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 Experiment Videos

Interactive exploration of microarray gene expression patterns in a reduced dimensional space.

Jatin Misra1, William Schmitt, Daehee Hwang

  • 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Genome Research
|July 5, 2002
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Multi-omics study to elucidate molecular mechanism of polyhexamethylene guanidine phosphate (PHMG-p)-induced pulmonary damage in mice.

Archives of toxicology·2026
Same author

Comprehensive proteogenomic characterization reveals clinically relevant molecular subtypes associated with medulloblastoma progression.

Experimental & molecular medicine·2026
Same author

cP1P Maintains Long-Term Pluripotency in Human Pluripotent Stem Cells.

International journal of stem cells·2026
Same author

Association between smoking cessation and depressive symptoms according to cessation duration, pack-years, and tobacco product type: a nationwide cross-sectional study in Korea.

Frontiers in public health·2026
Same author

Leveraging Quantitative Proteomics and Extracellular Vesicle Data to Uncover Druggable Receptor Kinases Across Cancers.

Journal of extracellular vesicles·2026
Same author

Chaperonin-mediated winter cold response via circadian clock components in Arabidopsis.

The New phytologist·2026
Same journal

Complete sequencing of medaka genomes reveals the architecture of centromeric satellites, giant mobile elements, and sex chromosomes.

Genome research·2026
Same journal

Convergence and conflict among telomere specialized transposons across 60 million years of Drosophilid evolution.

Genome research·2026
Same journal

A unified analysis of cell type- and trajectory-associated pathways in single-cell data using Phoenix.

Genome research·2026
Same journal

Resf1 is required for proper placental development and configuration of trophoblast cell-specific heterochromatin.

Genome research·2026
Same journal

Telomere-driven replicative crisis is driven by large-scale changes in genomic architecture.

Genome research·2026
Same journal

Spatially informed reference-free cell-type deconvolution for spatial transcriptomics with SpatialCD.

Genome research·2026
See all related articles

Principal Components Analysis (PCA) helps uncover gene expression patterns in high-dimensional data. This method identifies tissue-specific genes, enabling accurate sample classification and understanding of biological data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • High-dimensional gene expression data from DNA microarrays presents challenges in pattern detection and gene identification.
  • Existing methods struggle to link gene expression patterns directly to sample characteristics.

Purpose of the Study:

  • To demonstrate the utility of projection methods, specifically Principal Components Analysis (PCA), for analyzing gene expression data.
  • To establish a direct link between patterns in genes and patterns in biological samples (tissues).

Main Methods:

  • Application of Principal Components Analysis (PCA) to oligonucleotide microarray data from 40 normal human tissue samples.
  • Projection of gene expression data onto a 2D plane defined by major principal components.
  • Identification of class-specific (tissue-specific) genes based on PCA loadings and scores.

Related Experiment Videos

Main Results:

  • Distinct gene expression patterns were observed when genes were projected onto the PCA plane.
  • PCA facilitated the selection of discriminatory genes and identified tissue-specific expression signatures for liver, skeletal muscle, and brain.
  • A classification model was developed using tissue-specific genes, accurately classifying new samples.

Conclusions:

  • PCA is an effective tool for interactive exploration and data-driven learning of gene expression data.
  • The method successfully identifies tissue-specific genes and enables accurate sample classification.
  • PCA projection offers a valuable approach for gene selection and understanding biological sample types.