Identification of perturbed signaling pathways from gene expression data using information divergence

2017 ◽  
Vol 13 (9) ◽  
pp. 1797-1804
Author(s):  
Xinying Hu ◽  
Hang Wei ◽  
Haoran Zheng
Keyword(s):  
Gene Expression ◽  
Probability Distribution ◽  
Signaling Pathways ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Expression Data ◽  
Analysis Method ◽  
Information Divergence

We propose a pathway analysis method based on information divergence and the probability distribution of the regulation capacity.

GENOME-WIDE PATHWAY ANALYSIS AND VISUALIZATION USING GENE EXPRESSION DATA

2001 ◽  
Author(s):  
M. P. KURHEKAR ◽  
S. ADAK ◽  
S. JHUNJHUNWALA ◽  
K. RAGHUPATHY
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Expression Data ◽  
Genome Wide

scholarly journals HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 931 ◽  
Author(s):  
Mok ◽  
Kim ◽  
Lee ◽  
Choi ◽  
Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Structural Component ◽  
Biological Data ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Global Test ◽  
Causal Pathways

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.

scholarly journals Genome-wide Pathway Analysis Using Gene Expression Data of Colonic Mucosa in Patients with Inflammatory Bowel Disease

2015 ◽  
pp. 1 ◽  
Author(s):  
Orazio Palmieri ◽  
Teresa M. Creanza ◽  
Fabrizio Bossa ◽  
Orazio Palumbo ◽  
Rosalia Maglietta ◽  
...  
Keyword(s):  
Gene Expression ◽  
Inflammatory Bowel Disease ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Bowel Disease ◽  
Colonic Mucosa ◽  
Expression Data ◽  
Genome Wide ◽  
Inflammatory Bowel

scholarly journals Pathway analysis of transcriptomic data shows immunometabolic effects of vitamin D

2018 ◽  
Vol 60 (2) ◽  
pp. 95-108 ◽  
Author(s):  
Amadeo Muñoz Garcia ◽  
Martina Kutmon ◽  
Lars Eijssen ◽  
Martin Hewison ◽  
Chris T Evelo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Vitamin D ◽  
Cell Line ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Immune Cell ◽  
Cell Model ◽  
Active Form ◽  
Expression Data ◽  
Regulatory Pathways

Unbiased genomic screening analyses have highlighted novel immunomodulatory properties of the active form of vitamin D, 1,25-dihydroxyvitamin D (1,25(OH)2D). However, clearer interpretation of the resulting gene expression data is limited by cell model specificity. The aim of the current study was to provide a broader perspective on common gene regulatory pathways associated with innate immune responses to 1,25(OH)2D, through systematic re-interrogation of existing gene expression databases from multiple related monocyte models (the THP-1 monocytic cell line (THP-1), monocyte-derived dendritic cells (DCs) and monocytes). Vitamin D receptor (VDR) expression is common to multiple immune cell types, and thus, pathway analysis of gene expression using data from multiple related models provides an inclusive perspective on the immunomodulatory impact of vitamin D. A bioinformatic workflow incorporating pathway analysis using PathVisio and WikiPathways was utilized to compare each set of gene expression data based on pathway-level context. Using this strategy, pathways related to the TCA cycle, oxidative phosphorylation and ATP synthesis and metabolism were shown to be significantly regulated by 1,25(OH)2D in each of the repository models (Z-scores 3.52–8.22). Common regulation by 1,25(OH)2D was also observed for pathways associated with apoptosis and the regulation of apoptosis (Z-scores 2.49–3.81). In contrast to the primary culture DC and monocyte models, the THP-1 myelomonocytic cell line showed strong regulation of pathways associated with cell proliferation and DNA replication (Z-scores 6.1–12.6). In short, data presented here support a fundamental role for active 1,25(OH)2D as a pivotal regulator of immunometabolism.

scholarly journals Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1559
Author(s):  
Jiande Wu ◽  
Tarun Karthik Kumar Mamidi ◽  
Lu Zhang ◽  
Chindo Hicks
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathways ◽  
Somatic Mutation ◽  
Gene Expression Data ◽  
Triple Negative Breast Cancer ◽  
Somatic Mutations ◽  
Triple Negative ◽  
Expression Data ◽  
Gene Expression Signatures

Background: The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). Methods: We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer. Results: The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways. Conclusion: The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.

rPAC: Route based Pathway Analysis for Cohorts of Gene Expression Data Sets

Methods ◽  
2021 ◽  
Author(s):  
Pujan Joshi ◽  
Brent Basso ◽  
Honglin Wang ◽  
Seung-Hyun Hong ◽  
Charles Giardina ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Data Sets ◽  
Expression Data

scholarly journals Pathway analysis of gene expression data from colonic biopsies distinguishes salmon from cod consumers

2013 ◽  
Vol 72 (OCE2) ◽  
Author(s):  
G. Majsak-Newman ◽  
G. Hooiveld ◽  
G. K. Pot ◽  
L. J. Harvey ◽  
J. F. Doleman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Expression Data ◽  
Colonic Biopsies
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