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

12.3K
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...
12.3K
DNA Microarrays02:34

DNA Microarrays

18.2K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
18.2K
What is Gene Expression?01:42

What is Gene Expression?

169.6K
Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
169.6K
RNA-seq03:21

RNA-seq

10.3K
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...
10.3K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.0K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
14.0K

You might also read

Related Articles

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

Sort by
Same author

Nomenclature for Factors of the HLA System, 2026.

HLA·2026
Same author

Photodynamic Activation of a Novel Chlorophyll-Enriched Green Propolis Compound Triggers Apoptosis in Renal Cell Carcinoma.

International journal of molecular sciences·2025
Same author

Novel compounds of Taiwanese green propolis induce apoptosis of human glioblastoma cells by daylight photodynamic action.

Future science OA·2025
Same author

Simultaneous, real-time tracking of many neuromodulatory signals with Multiplexed Optical Recording of Sensors on a micro-Endoscope.

bioRxiv : the preprint server for biology·2025
Same author

Mendelian genetics and eugenics.

American journal of human genetics·2025
Same author

Stochastic neuropeptide signals compete to calibrate the rate of satiation.

Nature·2024

Related Experiment Video

Updated: Aug 23, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

GMMchi: gene expression clustering using Gaussian mixture modeling.

Ta-Chun Liu1, Peter N Kalugin2,3, Jennifer L Wilding2

  • 1Cancer and Immunogenetics Laboratory, Weatherall Institute of Molecular Medicine, Department of Oncology, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK. jeffliu6068@gmail.com.

BMC Bioinformatics
|November 3, 2022
PubMed
Summary
This summary is machine-generated.

GMMchi, a new Python package, identifies bimodal gene expression patterns in cancer, aiding the discovery of driver mutations and tumor characteristics. It offers robust performance and novel insights into cancer evolution.

Keywords:
BimodalCancerChisqaureColorectalGobletRNA Message

More Related Videos

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
09:40

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

5.7K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

Related Experiment Videos

Last Updated: Aug 23, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
09:40

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

5.7K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Cancer evolves through genetic and epigenetic changes, altering gene expression and cell phenotypes.
  • Driver mutations confer selective advantages, influencing cancer gene expression and phenotypes.
  • Bimodal gene expression patterns may correlate with driver mutations.

Purpose of the Study:

  • To introduce GMMchi, a Python package for detecting and characterizing bimodal gene expression patterns in cancer.
  • To analyze correlations between bimodal gene expression shifts and driver mutations.
  • To provide a tool for enhanced analysis of bulk gene expression data.

Main Methods:

  • Utilized Gaussian Mixture Modeling (GMM) to identify bimodal gene expression patterns.
  • Employed 2x2 contingency table statistics for correlation analysis.
  • Validated GMMchi using simulated data and applied it to microarray and RNA-Seq data.

Main Results:

  • GMMchi demonstrated robust performance with 85% accuracy on simulated data (n=90).
  • The package can characterize background signals, filter probes, and uncover genetic interrelationships.
  • Successfully extracted bimodal patterns from colorectal cancer (CRC) cell line and tumor data.

Conclusions:

  • GMMchi reliably detects bimodal gene expression patterns in cancer datasets.
  • The tool verifies known gene expression correlates of CRC phenotypes.
  • Offers a valuable addition to traditional continuous-valued statistical analysis in cancer research.