:, 2] * zscale, color=colordot,
ax.scatter(cscore[:, firstToPlot] * xscale, cscore[:, secondToPlot] * yscale, cscore[:, thirdToPlot] * zscale, color=colordot,
s=dotsize, alpha=valphadot, marker=markerdot)
for i in range(len(loadings[0])):
ax.quiver(0, 0, 0, loadings[0][i], loadings[1][i], loadings[2][i], color=arrowcolor, alpha=valphaarrow,
ax.quiver(0, 0, 0, loadings[firstToPlot][i], loadings[secondToPlot][i], loadings[thirdToPlot][i], color=arrowcolor, alpha=valphaarrow,
ls=arrowlinestyle, lw=arrowlinewidth)
ax.text(loadings[0][i], loadings[1][i], loadings[2][i], labels[i])

xlimit_max = np.max([np.max(cscore[:, 0] * xscale), np.max(loadings[0])])

xlimit_min = np.min([np.min(cscore[:, 0] * xscale), np.min(loadings[0])])
ylimit_max = np.max([np.max(cscore[:, 1] * yscale), np.max(loadings[1])])
ylimit_min = np.min([np.min(cscore[:, 1] * yscale), np.min(loadings[1])])
zlimit_max = np.max([np.max(cscore[:, 2] * zscale), np.max(loadings[2])])
zlimit_min = np.min([np.min(cscore[:, 2] * zscale), np.min(loadings[2])])
# ax.set_xlim(min(loadings[0])-0.1, max(loadings[0])+0.1)
# ax.set_ylim(min(loadings[1])-0.1, max(loadings[1])+0.1)
# ax.set_zlim(min(loadings[2])-0.1, max(loadings[2])+0.1)
ax.text(loadings[firstToPlot][i], loadings[secondToPlot][i], loadings[thirdToPlot][i], labels[i])

xlimit_max = np.max([np.max(cscore[:, firstToPlot] * xscale), np.max(loadings[firstToPlot])])

xlimit_min = np.min([np.min(cscore[:, firstToPlot] * xscale), np.min(loadings[firstToPlot])])
ylimit_max = np.max([np.max(cscore[:, secondToPlot] * yscale), np.max(loadings[secondToPlot])])
ylimit_min = np.min([np.min(cscore[:, secondToPlot] * yscale), np.min(loadings[secondToPlot])])
zlimit_max = np.max([np.max(cscore[:, thirdToPlot] * zscale), np.max(loadings[thirdToPlot])])
zlimit_min = np.min([np.min(cscore[:, thirdToPlot] * zscale), np.min(loadings[thirdToPlot])])
# ax.set_xlim(min(loadings[firstToPlot])-0.1, max(loadings[firstToPlot])+0.1)
# ax.set_ylim(min(loadings[secondToPlot])-0.1, max(loadings[secondToPlot])+0.1)
# ax.set_zlim(min(loadings[thirdToPlot])-0.1, max(loadings[thirdToPlot])+0.1)
ax.set_xlim(xlimit_min-0.2, xlimit_max+0.2)
ax.set_ylim(ylimit_min-0.2, ylimit_max+0.2)
ax.set_zlim(zlimit_min-0.2, zlimit_max+0.2)
ax.set_xlabel("PC1 ({}%)".format(var1), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_ylabel("PC2 ({}%)".format(var2), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_zlabel("PC3 ({}%)".format(var3), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_xlabel("PC{} ({}%)".format(firstToPlot+1, var1), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_ylabel("PC{} ({}%)".format(secondToPlot+1, var2), fontsize=axlabelfontsize, fontname=axlabelfontname)
ax.set_zlabel("PC{} ({}%)".format(thirdToPlot+1, var3), fontsize=axlabelfontsize, fontname=axlabelfontname)
general.get_figure(show, r, figtype, 'biplot_3d', theme)

def tsneplot(score=None, axlabelfontsize=9, axlabelfontname="Arial", figtype='png', r=300, show=False,
Expand Down Expand Up @@ -1958,4 +1961,3 @@ def tsneplot(score=None, axlabelfontsize=9, axlabelfontname="Arial", figtype='pn
plt.ylabel("t-SNE-2", fontsize=axlabelfontsize, fontname=axlabelfontname)
general.get_figure(show, r, figtype, figname, theme)