Related Experiment Video
Updated: Jun 12, 2026
10:50
Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
Published on: February 25, 2017
A comparison of probe-level and probeset models for small-sample gene expression data.
John R Stevens1, Jason L Bell, Kenneth I Aston
1Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA. john.r.stevens@usu.edu
BMC Bioinformatics
|May 28, 2010
Summary
Identifying differentially expressed genes in small sample microarray studies is challenging. A novel nested factorial model (affyNFM) offers a competitive statistical tool for such analyses, implemented in freely available R code.
Area of Science:
- Bioinformatics
- Statistical Genetics
- Genomics
Background:
- Traditional gene expression analysis methods require large sample sizes, limiting their application in certain research scenarios.
- Microarray studies often face constraints on sample availability, necessitating specialized statistical approaches.
Purpose of the Study:
- To evaluate existing statistical models for identifying differentially expressed genes in small sample sizes.
- To develop and validate a novel statistical model suitable for small-sample gene expression data analysis.
Main Methods:
- Assessed performance of probe-level and probeset models using graphical and numerical analysis on spike-in datasets.
- Developed a novel nested factorial model based on the Affymetrix GeneChip platform.
- Implemented the nested factorial model in R code (affyNFM).
Main Results:
- The nested factorial model demonstrated competitive performance in small-sample spike-in experiments.
- Statistical methods utilizing estimated log fold change exhibited consistent performance for small-sample gene expression data.
- The affyNFM R package provides a practical implementation of the proposed model.
Conclusions:
- The nested factorial model is a valuable statistical tool for small-sample gene expression studies.
- Test statistics related to log fold change are recommended for consistent performance in small-sample analyses.
- Freely available R code (affyNFM) facilitates the application of this novel statistical approach.
Related Concept Videos
02:34DNA Microarrays
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
02:26Comparing Copy Number Variations and SNPs
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...