ArticleBMC bioinformatics2014
Fold change rank ordering statistics: a new method for detecting differentially expressed genes.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs.PNAS nexus · 2023Article
- Chemical-induced phase transition and global conformational reorganization of chromatin.Nature communications · 2023Article
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- End-to-end computational approach to the design of RNA biosensors for detecting miRNA biomarkers of cervical cancer.Synthetic and systems biotechnology · 2022Article
- DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning.BMC bioinformatics · 2022Article
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- Helios represses megakaryocyte priming in hematopoietic stem and progenitor cells.The Journal of experimental medicine · 2021Article
- In Silico Analysis to Explore Lineage-Independent and -Dependent Transcriptional Programs Associated with the Process of Endothelial and Neural Differentiation of Human Induced Pluripotent Stem Cells.Journal of clinical medicine · 2021Article
8 more citing papers are in PubMed but not listed here.
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferent methods have been proposed for analyzing differentially expressed (DE) genes in microarray data. Methods based on statistical tests that incorporate expression level variability are used more commonly than those based on fold change (FC). However, FC based results are more reproducible and biologically relevant.
resultsWe propose a new method based on fold change rank ordering statistics (FCROS). We exploit the variation in calculated FC levels using combinatorial pairs of biological conditions in the datasets. A statistic is associated with the ranks of the FC values for each gene, and the resulting probability is used to identify the DE genes within an error level. The FCROS method is deterministic, requires a low computational runtime and also solves the problem of multiple tests which usually arises with microarray datasets.
conclusionWe compared the performance of FCROS with those of other methods using synthetic and real microarray datasets. We found that FCROS is well suited for DE gene identification from noisy datasets when compared with existing FC based methods.
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