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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: Jan 13, 2026

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Single-cell multiomics data integration and generation with scPairing.

Jeffrey Niu¹, Carlos Vasquez-Rios¹, Jiarui Ding¹

¹Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.

Cell Reports Methods

|October 28, 2025

Summary

This summary is machine-generated.

scPairing, a deep learning model, generates novel multiomics data by embedding single-cell modalities into a shared space. This approach overcomes data limitations, enabling new biological discoveries from gene expression and chromatin accessibility.

Keywords:

CITE-seq CP: computational biology CP: systems biology contrastive learning deep generative models scATAC-seq scRNA-seq single-cell multiomics variational autoencoders

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Last Updated: Jan 13, 2026

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Computational Biology
Genomics
Single-cell Analysis

Background:

Single-cell multiomics technologies offer paired measurements of cellular modalities like gene expression and chromatin accessibility.
High costs limit the availability of multiomics datasets compared to unimodal data.

Purpose of the Study:

To introduce scPairing, a deep learning model for generating novel multiomics data.
To address the scarcity of multiomics datasets by leveraging unimodal data.

Main Methods:

scPairing employs a deep learning architecture inspired by contrastive language-image pre-training (CLIP).
It embeds diverse cellular modalities from single cells into a common embedding space.
Bridge integration is utilized to generate new multiomics data from unimodal datasets.

Main Results:

scPairing successfully constructs an embedding space that captures both coarse and fine biological structures.
The model generated novel multiomics data for retina, immune, and renal cells.
scPairing was extended to successfully generate trimodal data.

Conclusions:

Generated multiomics datasets can accelerate the discovery of novel cross-modality relationships.
The scPairing model facilitates the validation of existing biological hypotheses using synthetic multiomics data.