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

Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Stratified Sampling Method01:16

Stratified Sampling Method

11.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.9K

You might also read

Related Articles

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

Sort by
Same author

A Method for Improving Human Joint Moment Estimation during Lower Limb Rehabilitation Training Based on sEMG Signals.

IEEE transactions on bio-medical engineering·2026
Same author

Time series-based forecasting of infectious disease outbreak using information systems in public health.

Frontiers in public health·2026
Same author

Hypoxia‑induced exosomal CAMTA1 promotes radio‑resistance in MDA‑MB‑231 cells by regulating NRG1 to mediate M2 macrophage polarization.

International journal of oncology·2026
Same author

Role of LRRK2 in physiological activities, diseases, and therapy.

Chinese medical journal·2026
Same author

An Elite Haplotype of Nitrogen-Use-Efficiency Gene LHT5 Enhances Salt Tolerance in Rice.

Plant biotechnology journal·2026
Same author

Correction: BDNF augmentation reverses cranial radiation therapy-induced cognitive decline and neurodegenerative consequences.

Acta neuropathologica communications·2025

Related Experiment Video

Updated: Jun 22, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.5K

ULV: A robust statistical method for clustered data, with applications to multi-subject, single-cell omics data.

Mingyu Du1, Kevin Johnston2, Veronica Berrocal3

  • 1Center for Complex Biological Systems, University of California, Irvine, 92697, CA, USA.

Arxiv
|July 1, 2024
PubMed
Summary
This summary is machine-generated.

A new U-statistic based latent variable (ULV) method enhances single-cell data analysis by robustly handling small sample sizes and complex data issues. This approach improves the identification of key biological markers in genomics and proteomics studies.

More Related Videos

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics
09:09

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics

Published on: December 9, 2022

5.7K
Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations
10:23

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

11.0K

Related Experiment Videos

Last Updated: Jun 22, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.5K
Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics
09:09

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics

Published on: December 9, 2022

5.7K
Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations
10:23

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

11.0K

Area of Science:

  • Genomics and Proteomics
  • Computational Biology
  • Single-cell Analysis

Background:

  • Technological advancements enable high-resolution biological measurements, including single-cell profiling.
  • Analyzing single-cell data presents challenges like small sample sizes, non-normality, dropouts, and outliers.
  • Existing methods may struggle with the complexities of multi-omic single-cell datasets.

Purpose of the Study:

  • To introduce a novel computational method, U-statistic based latent variable (ULV), for analyzing complex single-cell data.
  • To address limitations of current methods in handling small sample sizes, non-normality, and other data challenges.
  • To provide a flexible and computationally feasible framework for both single-cell RNA and protein abundance data.

Main Methods:

  • Developed a U-statistic based latent variable (ULV) framework.
  • Leveraged the robustness of rank-based statistics and efficiency of parametric methods for small sample sizes.
  • Designed a computationally feasible method to simultaneously address data limitations like dropouts and outliers.

Main Results:

  • ULV controls false positives at desired significance levels.
  • Demonstrated effectiveness in single-cell proteomics (AML) and single-cell RNA (COVID-19) studies.
  • ULV identified differentially expressed proteins and genes missed by traditional methods, including those affected by covariates and less biased by high expression levels.

Conclusions:

  • ULV offers a robust and flexible approach for single-cell data analysis, outperforming existing methods.
  • The method successfully identified novel biological insights in both cancer and infectious disease studies.
  • ULV provides a valuable tool for advancing our understanding of biological mechanisms at the single-cell level.