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.

Cancer is a disease that stems from the disruption of cellular state. Through genetic perturbations, tumor cells attain cellular states that give them proliferative advantage over the surrounding normal tissue [1]. The inherent variability of this process has hampered efforts to find highly effective common therapies, thereby ushering the need for precision medicine [2]. The scale of single-cell experiments is poised to revolutionize personalized medicine by effective characterization of the complete heterogeneity within a tumor for each individual patient [3, 4]. Recent expansion of single-cell sequencing technologies has exponentially increased the scale of knowledge attainable through a single biological experiment [5].

Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Here, researchers propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. Since its introduction in 2011 the variant call format (VCF) has been widely adopted for processing DNA and RNA variants in practically all population studies--as well as in somatic and germline mutation studies. Here the authors present a spectrum of over 125 useful, complimentary free and open source software tools and libraries, they wrote and made available through the multiple vcflib, bio-vcf, cyvcf2, hts-nim and slivar projects. These tools are applied for comparison, filtering, normalisation, smoothing and annotation of VCF, as well as output of statistics, visualisation, and transformations of files variants.

Scientists at the University of California, Irvine have developed a new deep-learning framework that predicts gene regulation at the single-cell level. In a study published recently in Science Advances, UCI researchers describe how their deep-learning technique can also be successfully used to observe gene regulation at the cellular level. Until now, that process had been limited to tissue-level analysis. According to co-author Xiaohui Xie, UCI professor of computer science, the framework enables the study of transcription factor binding at the cellular level, which was previously impossible due to the intrinsic noise and sparsity of single-cell data. A transcription factor (TF) is a protein that controls the translation of genetic information from DNA to RNA; TFs regulate genes to ensure they're expressed in proper sequence and at the right time in cells.