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1. DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype–phenotype prediction

   https://doi.org/10.1186/s13073-023-01248-6  

   Chandrashekar, Pramod Bharadwaj ; Alatkar, Sayali ; Wang, Jiebiao ; Hoffman, Gabriel E. ; He, Chenfeng ; Jin, Ting ; Khullar, Saniya ; Bendl, Jaroslav ; Fullard, John F. ; Roussos, Panos ; et al ( October 2023 , Genome Medicine)

   Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models.

   To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype–phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes.

   We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer’s disease).

   We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

    

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2. Explainable multi-task learning for multi-modality biological data analysis

   https://doi.org/10.1038/s41467-023-37477-x  

   Tang, Xin ; Zhang, Jiawei ; He, Yichun ; Zhang, Xinhe ; Lin, Zuwan ; Partarrieu, Sebastian ; Hanna, Emma Bou ; Ren, Zhaolin ; Shen, Hao ; Yang, Yuhong ; et al ( May 2023 , Nature Communications)

   Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities.

    

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3. Learning neural decoders without labels using multiple data streams

   https://doi.org/10.1088/1741-2552/ac857c  

   Peterson, Steven M. ; Rao, Rajesh P. N. ; Brunton, Bingni W. ( August 2022 , Journal of Neural Engineering)

   Objective.Recent advances in neural decoding have accelerated the development of brain–computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. Alternatively, self-supervised models that share self-generated pseudo-labels between two data streams have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding.Approach.We learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to train decoders that can classify movements from brain recordings. After training, we then isolate the decoders for each input data stream and compare the accuracy of decoders trained using cross-modal deep clustering against supervised and unimodal, self-supervised models.Main results.We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we extend cross-modal decoder training to three or more modalities, achieving state-of-the-art neural decoding accuracy that matches or slightly exceeds the performance of supervised models.Significance.We demonstrate that cross-modal, self-supervised decoding can be applied to train neural decoders when few or no labels are available and extend the cross-modal framework to share information among three or more data streams, further improving self-supervised training.

    

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4. Lived Experiences of African American Engineering Students at a PWI Through the Lens of Navigational Capital

   Damas, S. ; Benson, L. ( February 2022 , 2022 CoNECD (Collaborative Network for Engineering & Computing Diversity))

