Cited by 1,703Gene expression and cancer classificationBioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

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STEM: a tool for the analysis of short time series gene expression data

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Ernst, Jason, Bar-Joseph, Ziv·2006·BMC Bioinformatics

DOI 10.1186/1471-2105-7-191PMC PMC1456994

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Abstract

Background: Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results: We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion: The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at http://www.cs.cmu.edu/~jernst/stem .

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Figure 1 Distribution of microarray experiments by type. Summary of the 786 microarray datasets for human, mouse, rat, and yeast in the Gene Expression Omnibus as of August 2005. As can be seen, 27.5% of the sets are time series experiments with 3–8 time points. All of these sets were labeled as either time, development, or age in the database. An additional 1% percent contains other types of sequential experiments including dose or temperature response, with 3–8 different levels.CC BY · PubMed Central

Figure 2 STEM input interface. The image shows the STEM input interface, which is divided into four sections. In the top section a user specifies the gene expression data and normalization options. In the second section a user specifies the gene annotation source, in this case the annotations are selected to be Human annotations from the European Bioinformatics Institute. In the third section a user specifies to either use the STEM clustering method or k-means, and can also change various parameter settings. The fourth section of the interface contains the execute button.CC BY · PubMed Central

Figure 3 Example model profiles overview interface. The example data is drawn from an experiment measuring the response of gastric epithelial cells infected with the vacA-mutant strain of the pathogen Helicobacter pylori [3]. The data was sampled at five time points 0 h, .5 h, 3 h, 6 h, and 12 h. The data set was filtered to contain only the 2989 genes with no missing data (though STEM can handle missing data without filtering, see Additional file 1) that exhibited a .8 log base two fold increase or decrease for at least one time point. The number in the top left-hand corner of a profile box is the profile ID number. The colored profiles had a statistically significant number of genes assigned. Non-white profiles of the same color represent profiles grouped into a single cluster. By clicking on one of the buttons along the bottom of the window, a dialog window appears by which the profiles can be reordered by various criteria. Another button displays a table of all genes passing filter and the profile to which they were assigned. Clicking on a profile box brings up detailed information about the profile (Figure 5).CC BY · PubMed Central

Figure 4 Model profiles reordered interface. The profiles from Figure 3 are reordered based on actual size based p-value enrichment for genes being annotated as belonging to the GO category DNA metabolism. For each profile the number of DNA metabolism genes assigned to it and the enrichment p-value appears in the lower left corner of the profile box.CC BY · PubMed Central

Figure 5 Four model profile detailed information windows. The above image are screenshots of four detailed model profile windows for the data of Figure 3. The four profiles shown were those which had the most genes assigned. All four profiles were significant, and the two profiles on the left were clustered together. Along the top of each window are statistics on the number of genes assigned to the profile, the number of genes expected, and the enrichment p-value. Along the bottom of the window is a button to display a table of genes assigned to the profile and a button to display a GO enrichment table for the profile (Figure 6). Additional buttons can be used to extract the cluster information for this profile.CC BY · PubMed Central

Figure 6 Gene Ontology enrichment analysis table. The image shows an example of a GO enrichment analysis table. This example table contains the GO enrichment results for the set of genes shown in the upper right of Figure 5 where the enrichment is computed based on actual size enrichment. The first two columns of the table are the GO category ID and name. The third column contains the total number of genes of each GO category on the microarray. The fourth column contains for each GO category total number of genes on the microarray that were also assigned to the profile. The fifth column contains the number of genes of that GO category that were expected to be assigned to the profile, in this case computed based on the profiles actual size. The sixth column contains how many more genes were assigned than expected. The seventh and eight columns contain the p-value and corrected p-values for the enrichment. Clicking on a row of the table brings up the list of genes of that GO category that were also assigned to the profile.CC BY · PubMed Central

Figure 7 Model profiles comparison interface. The window above shows a portion of the comparison interface comparing the wildtype and vacA- experiments from [3]. A profile to the immediate left of a yellow bar in this image is from the vacA- experiment. A profile to the right of the yellow bar is from the wildtype experiment, and has a significant intersection (in terms of the genes assigned to them) with the profile to the left of the yellow bar in its row. The profile pairs are currently arranged based on the p-value of their intersection, with the temporal profile pairs that are most significant appearing to the top and left. The profile pairs can also be arranged based on their correlation or IDs.CC BY · PubMed Central

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Authors: Ernst, J., & Bar-Joseph, Z.
Affiliations: Carnegie Mellon University (US)
Corresponding author: Ernst, Jason
ORCID: Ernst, Jason
Venue: BMC Bioinformatics
Publisher: Springer Science and Business Media LLC
Published: 2006-04-05
Volume: 7
Issue: 1
Pages: 191-191
ISSN: 1471-2105 (ISSN-L)
DOI: 10.1186/1471-2105-7-191
PMC: PMC1456994
OpenAlex: W2101142880
Cited by: 1,703
References: 27
Citations by year: 2026: 109 · 2025: 116 · 2024: 185 · 2023: 159 · 2022: 220 · 2021: 158 · 2020: 155 · 2019: 114 · 2018: 78 · 2017: 64 · 2016: 71 · 2015: 57 · 2014: 36 · 2013: 30 · 2012: 30
FWCI: 9.64
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: BMC Bioinformatics (publishedVersion, cc-by)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
eScholarship (California Digital Library) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
eScholarship (California Digital Library) (submittedVersion)
Funders: National Science Foundation (0448453, CAREER)
National Institutes of Health
National Institute of Allergy and Infectious Diseases (N01 AI-5001, Z01AI005001)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: time series microarray data, gene expression clustering, time series comparison, gene expression visualization, gene ontology annotation
MeSH terms: Computer Simulation, Gene Expression Regulation, Models, Biological, Pattern Recognition, Automated, Software, Time Factors, Transcription Factors, Signal Transduction, Cluster Analysis, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling

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STEM: a tool for the analysis of short time series gene expression data

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