Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings

Guan, Hong; Li, Hongdong; Zhang, Jiahui; Guan, Qingzhou; Chen, Rou; Guo, Zheng

doi:10.1038/s41598-017-01536-3

Cited by 8 publications

(11 citation statements)

References 47 publications

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“…Many studies have investigated disease-gene and disease-pathway associations with the objective of improving diagnoses [6,7,8,9]. For instance, Zhao et al [8] propose a ranking of disease genes based on gene expression and protein interactions using Katz-centrality.…”

Section: Introductionmentioning

confidence: 99%

Unveiling new disease, pathway, and gene associations via multi-scale neural network

et al. 2020

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Diseases involve complex modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, new biological knowledge about a disease can be extracted from these profiles, improving our ability to diagnose and assess disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient's condition and co-morbidity risk.Here, we consider differential gene expressions obtained by microarray technology for patients diagnosed with various diseases. Based on these data and cellular multi-scale organization, we aim at uncovering disease-disease links, disease-gene and disease-pathways associations. We propose neural networks with structures based on the multi-scale organization of proteins in a cell into protein complexes and pathways. We show that these models are able to correctly predict the diagnosis for the majority of patients. Through the analysis of the trained models, we predict disease-disease, disease-pathway, and disease-gene associations and validate the predictions by comparisons to known interactions and literature search, proposing putative explanations for the predictions.

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Section: Introductionmentioning

confidence: 99%

Unveiling new disease, pathway, and gene associations via multi-scale neural network

et al. 2020

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“…In contrast, for a DEG detected at the population-level, we cannot know whether it is differentially expressed in a particular cancer sample because of the heterogeneity of cancer. The REOs analysis method could also be applied to the identification of disease-associated genes or pathways based on one-phenotype disease data when the normal tissues are unavailable or insufficient for some vital organs such like brain and heart [1,[21][22][23]. In this situation, it is of great value to reuse the normal control data accumulated in other studies.…”

Section: Introductionmentioning

confidence: 99%

“…In this situation, it is of great value to reuse the normal control data accumulated in other studies. And we have proposed a REO-based algorithm, named DRFunc [23], to identify disease-associated pathways based on one-phenotype data through comparing the stable REO in the one-phenotype disease samples with the normal stable REOs background pre-determined in previously accumulated normal samples from other studies. Based on the REOs analysis, we have also proposed a method named "RankCompV2" for identifying DEGs at the population-level through comparing the stable REOs of two phenotypes [24].…”

Section: Introductionmentioning

confidence: 99%

The effects of age, cigarette smoking, sex and race on the qualitative characteristics of lung transcriptome

Guan

Zhang

Guo

et al. 2019

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Background Methods based on within-sample relative expression orderings (REOs) comparisons could be applied for various medical issues such as individualized diagnosis of cancer and subtype identification etc., it could also be used for identifying differentially expressed genes (DEGs) at the individual level and detecting disease-associated genes based on one-phenotype disease data by reusing data of normal samples from other sources. However, the common potential confounding factors, including age, cigarette smoking, sex and race whether could affect the REOs of gene pairs is still unclear. Here, we evaluated these confounding factors on the REOs of gene pairs within normal lung tissues transcriptome. Results For one confounding factor, based on the number of related gene pairs or DEGs, the effect of this confounding factor on REO was evaluated. Our results showed that age has little effect on REOs within lung tissues. We found that about 0.23% of the significantly stable REOs of gene pairs in non-smokers’ lung tissues are reversed in smokers’ lung tissues, introduced by 344 DEGs between the two groups of samples (RankCompV2, FDR < 0.05), which are enriched in metabolism of xenobiotics by cytochrome P450, glutathione metabolism and other pathways (hypergeometric test, FDR < 0.05). Comparison between the normal lung tissue samples of males and females revealed fewer reversal REOs introduced by 24 DEGs between the sex groups, among which 19 DEGs are located on sex chromosomes and 5 DEGs involving in spermatogenesis and regulation of oocyte are located on autosomes. Between the normal lung tissue samples of white and black people, we identified 22 DEGs (RankCompV2, FDR < 0.05) which introduced a few reversal REOs between the two races. Conclusions In summary, the REO-based study should consider the confounding factors of cigarette smoking, sex and race.

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“…In contrast, for a DEG detected at the population-level, we cannot know whether it is differentially expressed in a particular cancer sample because of the heterogeneity of cancer. The REOs analysis method could also be applied to the identification of disease-associated genes or pathways based on one-phenotype disease data when the normal tissues are unavailable or insufficient for some vital organs such like brain and heart [1,[17][18][19]. In this situation, it is of great value to reuse the normal control data accumulated in other studies.…”

Section: Introductionmentioning

confidence: 99%

“…In this situation, it is of great value to reuse the normal control data accumulated in other studies. And we have proposed a REObased algorithm, named DRFunc [19] [21,22]. Several studies have also reported that cigarette smoking [23] and race [24] could alert the gene expression levels, and the gene expression levels change with age in many organ tissues, including lung tissues [25].…”

Section: Introductionmentioning

confidence: 99%

The effects of age, cigarette smoking, sex and race on the qualitative characteristics of lung transcriptome

Guan

Zhang

Guo

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Methods based on within-sample relative expression orderings (REOs) comparisons have been proposed for identifying differentially expressed genes (DEGs) at the individual level and for detecting disease-associated genes based on one-phenotype disease data by reusing data of normal samples from other sources. However, the common potential confounding factors, including age, cigarette smoking, sex and race whether could affect the REOs of gene pairs is still unclear. Methods: For one confounding factor, based on the numbers of related gene pairs or DEGs, we evaluated the effect of this confounding factor on the REOs of gene pairs within normal lung tissues transcriptome. Results: Our results showed that age has little effect on REOs within lung tissues. We found that about 0.23% of the significantly stable REOs of gene pairs in non-smokers’ lung tissues are reversed in smokers’ lung tissues, introduced by 344 DEGs between the two groups of samples (RankCompV2, FDR < 0.05), which are enriched in metabolism of xenobiotics by cytochrome P450, glutathione metabolism and other pathways (hypergeometric test, FDR < 0.05). Comparison between the normal lung tissue samples of males and females revealed fewer reversal REOs introduced by 24 DEGs between the sex groups, among which 19 DEGs are located on sex chromosomes and 5 DEGs involving in spermatogenesis and regulation of oocyte are located on autosomes. Between the normal lung tissue samples of white and black people, we identified 22 DEGs (RankCompV2, FDR < 0.05) which introduced a few reversal REOs between the two races. Conclusions: In summary, the REO-based study should take into account the confounding factors of cigarette smoking, sex and race.

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Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings

Cited by 8 publications

References 47 publications

Unveiling new disease, pathway, and gene associations via multi-scale neural network

Unveiling new disease, pathway, and gene associations via multi-scale neural network

The effects of age, cigarette smoking, sex and race on the qualitative characteristics of lung transcriptome

The effects of age, cigarette smoking, sex and race on the qualitative characteristics of lung transcriptome

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