Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Murie, Carl; Barette, Caroline; Lafanechère, Laurence; Nadon, Robert

doi:10.1177/1087057113516003

Cited by 15 publications

(11 citation statements)

References 29 publications

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“…Subsequently, gene symbols were identified from annotation files, with the use of editing codes. Next, expression profiling of gene symbols was performed by Z-score normalization, as previously described (10). The linear models for microarray data (limma) version 3.28.17 (11) in R-software package () were applied to identify the DEGs among the three mouse groups.…”

Section: Methodsmentioning

confidence: 99%

Identification of functional pathways associated with the conditional ablation of serum response factor in Dstncorn1 mice

Huo

Xie

Jiang

2016

Molecular Medicine Reports

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The aim of the present study was to investigate the signaling pathways associated with functional alterations in corneal tissues following the conditional ablation of serum response factor (Srf) in Dstncorn1 mice. The gene expression profiling array GSE49688, which includes 3 samples each from the wild-type (WT), Dstncorn1 mutant (corn1) and corn1 mice following the conditional ablation of Srf from the corneal epithelium [namely rescued (res)] mouse groups, was downloaded from the Gene Expression Omnibus database. The limma package was used to identify differentially expressed genes (DEGs) among the three mouse groups. DEGs were subsequently analyzed by dynamic comparison, hierarchical clustering and pathway enrichment analysis. Pathway alteration scores were also calculated in order to study the dynamic metergasis of each identified pathway. A total of 788 DEGs were identified between the corn1 and res groups, 1,365 DEGs were identified between the corn1 and WT groups, and 852 DEGs were identified between the res and WT groups. Among these DEGs, 228 genes were differentially expressed across all three groups, and were mainly enriched in signaling pathways involved in the regulation of the actin cytoskeleton, including the cofilin 1 (CFL1), the mitogen-activated protein kinase (MAPK) signaling pathway and focal adhesion. The dilated cardiomyopathy signaling pathway displayed the highest alteration score, and was enriched with integrin and integrin β-6 (ITGB6). In conclusion, the actin cytoskeleton regulatory pathway, MAPK and dilated cardiomyopathy signaling pathways, as well as CFL1 and ITGB6 genes, may be regulated by Srf to serve important roles in the progression of corneal disease.

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Section: Methodsmentioning

confidence: 99%

Identification of functional pathways associated with the conditional ablation of serum response factor in Dstncorn1 mice

Huo

Xie

Jiang

2016

Molecular Medicine Reports

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“…The average value of multiple probes (that were corresponding to the same gene) was used as the gene expression value. To eliminate inherent expression differences between genes, the gene expression values were performed with Z-score normalization as previously described ( 15 ). Subsequently, the limma package version 3.32.2 in R ( 16 ) was used to screen the DEGs in the Gram-positive and Gram-negative samples compared with the control samples.…”

Section: Methodsmentioning

confidence: 99%

Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation

Zhang

Liu

et al. 2017

Molecular Medicine Reports

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Sepsis is an inflammatory response to pathogens (such as Gram-positive and Gram-negative bacteria), which has high morbidity and mortality in critically ill patients. The present study aimed to identify the key genes in Gram-positive and Gram-negative sepsis. GSE6535 was downloaded from Gene Expression Omnibus, containing 17 control samples, 18 Gram-positive samples and 25 Gram-negative samples. Subsequently, the limma package in R was used to screen the differentially expressed genes (DEGs). Hierarchical clustering was conducted for the specific DEGs in Gram-negative and Gram-negative samples using cluster software and the TreeView software. To analyze the correlation of samples at the gene level, a similarity network was constructed using Cytoscape software. Functional and pathway enrichment analyses were conducted for the DEGs using DAVID. Finally, stochastic perturbation was used to determine the significantly differential functions between Gram-positive and Gram-negative samples. A total of 340 and 485 DEGs were obtained in Gram-positive and Gram-negative samples, respectively. Hierarchical clustering revealed that there were significant differences between control and sepsis samples. In Gram-positive and Gram-negative samples, myeloid cell leukemia sequence 1 was associated with apoptosis and programmed cell death. Additionally, NADH:ubiquinone oxidoreductase subunit S4 was associated with mitochondrial respiratory chain complex I assembly. Stochastic perturbation analysis revealed that NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2), NDUFB8 and ubiquinol-cytochrome c reductase hinge protein (UQCRH) were associated with cellular respiration in Gram-negative samples, whereas large tumor suppressor kinase 2 (LATS2) was associated with G1/S transition of the mitotic cell cycle in Gram-positive samples. NDUFB2, NDUFB8 and UQCRH may be biomarkers for Gram-negative sepsis, whereas LATS2 may be a biomarker for Gram-positive sepsis. These findings may promote the therapies of sepsis caused by Gram-positive and Gram-negative bacteria.

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“…To eliminate inherent variations in gene expression, we used the Z-score correction method [16]. We defined the good prognosis group as the control group, and the poor prognosis group as the case group.…”

Section: Methodsmentioning

confidence: 99%

Identification of breast cancer recurrence risk factors based on functional pathways in tumor and normal tissues

et al. 2016

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The recurrence of breast cancer (BC) is a serious therapeutic problem, and the risk factors for recurrence urgently need to be identified. In this study, we examined the functional pathways in tumor and normal tissues to more comprehensively identify biomarkers for the risk of BC recurrence. We collected tumor and normal tissue gene expression profiles of recurrent BC patients and non-recurrent BC patients from the TCGA database.We derived an expression interval (mean ± 1.96SD) based on non-recurrent patients rather than a single value, such as a mean or median. If the expression of a gene was significantly different from its normal expression interval, it was considered a differentially expressed gene. Eight pathways that significantly distinguished recurrent and non-recurrent BC patients were obtained based on 65% accuracy, and these pathways were all associated with the immune response and sensitivity to drugs. The genes in these eight pathways were also used to analyze survival, and the significance level reached 0.003 in an independent dataset (p = 0.02 in tumor and p = 0.03 in normal tissue). Our results reveal that the integration of tumor and normal tissue functional analyses can comprehensively enhance the understanding of BC prognosis.

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Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Cited by 15 publications

References 29 publications

Identification of functional pathways associated with the conditional ablation of serum response factor in Dstncorn1 mice

Identification of functional pathways associated with the conditional ablation of serum response factor in Dstncorn1 mice

Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation

Identification of breast cancer recurrence risk factors based on functional pathways in tumor and normal tissues

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