  The development of single-cell sequencing technology had promoted the
generation of a large amount of single-cell transcriptional profiles, providing
valuable opportunities to explore drug-resistant cell subpopulations in a
tumor. However, the drug sensitivity data in single-cell level is still scarce
to date, pressing an urgent and highly challenging task for computational
prediction of the drug sensitivity to individual cells. This paper proposed
scAdaDrug, a multi-source adaptive weighting model to predict single-cell drug
sensitivity. We used an autoencoder to extract domain-invariant features
related to drug sensitivity from multiple source domains by exploiting
adversarial domain adaptation. Especially, we introduced an adaptive weight
generator to produce importance-aware and mutual independent weights, which
could adaptively modulate the embedding of each sample in dimension-level for
both source and target domains. Extensive experimental results showed that our
model achieved state-of-the-art performance in predicting drug sensitivity on
sinle-cell datasets, as well as on cell line and patient datasets.
