ArticleCancer informatics2015
An Application of Sequential Meta-Analysis to Gene Expression Studies.
Article in Cancer informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- The Role of Systems Biology in Deciphering Asthma Heterogeneity.Life (Basel, Switzerland) · 2022Review
- Derangement of cell cycle markers in peripheral blood mononuclear cells of asthmatic patients as a reliable biomarker for asthma control.Scientific reports · 2021Article
- Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia.BMC bioinformatics · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is used to evaluate whether the accumulated samples show enough evidence or more experiments should be initiated. We found 313 differentially expressed genes, based on the cumulative information of the experiments. SMA offers an alternative to existing methods in generating a gene list by evaluating the adequacy of the cumulative information.
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Registered trials
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