Abstract
The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.
Highlights
Gene expression profiling provides an opportunity to explore the unique characteristics of biological states or phenotypes
The interactive rank–rank hypergeometric overlap (RRHO) plots visualize the upregulated or downregulated pathways between two or more differential gene expression profiles based on overlapping genes belonging to bins in two expression profiles
RankerGUI provided a rich set of features to explore differential gene expression data and capture the signature or patterns from different experimental conditions using heterogeneous data
Summary
Gene expression profiling provides an opportunity to explore the unique characteristics of biological states or phenotypes. With the availability of huge amounts of gene expression data in public repositories (The Library of Integrated Network-Based Cellular Signatures, ArrayExpress, Gene Expression Omnibus), it is possible to obtain the expression data for the comparison of studies across different experimental conditions. Comparative studies help in characterizing experimental conditions by their expression patterns, which in turn leads to the understanding of underlying transcriptional responses in diseases, drugs, gene perturbations’ effects, and complex interaction networks within genes and associated pathways. The small number of biological samples used in the experiment is a hindrance to expression analysis. Data integration is a crucial step towards gaining new perspectives from big data produced by various independent studies intended to address similar biological problems
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