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

Transcriptome network component analysis with limited microarray data.

Simon J Galbraith1, Linh M Tran, James C Liao

  • 1Department of Computer Science, University of California Los Angeles, CA, USA.

Bioinformatics (Oxford, England)
|June 13, 2006
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

FLT3L-secreting cDC1 in situ vaccination enhances antitumor immunity and synergizes with PD-1 blockade in murine non-small cell lung cancer.

Journal for immunotherapy of cancer·2026
Same author

The energetic costs of escaping predation in wild, schooling white mullet (Mugil curema).

The Journal of experimental biology·2026
Same author

Protocol for mapping tumor infiltration by murine T cells using multiplex immunofluorescence.

STAR protocols·2026
Same author

Archetype analysis of lung adenocarcinoma premalignancy links heterogeneity in premalignant lesions to diverging features of invasive disease.

Molecular cancer research : MCR·2026
Same author

Neoadjuvant BO-112 and Hypofractionated Radiation Therapy with or without Nivolumab in Soft-Tissue Sarcoma: Preclinical and Phase I Results.

Cancer discovery·2026
Same author

Archetype analysis of lung adenocarcinoma premalignancy links heterogeneity in premalignant lesions to diverging features of invasive disease.

bioRxiv : the preprint server for biology·2025
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

We improved Network Component Analysis (NCA) to estimate transcription factor (TF) activities from gene expression data, even with fewer samples than regulators. Our method accurately identifies TFAs, outperforming regression and revealing cell cycle-specific TF functions.

Area of Science:

  • Systems biology
  • Computational biology
  • Genomics

Background:

  • Network Component Analysis (NCA) infers transcription factor (TF) activities and regulatory strengths from gene expression and TF-gene networks.
  • Previous NCA versions were limited by the number of samples, restricting analysis to cases where sample size exceeded the number of regulators.
  • A need exists for NCA methods that can handle datasets with fewer samples than regulators, addressing inherent numerical challenges and multiple solutions.

Purpose of the Study:

  • To enhance NCA for robust transcription factor activity (TFA) estimation, particularly when the number of regulators surpasses the available data points.
  • To develop a new identifiability criterion enabling data decomposition in scenarios with more TFs than experiments.
  • To validate the improved NCA method using real biological data and compare its performance against existing regression techniques.

Related Experiment Videos

Main Methods:

  • Modified NCA based on the principle that most genes are regulated by a limited number of TFs.
  • Derived and implemented a novel identifiability criterion tested during numerical iteration for data decomposition.
  • Applied the enhanced NCA to Saccharomyces cerevisiae cell cycle microarray data (73 experiments) with a TF-gene connectivity network (96 TFs) from ChIP-chip data.

Main Results:

  • The improved NCA successfully decomposed data when the number of TFs (96) exceeded the number of experiments (73).
  • NCA-derived TFAs were qualitatively similar to regression-based TFAs but showed superior performance in statistical tests.
  • NCA identified subtle TFA signals correlating with known cell cycle TF functions and phases, with 31 TFs exhibiting statistically periodic TFAs and 12 showing periodicity in multiple experiments.

Conclusions:

  • The enhanced NCA method overcomes previous limitations, enabling accurate TFA estimation even with limited sample sizes relative to the number of regulators.
  • The method demonstrates biological utility by identifying known cell cycle regulators and subtle TFA dynamics in yeast.
  • NCA provides a powerful tool for dissecting gene regulatory networks and TF activities in complex biological systems.