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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
What Are Outliers?01:12

What Are Outliers?

Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...

You might also read

Related Articles

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

Sort by
Same author

Glycemic response trajectories on metformin monotherapy in real-world diabetes care.

medRxiv : the preprint server for health sciences·2026
Same author

Robust ranking of renewable energy alternatives handling uncertainty using novel hesitant bi-fuzzy MEREC-MOORA and Dombi aggregation approach.

Scientific reports·2026
Same author

The Impact of Social Vulnerability on Exercise Outcomes: A Longitudinal Study of Physical Function in Older People With HIV.

Journal of the International Association of Providers of AIDS Care·2026
Same author

Special issue: cell and gene causal inference in the design and analysis of gene therapy clinical trials.

Journal of biopharmaceutical statistics·2026
Same author

Mapping the last mile: Micro-stratification for sustained visceral leishmaniasis elimination in Bangladesh.

PLoS neglected tropical diseases·2026
Same author

The effects of high-intensity interval training versus continuous moderate-intensity exercise on body composition among older adults with HIV.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: May 14, 2026

Competitive Genomic Screens of Barcoded Yeast Libraries
11:59

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Genomic outlier detection in high-throughput data analysis.

Debashis Ghosh1

  • 1Departments of Statistics and Public Health Sciences, Penn State University, DuBios, PA, USA. ghoshd@psu.edu

Methods in Molecular Biology (Clifton, N.J.)
|February 7, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces oncogene outlier detection for high-throughput data analysis. It presents new statistical methods to identify potential oncogenes by finding unusual gene expression patterns, aiding cancer research.

More Related Videos

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor
09:33

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Published on: August 25, 2023

Related Experiment Videos

Last Updated: May 14, 2026

Competitive Genomic Screens of Barcoded Yeast Libraries
11:59

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor
09:33

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Published on: August 25, 2023

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • High-throughput data analysis frequently aims to detect differential gene expression between groups.
  • Identifying novel oncogenes is crucial for understanding cancer development, as demonstrated by recent prostate cancer research.
  • Differential expression patterns can reveal candidate oncogenes.

Purpose of the Study:

  • To address the statistical challenge of oncogene outlier detection in high-throughput data.
  • To propose and evaluate statistical models and procedures for identifying outlier genes indicative of oncogenes.
  • To establish links between oncogene outlier detection and multiple testing concepts.

Main Methods:

  • Development of a statistical model for the multiclass situation.
  • Introduction of new nonparametric procedures for outlier detection.
  • Comparison of proposed methods with existing techniques via simulation studies.

Main Results:

  • The study details a statistical framework for identifying oncogene outliers.
  • New nonparametric methods are presented and evaluated.
  • Simulation studies compare the performance of novel and existing approaches.

Conclusions:

  • Oncogene outlier detection is a key statistical problem in analyzing high-throughput data.
  • The proposed statistical models and nonparametric procedures offer valuable tools for identifying candidate oncogenes.
  • This approach can advance the discovery of new oncogenes in various cancers.