Increasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in large numbers, inherently contain variations introduced by technical artifacts (e.g. batch effects) and uninteresting biological variables (e.g. age) in addition to the true signals of interest. These sources of variations, called confounders, produce embeddings that fail to transfer to different domains, i.e. an embedding learned from one dataset with a specific confounder distribution does not generalize to different distributions. To remedy this problem, we attempt to disentangle confounders from true signals to generate biologically informative embeddings.

中文翻译：

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用于学习稳健基因表达嵌入的对抗性解混自编码器

越来越多的基因表达谱使得可以使用复杂模型（例如深度无监督神经网络）从这些谱中提取潜在空间。然而，表达谱，尤其是在大量收集时，除了真正感兴趣的信号之外，还固有地包含由技术人工制品（例如批次效应）和无趣的生物变量（例如年龄）引入的变化。这些变化源，称为混杂因素，产生无法转移到不同领域的嵌入，即从具有特定混杂分布的一个数据集中学习的嵌入不能推广到不同的分布。为了解决这个问题，我们尝试从真实信号中分离出混杂因素，以生成具有生物学信息的嵌入。