NMR metabolomic and microarray-based transcriptomic data integration identifies unique molecular signatures of hypersensitivity pneumonitis

Molecular Omics ◽

10.1039/d1mo00209k ◽

2022 ◽

Author(s):

Sanjukta Dasgupta ◽

Nilanjana Ghosh ◽

Priyanka Choudhury ◽

Mamata Joshi ◽

Sushmita Roy Chowdhury ◽

...

Keyword(s):

Data Integration ◽

Hypersensitivity Pneumonitis ◽

Molecular Signatures ◽

Transcriptomic Data ◽

Integrated Metabolomics

This original article focuses on integrated metabolomics and transcriptomics analysis to understand the pathogenesis of hypersensitivity pneumonitis (HP).

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Francisella tularensis novicida proteomic and transcriptomic data integration and annotation based on semantic web technologies

BMC Bioinformatics ◽

10.1186/1471-2105-10-s10-s3 ◽

2009 ◽

Vol 10 (S10) ◽

Cited By ~ 1

Author(s):

Nadia Anwar ◽

Ela Hunt

Keyword(s):

Semantic Web ◽

Data Integration ◽

Francisella Tularensis ◽

Semantic Web Technologies ◽

Web Technologies ◽

Transcriptomic Data

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GEDI: an R package for integration of transcriptomic data from multiple high-throughput platforms

10.1101/2021.11.11.468093 ◽

2021 ◽

Author(s):

Mathias N Stokholm ◽

Maria B Rabaglino ◽

Haja N Kadarmideen

Keyword(s):

Gene Expression ◽

Data Integration ◽

Principal Component ◽

R Package ◽

Gene Expression Omnibus ◽

Batch Effect ◽

Sequencing Data ◽

Transcriptomic Data ◽

Ncbi Gene Expression Omnibus ◽

Forward Stepwise

Transcriptomic data is often expensive and difficult to generate in large cohorts in comparison to genomic data and therefore is often important to integrate multiple transcriptomic datasets from both microarray and next generation sequencing (NGS) based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including re-annotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining already existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically re-annotating the data and removing the batch effect. The removal of the batch effect is verified with Principal Component Analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. The datasets included Affymetrix, Agilent and RNA-sequencing data. Furthermore, we compared the GEDI package to already existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration.

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Take ACTION to characterize the functional identity of single cells

10.1101/081273 ◽

2016 ◽

Author(s):

Shahin Mohammadi ◽

Vikram Ravindra ◽

David F. Gleich ◽

Ananth Grama

Keyword(s):

Regulatory Networks ◽

Single Cells ◽

Survival Rates ◽

Molecular Signatures ◽

Functional Identity ◽

Cell Type ◽

Differential Survival ◽

Transcriptomic Data ◽

Lineage Differentiation ◽

Therapeutic Responses

Single-cell transcriptomic data has the potential to radically redefine our view of cell type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods for automatically characterizing the functional identity of cells, and their associated properties, can be used to uncover processes involved in lineage differentiation as well as sub-typing cancer cells. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, to infer the functional identity of cells from their transcriptional profile, classify them based on their dominant function, and reconstruct regulatory networks that are responsible for mediating their identity. Using ACTION, we identify novel Melanoma sub-types with differential survival rates and therapeutic responses, for which we provide biomarkers along with their underlying regulatory networks.

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