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Table 3 Comparison of chemotherapy response prediction performance (AUROC) for XGBoost models trained with original transcriptome data (“Raw data”) or transcriptome data encoded with PCA, ICA, or VAE. This analysis was carried out across five cancers (BRCA, COAD, BLCA, PAAD, and SARC)

From: Predicting chemotherapy response using a variational autoencoder approach

Cancer type

AUROC (mean)

p (Welch’s t-test)

VAE

Raw data

PCA

ICA

VAE versus Raw data

VAE versus PCA

VAE versus ICA

BRCA

0.674

0.649

0.614

0.609

\(8.07 \times 10^{-4}\)

\(3.80 \times 10^{-12}\)

\(3.39 \times 10^{-14}\)

PAAD

0.738

0.694

0.710

0.685

\(6.99 \times 10^{-6}\)

\(5.04 \times 10^{-3}\)

\(2.41 \times 10^{-6}\)

COAD

0.707

0.674

0.726

0.689

\(1.19 \times 10^{-3}\)

\(2.81 \times 10^{-2}\)

\(5.65 \times 10^{-2}\)

BLCA

0.659

0.626

0.593

0.650

\(7.81 \times 10^{-5}\)

\(4.27 \times 10^{-9}\)

\(2.61 \times 10^{-1}\)

SARC

0.704

0.679

0.682

0.701

\(3.49 \times 10^{-2}\)

\(2.91 \times 10^{-2}\)

\(8.64 \times 10^{-1}\)

  1. The p values are for row-wise difference of means tests for the indicated pairs of sample groups (columns). For each cancer type (row), the highest mean AUROC performance is shown in boldface