Example: Principal Component AnalysisΒΆ
Principal Component Analysis (PCA) is a popular dimensionality reduction method. Because outlier samples can strongly affect the results of PCA, singlet also implements a robust PCA version via Principal Component Pursuit (cite).
from singlet.dataset import Dataset
ds = Dataset(
samplesheet='example_sheet_tsv',
counts_table='example_table_tsv')
ds.counts.normalize('counts_per_million', inplace=True)
ds.counts = ds.counts.iloc[:200]
print('Calculate PCA')
vs = ds.dimensionality.pca(
n_dims=2,
transform='log10',
robust=False)['vs']
print('Plot PCA')
ax = ds.plot.scatter_reduced_samples(
vs,
color_by='ACTB')
plt.show()
You should get figures similar to the following ones: