CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal

被引:0
作者
Lyu, Zhen [1 ]
Dahal, Sabin [1 ]
Zeng, Shuai [1 ,2 ]
Wang, Juexin [3 ]
Xu, Dong [1 ,2 ,4 ]
Joshi, Trupti [1 ,2 ,4 ,5 ]
机构
[1] Univ Missouri, Dept Elect Engn & Comp Sci, Columbia, MO 65211 USA
[2] Univ Missouri, Christopher S Bond Life Sci Ctr, Columbia, MO 65211 USA
[3] Indiana Univ Indianapolis, Luddy Sch Informat Comp & Engn, Dept Biohlth Informat, Indianapolis, IN 46202 USA
[4] Univ Missouri, MU Inst Data Sci & Informat, Columbia, MO 65211 USA
[5] Univ Missouri, Dept Biomed Informat Biostat & Med Epidemiol, Columbia, MO 65211 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
single-cell data analysis; scRNA-seq; scATAC-seq; co-assay; deep learning; cross-modality prediction;
D O I
10.3390/genes15070882
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.
引用
收藏
页数:13
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