Model-based clustering of multi-tissue gene expression data

Erola, Pau; Björkegren, Johan; Michoel, Tom

doi:10.1093/bioinformatics/btz805

Cited by 18 publications

(14 citation statements)

References 62 publications

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“…Gene co-expression patterns characterize regulatory interactions within and across tissues ( Dobrin et al, 2009 ; Erola et al, 2020 ; Gerring et al, 2019 ; Zhang et al, 2020 ). Third, we examined whether the UBI induced changes in mRNA–mRNA correlations within the left and right half of the lumbar spinal cord (intra-area correlations), and between these halves (inter-area correlations).…”

Section: Resultsmentioning

confidence: 99%

Left-right side-specific endocrine signaling complements neural pathways to mediate acute asymmetric effects of brain injury

Lukoyanov

Watanabe

Carvalho

et al. 2021

eLife

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Brain injuries can interrupt descending neural pathways that convey motor commands from the cortex to spinal motoneurons. Here, we demonstrate that a unilateral injury of the hindlimb sensorimotor cortex of rats with completely transected thoracic spinal cord produces hindlimb postural asymmetry with contralateral flexion and asymmetric hindlimb withdrawal reflexes within 3 hr, as well as asymmetry in gene expression patterns in the lumbar spinal cord. The injury-induced postural effects were abolished by hypophysectomy and were mimicked by transfusion of serum from animals with brain injury. Administration of the pituitary neurohormones β-endorphin or Arg-vasopressin-induced side-specific hindlimb responses in naive animals, while antagonists of the opioid and vasopressin receptors blocked hindlimb postural asymmetry in rats with brain injury. Thus, in addition to the well-established involvement of motor pathways descending from the brain to spinal circuits, the side-specific humoral signaling may also add to postural and reflex asymmetries seen after brain injury.

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Section: Resultsmentioning

confidence: 99%

Left-right side-specific endocrine signaling complements neural pathways to mediate acute asymmetric effects of brain injury

Lukoyanov

Watanabe

Carvalho

et al. 2021

eLife

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“…An elbow plot was constructed to examine the percentage of variance explained by the clusters and, based on this plot, network construction was conducted using the 7500 most varying genes clustered in 20 clusters, capturing ~ 33% of the variance, which was at the inflection point of the elbow plot. The ‘revamp’ task of Lemon-Tree was applied to the subsets of the scaled gene expression matrix based on the four time points 20 . Genes were reassigned to clusters to maximize the Bayesian co-expression clustering score sampling a parametric threshold (threshold = 4 × 10 –4 ).…”

Section: Methodsmentioning

confidence: 99%

Changes in the gene expression profile during spontaneous migraine attacks

Kogelman

Falkenberg

Buil

et al. 2021

Sci Rep

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Migraine attacks are delimited, allowing investigation of changes during and outside attack. Gene expression fluctuates according to environmental and endogenous events and therefore, we hypothesized that changes in RNA expression during and outside a spontaneous migraine attack exist which are specific to migraine. Twenty-seven migraine patients were assessed during a spontaneous migraine attack, including headache characteristics and treatment effect. Blood samples were taken during attack, two hours after treatment, on a headache-free day and after a cold pressor test. RNA-Sequencing, genotyping, and steroid profiling were performed. RNA-Sequences were analyzed at gene level (differential expression analysis) and at network level, and genomic and transcriptomic data were integrated. We found 29 differentially expressed genes between ‘attack’ and ‘after treatment’, after subtracting non-migraine specific genes, that were functioning in fatty acid oxidation, signaling pathways and immune-related pathways. Network analysis revealed mechanisms affected by changes in gene interactions, e.g. ‘ion transmembrane transport’. Integration of genomic and transcriptomic data revealed pathways related to sumatriptan treatment, i.e. ‘5HT1 type receptor mediated signaling pathway’. In conclusion, we uniquely investigated intra-individual changes in gene expression during a migraine attack. We revealed both genes and pathways potentially involved in the pathophysiology of migraine and/or migraine treatment.

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“…In this study, we propose the use of model-based clustering algorithms for the identification of DEGs from RNA-seq count data. Although various sophisticated algorithms for gene clustering are available [ 30 – 32 ], we focus our analysis on the R package MBCluster.Seq [ 28 ], as its framework is compatible with DE analysis and it is comparable with other R packages dedicated to detecting DEGs. We describe the application of the model-based gene clustering method and the incorporation of a robust normalization algorithm called DEGES [ 7 , 14 ] for detecting DEGs.…”

Section: Introductionmentioning

confidence: 99%

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

2021

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Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis. Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm. Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

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Model-based clustering of multi-tissue gene expression data

Cited by 18 publications

References 62 publications

Left-right side-specific endocrine signaling complements neural pathways to mediate acute asymmetric effects of brain injury

Left-right side-specific endocrine signaling complements neural pathways to mediate acute asymmetric effects of brain injury

Changes in the gene expression profile during spontaneous migraine attacks

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

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