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

Statgraphics01:10

Statgraphics

Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
Statistical Package for the Social Sciences (SPSS)01:22

Statistical Package for the Social Sciences (SPSS)

The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...

You might also read

Related Articles

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

Sort by
Same author

Mast cells promote pathology and susceptibility in tuberculosis.

eLife·2026
Same author

Targeting the lung innate pathways during tuberculosis can improve vaccine-induced protection via Th17 responses in diversity outbred mice.

mBio·2026
Same author

Therapeutic remodeling of the tuberculosis granuloma with 1-methyl-D-tryptophan enhances CD8<sup>+</sup> T cell-macrophage interactions.

bioRxiv : the preprint server for biology·2025
Same author

Concurrent TB and HIV therapies control TB reactivation during co-infection but not chronic immune activation.

Nature communications·2025
Same author

Type I interferons in tuberculosis pathogenesis and prevention.

Trends in microbiology·2025
Same author

Single dose alum adjuvanted RBD protein vaccine provides protection against homologous challenge with SARS-CoV-2 Washington strain and heterologous rechallenge with Delta and Omicron BA.5 variants in K18 hACE2 mouse model.

bioRxiv : the preprint server for biology·2025
Same journal

Protein Sequence Analysis Using the MPI Bioinformatics Toolkit.

Current protocols in bioinformatics·2020
Same journal

Exploring Manually Curated Annotations of Intrinsically Disordered Proteins with DisProt.

Current protocols in bioinformatics·2020
Same journal

Network Building with the Cytoscape BioGateway App Explained in Five Use Cases.

Current protocols in bioinformatics·2020
Same journal

Expanding the Perseus Software for Omics Data Analysis With Custom Plugins.

Current protocols in bioinformatics·2020
Same journal

Exploring Non-Coding RNAs in RNAcentral.

Current protocols in bioinformatics·2020
Same journal

How to Illuminate the Dark Proteome Using the Multi-omic OpenProt Resource.

Current protocols in bioinformatics·2020
See all related articles

Related Experiment Video

Updated: Jul 5, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Analyzing and visualizing expression data with Spotfire.

Deepak Kaushal1, Clayton W Naeve

  • 1St Jude Children's Research Hospital, Memphis, Tennessee, USA.

Current Protocols in Bioinformatics
|April 23, 2008
PubMed
Summary
This summary is machine-generated.

This guide details microarray data analysis in Spotfire, covering gene expression, clustering, and data transformation techniques. Learn to identify differentially expressed genes and visualize results effectively.

More Related Videos

Analysis of Histone Antibody Specificity with Peptide Microarrays
09:47

Analysis of Histone Antibody Specificity with Peptide Microarrays

Published on: August 1, 2017

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Related Experiment Videos

Last Updated: Jul 5, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Analysis of Histone Antibody Specificity with Peptide Microarrays
09:47

Analysis of Histone Antibody Specificity with Peptide Microarrays

Published on: August 1, 2017

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Microarray data analysis requires specialized bioinformatics tools and techniques.
  • Spotfire is a powerful platform for data visualization and analysis.
  • Effective analysis of gene expression data is crucial for biological discovery.

Purpose of the Study:

  • To provide a comprehensive guide for analyzing microarray data using Spotfire.
  • To demonstrate various statistical and computational methods for gene expression analysis.
  • To enable users to effectively interpret and visualize complex biological datasets.

Main Methods:

  • Differential gene expression analysis using t-test/ANOVA and distinction calculation.
  • Gene expression profiling and similarity searching.
  • Application of clustering algorithms: hierarchical clustering, K-means, and principal components analysis.
  • Coincidence testing for comparing clustering results.
  • Data transformation and internet-based data querying within Spotfire.

Main Results:

  • Identification of differentially expressed genes.
  • Successful implementation of various clustering methods for pattern discovery.
  • Ability to compare and validate results from different analytical approaches.
  • Generation of new data columns through mathematical transformations.
  • Export of visualizations for reporting and further investigation.

Conclusions:

  • Spotfire offers a versatile environment for comprehensive microarray data analysis.
  • The presented protocols enable robust identification of biological insights from gene expression data.
  • Users can effectively leverage Spotfire for advanced bioinformatics tasks, from data exploration to result interpretation.