MUON: multimodal omics analysis framework

Bredikhin, Danila; Kats, Ilia; Stegle, Oliver

doi:10.1186/s13059-021-02577-8

Cited by 75 publications

(56 citation statements)

References 59 publications

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“…After marking and removing doublets from our data, we repeated our preprocessing, dimensionality reduction, and clustering pipeline. After observing clear separation of distinct cell classes, we used MUON/v.0.1.2 (RRID:SCR_022804) [116] to calculate promoter accessibility scores by tabulating binarized UMI counts within the region 2,000 bp upstream of a transcriptional start site (TSS). Because at the time of this analysis MUON did not factor in DNA strand information, we ran the function ‘count_fragments_features‘ separately for + and – strand genes, using the “upstream_bp” or “downstream_ bp” arguments as necessary to tabulate counts in the correct upstream region (extending from the TSS to [TSS – 2,000 bp] or [TSS + 2,000 bp], respectively) (https://github.com/scverse/muon/issues/59).…”

Section: Methodsmentioning

confidence: 99%

A single-cell multi-omic atlas spanning the adult rhesus macaque brain

Chiou

Huang

Bohlen

et al. 2022

Preprint

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Cataloging the diverse cellular architecture of the primate brain is crucial for understanding cognition, behavior and disease in humans. Here, we generated a brain-wide single-cell multimodal molecular atlas of the rhesus macaque brain. Altogether, we profiled 2.58M transcriptomes and 1.59M epigenomes from single nuclei sampled from 30 regions across the adult brain. Cell composition differed extensively across the brain, revealing cellular signatures of region-specific functions. We also identified 1.19M candidate regulatory elements, many novel, allowing us to explore the landscape of cis-regulatory grammar and neurological disease risk in a cell-type- specific manner. Together, this multi-omic atlas provides an open resource for investigating the evolution of the human brain and identifying novel targets for disease interventions.

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Section: Methodsmentioning

confidence: 99%

A single-cell multi-omic atlas spanning the adult rhesus macaque brain

Chiou

Huang

Bohlen

et al. 2022

Preprint

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“…The single cell field is a particularly attractive guide for developing such an ecosystem. By creating standards for data structures, user interfaces, documentation pages and coding principles, tools such as Scanpy 43 , scVI 54 and muon 64 have greatly simplified single cell workflows. We mimicked the structure of many of the tools that sit within the overall single cell software universe by making EUGENe highly modular and wholly contained within the larger Python ecosystem (Pandas, NumPy, scikit-learn, etc.).…”

Section: Discussionmentioning

confidence: 99%

EUGENe: A Python toolkit for predictive analyses of regulatory sequences

Klie

Stites²,

Jores

et al. 2022

Preprint

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Deep learning (DL) has become a popular tool to study cis-regulatory element function. Yet efforts to design software for DL analyses in genomics that are Findable, Accessible, Interoperable and Reusable (FAIR) have fallen short of fully meeting these criteria. Here we present EUGENe (Elucidating the Utility of Genomic Elements with Neural Nets), a FAIR toolkit for the analysis of labeled sets of nucleotide sequences with DL. EUGENe consists of a set of modules that empower users to execute the key functionality of a DL workflow: 1) extracting, transforming and loading sequence data from many common file formats, 2) instantiating, initializing and training diverse model architectures, and 3) evaluating and interpreting model behavior. We designed EUGENe to be simple; users can develop and deploy workflows on new or existing datasets with functions that act on two customizable Python objects, annotated sequence data (SeqData) and PyTorch models (BaseModel). The modularity and simplicity of EUGENe also make it highly extensible and we illustrate these principles through application of the toolkit to three predictive modeling tasks. First, we train and compare a set of built-in models along with a custom architecture for the accurate prediction of activities of plant promoters from STARR-seq data. Next, we apply EUGENe to an RNA binding prediction task and showcase how seminal model architectures can be retrained in EUGENe or imported from Kipoi. Finally, we train models to classify transcription factor binding by wrapping functionality from Janngu, which can efficiently extract sequences in BED file format from the human genome. We emphasize that the code used in each use case is simple, readable, and well documented (https://eugene-tools.readthedocs.io/en/latest/index.html). We believe that EUGENe represents a springboard toward a collaborative ecosystem for DL applications in genomics research. EUGENe is available for download on GitHub (https://github.com/cartercompbio/EUGENe) along with several introductory tutorials and for installation on PyPi (https://pypi.org/project/eugene-tools/).

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“…One of the most used software packages for scRNA-seq analysis, Seurat, also has developed methodologies to integrate such modalities as well as antibody-derived tags from cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and spatial omics technologies such as Visium, by using weighted nearest neighbors (WNN) analysis. Multimodal omics analysis framework (MUON), which introduces the MuData format compatible across Python, R, and Julia programming languages, provides a shared interface for commonly used methodologies such as MOFA, WNN, and similarity network fusion (SNF) [112]. Similar framework packages include MultiMAP [113], linked inference of genomic experimental relationships (LIGER) [114], inteGrative anaLysis of mUlti-omics at single-cEll Resolution (GLUER) [115], clustering on network of samples (Conos) [116], and integrative non-negative matrix factorization (iNMF) [117], but some of the packages are more focused on specific spatial omics technologies or analysis of single-cell sequencing modalities.…”

Section: Multi-modal Analysis and Ml-aided Spatial Data Analysis Methodsmentioning

confidence: 99%

Spatial omics technologies at multimodal and single cell/subcellular level

et al. 2022

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Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of single cell to subcellular resolution spatial omics technologies and analysis tools to provide a comprehensive picture for both research and clinical applications.

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MUON: multimodal omics analysis framework

Cited by 75 publications

References 59 publications

A single-cell multi-omic atlas spanning the adult rhesus macaque brain

A single-cell multi-omic atlas spanning the adult rhesus macaque brain

EUGENe: A Python toolkit for predictive analyses of regulatory sequences

Spatial omics technologies at multimodal and single cell/subcellular level

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