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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: Jul 24, 2025

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention.

Oscar A Davalos¹, A Ali Heydari^2,3, Elana J Fertig⁴

¹Quantitative and Systems Biology Graduate Program, University of California, Merced, CA, USA.

Biorxiv : the Preprint Server for Biology

|July 3, 2023

Summary

This summary is machine-generated.

scANNA is a new interpretable deep learning model for single-cell RNA sequencing (scRNAseq) analysis. It uses gene importance learned from neural attention for downstream tasks, improving efficiency and results.

Keywords:

Deep Learning Interpretable Deep Learning Neural Attention Single Cell RNA Sequencing Analysis

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Last Updated: Jul 24, 2025

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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Published on: May 22, 2017

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Computational Biology
Genomics
Machine Learning

Background:

Current deep learning (DL) models for single-cell RNA sequencing (scRNAseq) analysis lack interpretability and require task-specific training.
Existing scRNAseq analysis pipelines are often disjointed, necessitating separate models for different analytical stages.

Approach:

We introduce scANNA, an interpretable DL model for scRNAseq data that utilizes neural attention mechanisms.
scANNA learns gene associations and importance during training, enabling direct application to downstream analyses without retraining.

Key Points:

The interpretability of scANNA allows for the identification of gene importance, facilitating tasks like marker selection and cell-type classification.
scANNA achieves performance comparable to or exceeding state-of-the-art methods on standard scRNAseq tasks without task-specific training.
The model enhances scRNAseq analysis by reducing the need for extensive prior knowledge and eliminating the requirement for multiple specialized models.

Conclusions:

scANNA offers a unified and interpretable framework for scRNAseq analysis, streamlining research workflows.
This approach empowers researchers to gain meaningful insights efficiently, saving time and computational resources.