dream: Powerful differential expression analysis for repeated measures designs

Hoffman, Gabriel E.; Roussos, Panos

doi:10.1101/432567

Cited by 6 publications

(7 citation statements)

References 64 publications

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“…Gene raw counts were normalized by TMM methods in edgeR (Robinson et al, 2010). To identify age-related differentially expressed genes, we used mixed effect linear regression model for global analysis, and linear regression model for tissue-specific analyses using DREAM (Hoffman & Roussos, 2020). For the global analysis, we set tissue and sex as fixed factors, age as a covariate and individual as a random effect.…”

Section: Bulk Rna Sequences Data Resources and Analysesmentioning

confidence: 99%

Age‐related telomere attrition causes aberrant gene expression in sub‐telomeric regions

et al. 2021

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Telomere attrition has been proposed as a biomarker and causal factor in aging. In addition to causing cellular senescence and apoptosis, telomere shortening has been found to affect gene expression in subtelomeric regions. Here, we analyzed the distribution of age‐related differentially expressed genes from the GTEx RNA sequencing database of 54 tissue types from 979 human subjects and found significantly more upregulated than downregulated genes in subtelomeric regions as compared to the genome‐wide average. Our data demonstrate spatial relationships between telomeres and gene expression in aging.

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Section: Bulk Rna Sequences Data Resources and Analysesmentioning

confidence: 99%

Age‐related telomere attrition causes aberrant gene expression in sub‐telomeric regions

et al. 2021

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“…Differentially expressed genes were identified using dream statistical model available within variancePartition package [26], considering a significance level threshold of FDR ≤ 0.05. Weighted-gene co-expression network analysis was performed separately for NPC and Neurons using WGCNA package from R [27] (Supplementary Material).…”

Section: Differential Expression and Weighted-gene Correlation Networmentioning

confidence: 99%

“…Since we have multiple clones for each individual, we then applied a statistical model that accounts for a repeated measurement design [26] to properly control the false discovery rate in the identification of differentially expressed genes (DEGs) between ASD and control neuronal cells. In NPC, we did not identify any gene with differential expression that reached statistical significance (Supplementary Table S6), while in neurons, we found 20 genes with significant expression dysregulation (multiple testing corrected p < 0.05, Supplementary Table S7).…”

Section: Transcriptome Analysis Reveals Altered Gene Expression In Asmentioning

confidence: 99%

Transcriptome of iPSC-derived neuronal cells reveals a module of co-expressed genes consistently associated with autism spectrum disorder

et al. 2020

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Evaluation of expression profile in autism spectrum disorder (ASD) patients is an important approach to understand possible similar functional consequences that may underlie disease pathophysiology regardless of its genetic heterogeneity. Induced pluripotent stem cell (iPSC)-derived neuronal models have been useful to explore this question, but larger cohorts and different ASD endophenotypes still need to be investigated. Moreover, whether changes seen in this in vitro model reflect previous findings in ASD postmortem brains and how consistent they are across the studies remain underexplored questions. We examined the transcriptome of iPSC-derived neuronal cells from a normocephalic ASD cohort composed mostly of high-functioning individuals and from non-ASD individuals. ASD patients presented expression dysregulation of a module of co-expressed genes involved in protein synthesis in neuronal progenitor cells (NPC), and a module of genes related to synapse/neurotransmission and a module related to translation in neurons. Proteomic analysis in NPC revealed potential molecular links between the modules dysregulated in NPC and in neurons. Remarkably, the comparison of our results to a series of transcriptome studies revealed that the module related to synapse has been consistently found as upregulated in iPSC-derived neurons-which has an expression profile more closely related to fetal brain-while downregulated in postmortem brain tissue, indicating a reliable association of this network to the disease and suggesting that its dysregulation might occur in different directions across development in ASD individuals. Therefore, the expression pattern of this network might be used as biomarker for ASD and should be experimentally explored as a therapeutic target.

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“…We compared the proposed method (FMT in LMM) to Limma [12] and Dream methods [13]. The Limma method calculates a common correlation coefficient between technical replicates for all genes to account for the correlation of repeated measurements within subjects in a linear fixed-effects model setting.…”

Section: Resultsmentioning

confidence: 99%

“…Limma method [12] incorporates correlation estimates of replicates in linear modeling, but it enforces a common correlation for all genes, which works well for within-array replicates as originally planned, but is risky for between-array replicates. Dream method [13] tries to solve this issue in the setting of linear mixed-effects models. This method shrinks residual variances only, while variance estimates of random effects are scaled to residual variances before and after shrinkage.…”

Section: Introductionmentioning

confidence: 99%

Fully moderated t-statistic in linear modeling of mixed effects for differential expression analysis

Zhang

Brock

et al. 2019

BMC Bioinformatics

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BackgroundGene expression profiling experiments with few replicates lead to great variability in the estimates of gene variances. Toward this end, several moderated t-test methods have been developed to reduce this variability and to increase power for testing differential expression. Most of these moderated methods are based on linear models with fixed effects where residual variances are smoothed under a hierarchical Bayes framework. However, they are inadequate for designs with complex correlation structures, therefore application of moderated methods to linear models with mixed effects are needed for differential expression analysis.ResultsWe demonstrated the implementation of the fully moderated t-statistic method for linear models with mixed effects, where both residual variances and variance estimates of random effects are smoothed under a hierarchical Bayes framework. We compared the proposed method with two current moderated methods and show that the proposed method can control the expected number of false positives at the nominal level, while the two current moderated methods fail.ConclusionsWe proposed an approach for testing differential expression under complex correlation structures while providing variance shrinkage. The proposed method is able to improve power by moderation and controls the expected number of false positives properly at the nominal level.

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dream: Powerful differential expression analysis for repeated measures designs

Cited by 6 publications

References 64 publications

Age‐related telomere attrition causes aberrant gene expression in sub‐telomeric regions

Age‐related telomere attrition causes aberrant gene expression in sub‐telomeric regions

Transcriptome of iPSC-derived neuronal cells reveals a module of co-expressed genes consistently associated with autism spectrum disorder

Fully moderated t-statistic in linear modeling of mixed effects for differential expression analysis

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