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

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...
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...
Modified Boxplots00:57

Modified Boxplots

A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
Quartile01:15

Quartile

Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
Quantitative Analysis01:12

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the method...
5-Number Summary01:04

5-Number Summary

In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...

You might also read

Related Articles

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

Sort by
Same author

SliceMap: a binary classification-driven 2D pipeline for detecting discriminative candidate regions in brain MRI.

Frontiers in neuroimaging·2026
Same author

Risk stratification of patients with TP53-mutated myeloproliferative neoplasms.

Leukemia·2026
Same author

RNA transcripts in salivary extracellular vesicle cargo isolated from aged populations.

Frontiers in aging·2026
Same author

Intraductal Papillary Mucinous Neoplasm Cellular Plasticity is Linked with Repeat Element Dysregulation.

bioRxiv : the preprint server for biology·2025
Same author

Bone Marrow Adipokine Mediates Hematopoietic Regeneration and Stem Cell Fitness.

bioRxiv : the preprint server for biology·2025
Same author

TORC: Target-Oriented Reference Construction for supervised cell-type identification in scRNA-seq.

Genome biology·2025
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Subset quantile normalization using negative control features.

Zhijin Wu1, Martin J Aryee

  • 1Center for Statistical Sciences and Department of Community Health, Brown University, Providence, Rhode Island 02912, USA. zhijin_wu@brown.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|October 27, 2010
PubMed
Summary
This summary is machine-generated.

Subset quantile normalization (SQN) offers a new approach for high-throughput biotechnology data. This method normalizes data using control probes, preserving biological variation and reducing noise without assumptions about biological signals.

More Related Videos

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT
07:33

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Published on: November 27, 2019

Related Experiment Videos

Last Updated: Jun 7, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT
07:33

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Published on: November 27, 2019

Area of Science:

  • Biotechnology
  • Bioinformatics
  • Genomics

Background:

  • Normalization is crucial for high-throughput biotechnologies like microarrays.
  • Existing methods rely on unverifiable assumptions about biological signals.
  • Newer platforms offer control probes across the signal range.

Purpose of the Study:

  • To introduce a novel normalization method, Subset Quantile Normalization (SQN).
  • To normalize data using non-specific control features, avoiding assumptions about biological signals.
  • To evaluate SQN's performance against existing normalization procedures.

Main Methods:

  • Developed Subset Quantile Normalization (SQN).
  • Utilized negative control probes for normalization basis.
  • Applied SQN to three different platforms and experimental settings.

Main Results:

  • SQN preserves more biological variation compared to other methods.
  • SQN effectively reduces noise observed on control features.
  • Demonstrated performance across diverse microarray experiments.

Conclusions:

  • SQN provides robust normalization without assumptions on biological signal behavior.
  • The method is applicable to various high-throughput technologies with control features.
  • SQN accommodates unequal feature counts and missing data.