   There are significant disparities between the conferring of science, technology, engineering, and mathematics (STEM) bachelor’s degrees to minoritized groups and the number of STEM faculty that represent minoritized groups at four-year predominantly White institutions (PWIs). Studies show that as of 2019, African American faculty at PWIs have increased by only 2.3% in the last 20 years. This study explores the ways in which this imbalance affects minoritized students in engineering majors. Our research objective is to describe the ways in which African American students navigate their way to success in an engineering program at a PWI where the minoritized faculty representation is less than 10%. In this study, we define success as completion of an undergraduate degree and matriculation into a Ph.D. program. Research shows that African American students struggle with feeling like the “outsider within” in graduate programs and that the engineering culture can permeate from undergraduate to graduate programs. We address our research objective by conducting interviews using navigational capital as our theoretical framework, which can be defined as resilience, academic invulnerability, and skills. These three concepts come together to denote the journey of an individual as they achieve success in an environment not created with them in mind. Navigational capital has been applied in education contexts to study minoritized groups, and specifically in engineering education to study the persistence of students of color. Research on navigational capital often focuses on how participants acquire resources from others. There is a limited focus on the experience of the student as the individual agent exercising their own navigational capital. Drawing from and adapting the framework of navigational capital, this study provides rich descriptions of the lived experiences of African American students in an engineering program at a PWI as they navigated their way to academic success in a system that was not designed with them in mind. This pilot study took place at a research-intensive, land grant PWI in the southeastern United States. We recruited two students who identify as African American and are in the first year of their Ph.D. program in an engineering major. Our interview protocol was adapted from a related study about student motivation, identity, and sense of belonging in engineering. After transcribing interviews with these participants, we began our qualitative analysis with a priori coding, drawing from the framework of navigational capital, to identify the experiences, connections, involvement, and resources the participants tapped into as they maneuvered their way to success in an undergraduate engineering program at a PWI. To identify other aspects of the participants’ experiences that were not reflected in that framework, we also used open coding. The results showed that the participants tapped into their navigational capital when they used experiences, connections, involvement, and resources to be resilient, academically invulnerable, and skillful. They learned from experiences (theirs or others’), capitalized on their connections, positioned themselves through involvement, and used their resources to achieve success in their engineering program. The participants identified their experiences, connections, and involvement. For example, one participant who came from a blended family (African American and White) drew from the experiences she had with her blended family. Her experiences helped her to understand the cultures of Black and White people. She was able to turn that into a skill to connect with others at her PWI. The point at which she took her familial experiences to use as a skill to maneuver her way to success at a PWI was an example of her navigational capital. Another participant capitalized on his connections to develop academic invulnerability. He was able to build his connections by making meaningful relationships with his classmates. He knew the importance of having reliable people to be there for him when he encountered a topic he did not understand. He cultivated an environment through relationships with classmates that set him up to achieve academic invulnerability in his classes. The participants spoke least about how they used their resources. The few mentions of resources were not distinct enough to make any substantial connection to the factors that denote navigational capital. The participants spoke explicitly about the PWI culture in their engineering department. From open coding, we identified the theme that participants did not expect to have role models in their major that looked like them and went into their undergraduate experience with the understanding that they will be the distinct minority in their classes. They did not make notable mention of how a lack of minority faculty affected their success. Upon acceptance, they took on the challenge of being a racial minority in exchange for a well-recognized degree they felt would have more value compared to engineering programs at other universities. They identified ways they maneuvered around their expectation that they would not have representative role models through their use of navigational capital. Integrating knowledge from the framework of navigational capital and its existing applications in engineering and education allows us the opportunity to learn from African American students that have succeeded in engineering programs with low minority faculty representation. The future directions of this work are to outline strategies that could enhance the path of minoritized engineering students towards success and to lay a foundation for understanding the use of navigational capital by minoritized students in engineering at PWIs. Students at PWIs can benefit from understanding their own navigational capital to help them identify ways to successfully navigate educational institutions. Students’ awareness of their capacity to maintain high levels of achievement, their connections to networks that facilitate navigation, and their ability to draw from experiences to enhance resilience provide them with the agency to unleash the invisible factors of their potential to be innovators in their collegiate and work environments. 

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5. Integrated analysis of multimodal single-cell data with structural similarity

   https://doi.org/10.1093/nar/gkac781  

   Cao, Yingxin ; Fu, Laiyi ; Wu, Jie ; Peng, Qinke ; Nie, Qing ; Zhang, Jing ; Xie, Xiaohui ( September 2022 , Nucleic Acids Research)

   Multimodal single-cell sequencing technologies provide unprecedented information on cellular heterogeneity from multiple layers of genomic readouts. However, joint analysis of two modalities without properly handling the noise often leads to overfitting of one modality by the other and worse clustering results than vanilla single-modality analysis. How to efficiently utilize the extra information from single cell multi-omics to delineate cell states and identify meaningful signal remains as a significant computational challenge. In this work, we propose a deep learning framework, named SAILERX, for efficient, robust, and flexible analysis of multi-modal single-cell data. SAILERX consists of a variational autoencoder with invariant representation learning to correct technical noises from sequencing process, and a multimodal data alignment mechanism to integrate information from different modalities. Instead of performing hard alignment by projecting both modalities to a shared latent space, SAILERX encourages the local structures of two modalities measured by pairwise similarities to be similar. This strategy is more robust against overfitting of noises, which facilitates various downstream analysis such as clustering, imputation, and marker gene detection. Furthermore, the invariant representation learning part enables SAILERX to perform integrative analysis on both multi- and single-modal datasets, making it an applicable and scalable tool for more general scenarios.

    

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