Our first experiment aimed to demonstrate that AD-AE could successfully encode the biological signals we wanted while not detecting the selected confounders. For these different use cases, we showed that AD-AE generates deconfounded embeddings that successfully predict biological phenotypes of interest. However, expression profiles, especially when collected in large numbers, inherently contain variations introduced by technical artifacts (e.g. Furthermore, investigating the deconfounded latent spaces and reconstructed expression matrices learned by AD-AE using feature attribution methods such as ‘expected gradients’ (Erion et al., 2019; Sturmfels et al., 2020) would allow us to detect the biological differences between the confounded and deconfounded spaces and carry enrichment tests to understand the relevance to biological pathways. (2019), or (ii) an adversarial approach for batch removal, such as training an autoencoder with two separate decoder networks that correspond to two different batches along with an adversarial discriminator to differentiate the batches (Shaham, 2018) or generative adversarial networks trained to match distributions of samples from different batches (Upadhyay and Jain, 2019) or to align different manifolds (Amodio and Krishnaswamy, 2018). (2020), which investigated the effect of the number of latent dimensions using multiple metrics on a variety of dimensionality reduction techniques. endobj Without focusing on a specific phenotype prediction, these models enable us to learn patterns unconstrained by the limited phenotype labels we have. This result shows that AD-AE much more successfully generalizes to other domains. (Clustering Complexity on the Hypercube) Readers can go through the paper here. We also see as future work experimenting on single cell RNA-Seq data to learn informative embeddings combining multiple datasets. Unsupervised deep learning has enormous potential to extract important biological signals from the vast amount of expression profiles, as explored by recent studies (Dincer et al., 2018; Du et al., 2019; Tan et al., 2016). Our work takes its inspiration from research in fair machine learning, where the goal is to prevent models from unintentionally encoding information about sensitive variables, such as sex, race or age. LOCA is a special type of autoencoder, consisting of an encoder (E) parametrized by ρ and a decoder (D) parametrized by γ (see Section 5). ... weights that allows deep autoencoder networks to learn low-dimensional codes that work much In Figure 5ai, we colored all samples by their ER labels. The PC plot in Figure 2c highlights the distinct separation between the external dataset and the two training datasets. Step 1: The autoencoder model l is defined per Section 2.1. We show how this idea can be extended to networks of multipletransmitters and receivers and present the concept of radio transformer networks … For the intuitive understanding, autoencoder compresses (learns) the input and then reconstruct (generates) of it. A high generalization gap means that model performance declines sharply when transferred to another domain; a small generalization gap indicates a model can transfer across domains with minimal performance decline. The research of M.W. In this paper we use very deep autoencoders to map small color images to short binary codes. But the critical point is the separation of samples by ER label (Fig. 13 082 068 trainable parameters for the all genes model compared to 1 052 050 trainable parameters for the 1000 cluster centers model for KMPlot expression). Advances in profiling technologies are rapidly increasing the availability of expression datasets. ... paper, sparse parameter is empirically chosen as a number. To this end, we combine a convolutional encoder network with an expert-designed generative model that serves as decoder. The last layer had five hidden nodes corresponding to the number of confounder classes and softmax activation. It has equally many nodes both in the input and output layers, and training is carried out to recreate the input vector instead of assigning a target label to it. S3). To simulate this problem, we use a separate set of samples from a different GEO study from the KMPlot data. 5b). In this paper, we propose a method of dimension re-duction by manifold learning, which extends the tradition-al autoencoder to iteratively explore data relation and use the relation to pursue the manifold structure. (c) Subtype label distributions for male and female samples. 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