Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data.

Nature communications·2026
Same author

Integrative transcriptome-wide association analyses reveal PRKCG-linked GABAergic dysfunction in Fragile X-associated tremor/ataxia syndrome.

Nature communications·2026
Same author

FastCCC: a permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies.

Nature communications·2025
Same author

Optimal transport modeling uncovers spatial domain dynamics in spatiotemporal transcriptomics studies.

bioRxiv : the preprint server for biology·2025
Same author

Improving estimation efficiency for survival data analysis by integrating a coarsened time-to-event outcome from an external study.

Biometrics·2025
Same author

Detecting anomalous anatomic regions in spatial transcriptomics with STANDS.

Nature communications·2024
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K

cypress: an R/Bioconductor package for cell-type-specific differential expression analysis power assessment.

Shilin Yu1, Guanqun Meng2, Wen Tang2

  • 1Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44106, United States.

Bioinformatics (Oxford, England)
|August 17, 2024
PubMed
Summary
This summary is machine-generated.

Researchers can now optimize experimental design for identifying cell-type-specific differentially expressed (csDE) genes using cypress. This tool provides statistical power analysis for bulk RNA-sequencing data, enhancing clinical applications.

More Related Videos

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus
12:10

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Published on: November 30, 2014

13.3K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

37.1K

Related Experiment Videos

Last Updated: Jun 16, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K
An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus
12:10

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Published on: November 30, 2014

13.3K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

37.1K

Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Advances in computational signal deconvolution allow bulk transcriptome analysis at a finer cell-type level.
  • Identifying cell-type-specific differentially expressed (csDE) genes is crucial for clinical applications but faces practical challenges in experimental design.
  • Existing methods lack dedicated tools for experimental design and statistical power analysis in csDE gene detection.

Purpose of the Study:

  • To introduce cypress, the first tool for experimental design and statistical power analysis specifically for csDE gene identification.
  • To provide researchers with a high-fidelity simulator for bulk RNA sequencing (RNA-seq) convolution and deconvolution processes.
  • To aid in optimizing experimental design and conducting power analyses for csDE gene studies.

Main Methods:

  • cypress models purified cell-type-specific (CTS) profiles, cell-type compositions, and biological/technical variations.
  • It functions as a simulator for bulk RNA-seq convolution and deconvolution.
  • The tool evaluates the impact of various influencing factors using statistical metrics.

Main Results:

  • cypress enables reliable modeling of complex biological and technical variations in transcriptome data.
  • It provides a robust simulation environment for assessing experimental designs.
  • The tool facilitates the evaluation of statistical power for detecting csDE genes under different conditions.

Conclusions:

  • cypress addresses critical needs in experimental design for csDE gene identification.
  • It empowers researchers to optimize their studies and improve the reliability of findings.
  • This tool enhances the practical application of deconvolution methods in clinical genomics.