Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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 microarray-based...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Clustering single-cell multi-omics data via weighted distance penalty and adaptive consistent graph regularization.

PLoS computational biology·2026

Same author

scVGAMF: a novel imputation method for scRNA-seq data by integrating linear and non-linear features.

Briefings in bioinformatics·2025

Same author

Joint Low Rank Representation with Symmetric Orthogonal Decomposition for Clustering of scRNA-seq Data.

Interdisciplinary sciences, computational life sciences·2025

Same author

Clustering Single-Cell RNA-Seq Data with Low-Rank Matrix Factorization and Local Graph Regularization.

Interdisciplinary sciences, computational life sciences·2025

Same author

Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty.

Briefings in bioinformatics·2024

Same author

scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data.

Interdisciplinary sciences, computational life sciences·2024

Same journal

OmicsTransformer: Self-Supervised Masked Consistency and Uncertainty-Aware Fusion for Robust Multi-Omics Prediction.

Bioinformatics (Oxford, England)·2026

Same journal

Computational Tool Choice Impacts CRISPR Spacer-Proto spacer Detection.

Bioinformatics (Oxford, England)·2026

Same journal

ARISE: RNA-Anchored Shared-Edge Topology and Hierarchical Fusion for Spatial Multi-Omics Integration.

Bioinformatics (Oxford, England)·2026

Same journal

Interactive exploration of biobank-scale ancestral recombination graphs with Lorax.

Bioinformatics (Oxford, England)·2026

Same journal

PepMCP: A Graph-Based Membrane Contact Probability Predictor for Membrane-Lytic Antimicrobial Peptides.

Bioinformatics (Oxford, England)·2026

Same journal

ARGscape: A modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 9, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

AcImpute: a constraint-enhancing smooth-based approach for imputing single-cell RNA sequencing data.

Wei Zhang¹, Tiantian Liu¹, Han Zhang¹

¹School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan 430205, China.

Bioinformatics (Oxford, England)

|March 4, 2025

Summary

This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) data imputation is vital. AcImpute, a new unsupervised method, improves accuracy by constraining smoothing weights, enhancing downstream analysis like clustering and trajectory inference.

More Related Videos

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

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Published on: January 19, 2024

Related Experiment Videos

Last Updated: May 9, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

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

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Published on: January 19, 2024

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Dropout events in scRNA-seq data compromise downstream analysis accuracy.
Imputation methods are essential for preprocessing scRNA-seq data.

Purpose of the Study:

To develop an unsupervised imputation method to address oversmoothing in scRNA-seq data.
To enhance the accuracy of gene expression imputation while preserving cellular variability.
To improve the performance of clustering and trajectory inference in scRNA-seq analysis.

Main Methods:

AcImpute, an unsupervised imputation method, constrains smoothing weights among cells.
The method adapts smoothing based on gene expression levels.
Performance was evaluated against nine other imputation methods.

Main Results:

AcImpute effectively restores gene expression levels in scRNA-seq data.
The method preserves inter-cell variability, mitigating oversmoothing.
AcImpute demonstrated improved performance in clustering and trajectory inference.

Conclusions:

AcImpute offers an effective solution for scRNA-seq data imputation.
The method enhances the accuracy and reliability of downstream analyses.
AcImpute provides a valuable tool for scRNA-seq data preprocessing.