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

Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

616
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
616
Introduction to R01:11

Introduction to R

4.4K
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
4.4K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.0K
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...
4.0K
Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Contingency Table01:29

Contingency Table

3.9K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
3.9K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

470
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
470

You might also read

Related Articles

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

Sort by
Same author

KSHV-infected endothelial cells expand and up-regulate angiogenic pathways and CXCR4 in patient-derived Kaposi sarcoma models.

Science translational medicine·2026
Same author

Antigen-presenting cancer-associated fibroblasts in murine pancreatic tumors differentially regulate T-cell phenotype and function.

Journal of immunology (Baltimore, Md. : 1950)·2026
Same author

Distinct fibroblast and perivascular senotypes define spatial niches that regulate fibrosis.

bioRxiv : the preprint server for biology·2026
Same author

Protocol for generation, time-course imaging, and automated quality control of 3D spheroid invasion using TRACEQC.

STAR protocols·2026
Same author

Asynchronous evolution of epithelium and stroma differentiates precursor lesions from pancreatic cancer.

Cancer discovery·2026
Same author

How is agentic AI changing how we do science?

Cell systems·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Apr 23, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.0K

switchBox: an R package for k-Top Scoring Pairs classifier development.

Bahman Afsari1, Elana J Fertig1, Donald Geman1

  • 1Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 and Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.

Bioinformatics (Oxford, England)
|September 29, 2014
PubMed
Summary
This summary is machine-generated.

The switchBox R package implements k-Top Scoring Pairs (kTSP), a robust, parameter-free classification method for high-throughput data. kTSP offers accuracy comparable to standard genomics techniques for biological predictions.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

869

Related Experiment Videos

Last Updated: Apr 23, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.0K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

869

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput data analysis requires robust classification methods.
  • Existing methods like Support Vector Machines (SVM) and Prediction Analysis for Microarrays (PAM) are widely used.
  • The Top Scoring Pair (TSP) classifier offers a parameter-free approach but can be limited in accuracy.

Purpose of the Study:

  • Introduce the 'switchBox' R package for k-Top Scoring Pairs (kTSP) classification.
  • Provide a user-friendly tool for applying kTSP to high-throughput biological data.
  • Enable accurate and interpretable predictions from complex biological datasets.

Main Methods:

  • k-Top Scoring Pairs (kTSP) classification based on paired measurements and order switching.
  • Utilizes ranking of a small subset of features for robustness and interpretability.
  • Implementation within the 'switchBox' R package available on Bioconductor.

Main Results:

  • kTSP demonstrates comparable accuracy to established genomics classification techniques (SVM, PAM).
  • The method is parameter-free, relying solely on feature ranking.
  • kTSP is robust to noise and offers potential for biological interpretation.

Conclusions:

  • The 'switchBox' package provides an effective implementation of the kTSP classifier.
  • kTSP is a valuable alternative for high-throughput data classification, balancing accuracy and interpretability.
  • The R package facilitates the application of kTSP in biological research.