Statistical Analysis of MPSS Measurements: Application to the Study of LPS-Activated Macrophage Gene Expression

Massively Parallel Signature Sequencing (MPSS), a recently developed high-throughput transcription profiling technology, has the ability to profile almost every transcript in a sample without requiring prior knowledge of the sequence of the transcribed genes. As is the case with DNA microarrays, eff...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 102; no. 5; pp. 1402 - 1407
Main Authors Stolovitzky, G. A., Kundaje, A., Held, G. A., Duggar, K. H., Haudenschild, C. D., Zhou, D., Vasicek, T. J., Smith, K. D., Aderem, A., Roach, J. C., Cantor, Charles R.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 01.02.2005
National Acad Sciences
Subjects
Base Sequence
Biological Sciences
Breast Neoplasms
Cell Line, Tumor
Cells, Cultured
Cluster Analysis
DNA, Complementary - chemistry
Female
Gene expression
Gene Expression Regulation - physiology
Genes
Genetics
Humans
Libraries
Lipopolysaccharides - pharmacology
Macrophage Activation - drug effects
Macrophage Activation - physiology
Macrophages
Macrophages - drug effects
Macrophages - physiology
Models, Genetic
Noise measurement
Null hypothesis
Oligonucleotide Array Sequence Analysis - methods
P values
Reproducibility of Results
Sequencing
Signal noise
Signatures
Statistical analysis
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Summary:Massively Parallel Signature Sequencing (MPSS), a recently developed high-throughput transcription profiling technology, has the ability to profile almost every transcript in a sample without requiring prior knowledge of the sequence of the transcribed genes. As is the case with DNA microarrays, effective data analysis depends crucially on understanding how noise affects measurements. We analyze the sources of noise in MPSS and present a quantitative model describing the variability between replicate MPSS assays. We use this model to construct statistical hypotheses that test whether an observed change in gene expression in a pair-wise comparison is significant. This analysis is then extended to the determination of the significance of changes in expression levels measured over the course of a time series of measurements. We apply these analytic techniques to the study of a time series of MPSS gene expression measurements on LPS-stimulated macrophages. To evaluate our statistical significance metrics, we compare our results with published data on macrophage activation measured by using Affymetrix GeneChips.
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This paper was submitted directly (Track II) to the PNAS office.
Edited by Charles R. Cantor, Sequenom, Inc., San Diego, CA
Abbreviations: FS, four stepper; TS, two stepper; MPSS, Massively Parallel Signature Sequencing; tpm, transcripts per million; SAGE, serial analysis of gene expression; SI, significance index.
To whom correspondence may be addressed. E-mail: gustavo@us.ibm.com or jroach@systemsbiology.org.
G.A.S. and A.K. contributed equally to this work.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0406555102