We are analyzing https://link.springer.com/article/10.1186/1471-2105-7-191.

Title:
STEM: a tool for the analysis of short time series gene expression data | BMC Bioinformatics
Description:
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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Keywords {🔍}

time, stem, expression, gene, genes, profiles, data, series, profile, analysis, clustering, figure, number, model, enrichment, based, article, experiments, category, pubmed, short, microarray, software, significant, size, user, assigned, google, scholar, set, algorithm, temporal, cluster, file, points, ontology, interface, methods, response, bioinformatics, window, cas, table, expected, authors, information, integration, full, biological, image,

Topics {✒️}

genome-wide expression patterns gene expression time-courses open access article bar-joseph time series data time series experiment longer time series author information authors time series experiments aligning time series long time series analysis primarily designed gene expression data software implementing methods k-means clustering methodology gene expression analysis k-means clustering algorithm gene expression dynamics gene expression omnibus differential gene expression open-source system model overview screen top left-hand corner jason ernst article download pdf gene annotation source high-throughput miner [20] gene ontology[http microarray data analysis gastric epithelial cells privacy choices/manage cookies article ernst gene annotation files cell cycle-regulated genes microarray studies include integrative program suite microarray data management time series full size image authors’ original file temporal expression pattern temporal expression profiles supports gene ontology related subjects european bioinformatics institute graphical query language external gene ontology multiple-microarray experiments differentially expressed genes clustering algorithm designed

Questions {❓}

STEM allows a user to investigate questions such as: "for a set of genes which had temporal response X in experiment A, what significant responses did they have in experiment B?

Schema {🗺️}

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         Microarrays
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         Computer Appl. in Life Sciences
         Algorithms
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