| Abstract | Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. |
| Author Summary | Our work argues that genome-scale gene expression can be a multipurpose marker for identifying latent, heterogeneous cellular and environmental states and that optimal classification can be achieved with a feature set of a couple hundred genes that might not necessarily have the most pronounced differential expression in the respective conditions. |
| Biomarker discovery through functional and network analysis | In this set of genes, there are also genes already described to be differentially expressed in stationary phase, like hpf, crr and sspA [42—44]. |
| Biomarker discovery through functional and network analysis | In addition, 5 differentially expressed genes involved in carbohydrate metabolism also stand out (malS, kgtP, malF, malG, pta). |
| Biomarker discovery through functional and network analysis | Interestingly, a substantial subset of the informative genes that were selected as features were not differentially expressed in the respective samples (82 Table). |
| Introduction | Similarly, it is known that bacterial organisms undergoing rapid adaptations to varying environments, such as heat-shock and osmotic stress, produce differential expression profiles that are indicative of the corresponding stress [4—9]. |
| Supporting Information | The intersection of the feature gene set when mutual information (MI) and differential expression (DEG) are used for ranking. |
| Supporting Information | Differential expression ranking was determined by ANOVA. |
| Cryptic 3’SSs are used infrequently relative to canonical 3’SSs | To investigate the potential role of NMD, we identified differentially expressed genes between the SF3BI mutant and wild-type samples in a joint analysis of all three cancers and performed a gene set enrichment analysis. |
| Cryptic 3’SSs are used infrequently relative to canonical 3’SSs | We found that genes in the “Reactome NMD enhanced by the exon junction complex” set were enriched (GSEA [21], q < 1028) among the 272 differentially expressed genes (DESeq2, BH-adjusted p < 0.1, 88—89 Files) suggesting that NMD may be different between the SF3BI mutants and wild-types. |
| Cryptic 3’SSs are used infrequently relative to canonical 3’SSs | 33 of the 582 genes that contained the 619 proximal cryptic 3’SSs were differentially expressed with the expression of 29/33 of these genes lower in the SF3BI mutants. |
| Discussion | While the differentially eXpressed genes between the SF3BI mutated and wild-type samples are enriched for genes in the NMD pathway, even in-frame cryptic 3’SSs are used at a low frequency indicating that the associated canonical 3’SS is mostly preferred to the cryptic 3’SS even in SF3BI mutants. |
| Supporting Information | 272 genes that are differentially expressed between SF3BI mutant and wild-type samples from joint analysis of CLL, BRCA, and UM using DESeq2. |
| Supporting Information | 33 genes that are differentially expressed between SF3BI mutant and wild-type |
| Gene expression profiling | Differentially expressed genes between different treatments were detected by fitting linear models and computing empirical Bayes moderated t statistics, comparing two groups at a time, using the limma package in R [58]. |
| Gene expression profiling | P values were adjusted for multiple comparisons using the Benjamini Hochberg procedure [59] and genes with an adjusted p value of < = 0.05 were selected as differentially expressed . |
| Gene expression profiling | For pathway analysis, differentially expressed genes were ranked according to fold change (high to low) comparing two treatments and Gene Set Enrichment Analysis (GSEA) was performed on the ranked lists against MSigDB gene sets using the NCBI gene id as a unique identifier [60]. |
| abundance of genes regulating differentiation and immune response of TEM differentiated in vitro | A total of 398 genes were significantly (p< 0.05) and differentially expressed between the two clusters among which 369 and 72 genes were altered by TGF-B/ANG-2 and TGF-B/PlGF treatments respectively (SS Table, NT unique lists) while 43 were regulated in common (SS Table, NT intersect list). |
| abundance of genes regulating differentiation and immune response of TEM differentiated in vitro | Therefore, the 398 differentially expressed genes were annotated and classified in categories manually (SS Table). |
| Abstract | With such relative—or compositional—data, differential expression needs careful interpretation, and correlation—a statistical workhorse for analyzing pain/vise relationships—is an inappropriate measure of association. |
| Correlations between relative abundances tell us absolutely nothing | While “differential expression” of relative abundances is challenging to interpret, in the absence of any other information or assumptions, correlation of relative abundances is just wrong. |
| Correlations between relative abundances tell us absolutely nothing | If this assumption holds, and all the mRNAs comprising that total are considered, the relative abundance of each kind of mRNA will be proportional to its absolute abundance, and analyses of correlation or “differential expression” of the relative values will have clear interpretations. |
| Results | Challenges in interpreting “differential expression” |
| Results | Tests for differential expression are popular for analyzing relative data in bioscience. |