Topconfects: a package for confident effect sizes in differential expression analysis provides a more biologically useful ranked gene list
Abstract Differential gene expression analysis may discover a set of genes too large to easily investigate, so a means of ranking genes by biological interest level is desired. p values are frequently abused for this purpose. As an alternative, we propose a method of ranking by confidence bounds on...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-03-01
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| Series: | Genome Biology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13059-019-1674-7 |
| Summary: | Abstract Differential gene expression analysis may discover a set of genes too large to easily investigate, so a means of ranking genes by biological interest level is desired. p values are frequently abused for this purpose. As an alternative, we propose a method of ranking by confidence bounds on the log fold change, based on the previously developed TREAT test. These confidence bounds provide guaranteed false discovery rate and false coverage-statement rate control. When applied to a breast cancer dataset, the top-ranked genes by Topconfects emphasize markedly different biological processes compared to the top-ranked genes by p value. |
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| ISSN: | 1474-760X |