ArticlePloS one2017
DEclust: A statistical approach for obtaining differential expression profiles of multiple conditions.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data.Nature communications · 2021Article
- A wild-derived inbred mouse strain, MSM/Ms, provides insights into novel skin tumor susceptibility genes.Experimental animals · 2021Article
- A graph-based algorithm for RNA-seq data normalization.PloS one · 2020Article
- Article
- A feature selection strategy for gene expression time series experiments with hidden Markov models.PloS one · 2019Article
- Time-Series Analysis of Tumorigenesis in a Murine Skin Carcinogenesis Model.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput RNA sequencing technology is widely used to comprehensively detect and quantify cellular gene expression. Thus, numerous analytical methods have been proposed for identifying differentially expressed genes (DEGs) between paired samples such as tumor and control specimens, but few studies have reported methods for analyzing differential expression under multiple conditions. We propose a novel method, DEclust, for differential expression analysis among more than two matched samples from distinct tissues or conditions. As compared to conventional clustering methods, DEclust more accurately extracts statistically significant gene clusters from multi-conditional transcriptome data, particularly when replicates of quantitative experiments are available. DEclust can be used for any multi-conditional transcriptome data, as well as for extending any DEG detection tool for paired samples to multiple samples. Accordingly, DEclust can be used for a wide range of applications for transcriptome data analysis. DEclust is freely available at http://www.dna.bio.keio.ac.jp/software/DEclust.
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Registered trials
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