Skip to main content

Table 3 SPC Explained Variances

From: Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality

SPC

Percent Explained Variance

X1a

53.84%

X2 a

9.43%

X3 a

9.19%

X4

5.31%

X5

3.14%

X6 a

2.27%

X7 a

2.04%

X8

1.67%

X9 a

1.24%

X10

0.93%

  1. Explained variances for the sparse principal components. The 10 SPCs account for 89.05% of the original data’s variance. a denotes SPCs chosen for further analysis based on variable importance (see Fig. 4)
Back to article page

BMC Bioinformatics

ISSN: 1471-2105

Contact us