Dataset Information

Consistent cross-modal identification of cortical neurons with coupled autoencoders.

ABSTRACT: Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. Although methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here we present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities.

SUBMITTER: Gala R

PROVIDER: S-EPMC8963134 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Consistent cross-modal identification of cortical neurons with coupled autoencoders.

Gala Rohan R Budzillo Agata A Baftizadeh Fahimeh F Miller Jeremy J Gouwens Nathan N Arkhipov Anton A Murphy Gabe G Tasic Bosiljka B Zeng Hongkui H Hawrylycz Michael M Sümbül Uygar U

Nature computational science 20210222 2

Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. Although methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here we present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical intern ...[more]

PMID: 35356158

Similar Datasets

Project description:BACKGROUND:Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources. RESULTS:We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The proposed model specifically leverages pathway information to effectively reduce the dimensionality of omics data into a pathway and patient specific score profile. In consequence, our method allows us to understand, which pathway is a feature of which particular patient cluster. Moreover, recently proposed machine learning techniques allow us to disentangle the specific impact of each individual omics feature on a pathway score. We applied our method to cluster patients in several cancer datasets using gene expression, miRNA expression, DNA methylation and CNVs, demonstrating the possibility to obtain biologically plausible disease subtypes characterized by specific molecular features. Comparison against several competing methods showed a competitive clustering performance. In addition, post-hoc analysis of somatic mutations and clinical data provided supporting evidence and interpretation of the identified clusters. CONCLUSIONS:Our suggested multi-modal sparse denoising autoencoder approach allows for an effective and interpretable integration of multi-omics data on pathway level while addressing the high dimensional character of omics data. Patient specific pathway score profiles derived from our model allow for a robust identification of disease subgroups.

Dataset Information

Consistent cross-modal identification of cortical neurons with coupled autoencoders.

Publications

Consistent cross-modal identification of cortical neurons with coupled autoencoders.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets