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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

707
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
707
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.4K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.4K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

11.5K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
11.5K
Proteomics01:33

Proteomics

7.5K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.5K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.8K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.8K

You might also read

Related Articles

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

Sort by
Same author

Early Prediction Model for Retinopathy of Prematurity Using Placental and Neonatal Risk Factors.

Investigative ophthalmology & visual science·2026
Same author

An observational diagnostic accuracy study comparing the urine dipstick with a consensus-based reference standard for the diagnosis of urinary tract infections in older adults.

BMC geriatrics·2026
Same author

Ex vivo T2*-weighted MRI and quantitative susceptibility mapping reflect spatial iron accumulation observed on histology in frontotemporal lobar degeneration.

Neurobiology of disease·2026
Same author

Biological and clinical characteristics of <i>ETV6</i>::<i>RUNX1</i>-like ALL.

HemaSphere·2026
Same author

UV-induced mutations accumulate during early clonal expansion in aneuploid subtypes of pediatric B-cell precursor acute lymphoblastic leukemia.

Haematologica·2026
Same author

[<sup>89</sup>Zr]bevacizumab PET/CT imaging of vestibular schwannomas for the prediction of bevacizumab treatment effect in patients with symptomatic <i>NF2</i>-related schwannomatosis: a study protocol for a phase II single centre, prospective, feasibility trial.

BMJ open·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Apr 27, 2026

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage
08:07

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage

Published on: September 16, 2025

1.6K

A test for comparing two groups of samples when analyzing multiple omics profiles.

Nimisha Chaturvedi1, Jelle J Goeman, Judith M Boer

  • 1Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. n.chaturvedi@vumc.nl.

BMC Bioinformatics
|July 10, 2014
PubMed
Summary
This summary is machine-generated.

We developed dSIM, a new method to compare gene expression and copy number associations between sample groups. This tool identifies differences in genomic regions, aiding in understanding complex biological data.

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.1K
Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
04:58

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

3.7K

Related Experiment Videos

Last Updated: Apr 27, 2026

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage
08:07

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage

Published on: September 16, 2025

1.6K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.1K
Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
04:58

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

3.7K

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Statistical models are crucial for integrated analysis of gene expression and copy number data.
  • Comparing association patterns across different sample groups is a key analytical challenge.

Purpose of the Study:

  • To introduce dSIM, a novel method for detecting differences in copy number-gene expression associations between two sample groups.
  • To enable robust comparison of association patterns using integrated genomic data.

Main Methods:

  • dSIM utilizes ridge regression to adjust for baseline copy number-gene expression associations.
  • A global test is applied to corrected data to identify differential association patterns.
  • The method is validated through simulation studies and application to real-world datasets.

Main Results:

  • dSIM effectively detects association differences, even in small genomic regions.
  • Application to breast cancer datasets identified chromosome arms with differing copy number-driven gene expression regulation between estrogen receptor-positive and negative samples.
  • Consistent findings across datasets suggest the method's reliability.

Conclusions:

  • dSIM offers a flexible and robust approach for comparative genomic association studies.
  • The method supports integration of diverse data types, including methylation and microRNA expression.
  • dSIM is implemented in R and will be available via the BioConductor package SIM.