Pathways Enrichment Analysis of Gene Expression Data in Type 2 Diabetes

2019 ◽  
pp. 119-128
Author(s):  
Maysson Ibrahim
Keyword(s):  
Gene Expression ◽  
Type 2 Diabetes ◽  
Gene Expression Data ◽  
Enrichment Analysis ◽  
Expression Data

scholarly journals Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13882 ◽  
Author(s):  
Binghuang Cai ◽  
Xia Jiang
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Gene Expression Data ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Pathway Gene

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

scholarly journals Fast gene set enrichment analysis

2016 ◽  
Author(s):  
Gennady Korotkevich ◽  
Vladimir Sukhov ◽  
Alexey Sergushichev
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Polynomial Algorithm ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
P Values ◽  
Gene Set

AbstractPreranked gene set enrichment analysis (GSEA) is a widely used method for interpretation of gene expression data in terms of biological processes. Here we present FGSEA method that is able to estimate arbitrarily low GSEA P-values with a higher accuracy and much faster compared to other implementations. We also present a polynomial algorithm to calculate GSEA P-values exactly, which we use to practically confirm the accuracy of the method.

scholarly journals Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

2020 ◽  
Vol 18 (4) ◽  
pp. 611-626
Author(s):  
Nestor Timonidis ◽  
Rembrandt Bakker ◽  
Paul Tiesinga
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Computational Models ◽  
Brain Connectivity ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Expression Data ◽  
Binary Forms ◽  
Major Step ◽  
Cell Class

Abstract Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

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