scholarly journals Assessing key decisions for transcriptomic data integration in biochemical networks

2019 ◽  
Vol 15 (7) ◽  
pp. e1007185 ◽  
Author(s):  
Anne Richelle ◽  
Chintan Joshi ◽  
Nathan E. Lewis
Keyword(s):  
Data Integration ◽  
Biochemical Networks ◽  
Transcriptomic Data

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

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.

scholarly journals Novel insights in the genetics of HCC recurrence and advances in transcriptomic data integration

2012 ◽  
Vol 56 (1) ◽  
pp. 279-281 ◽  
Author(s):  
Andreas Teufel ◽  
Jens U. Marquardt ◽  
Peter R. Galle
Keyword(s):  
Data Integration ◽  
Transcriptomic Data ◽  
Hcc Recurrence

scholarly journals Transcriptomic Data Integration Inferring the Dominance of Starch Biosynthesis in Carbon Utilization of Developing Cassava Roots

2012 ◽  
Vol 11 ◽  
pp. 96-106 ◽  
Author(s):  
Wanatsanan Siriwat ◽  
Saowalak Kalapanulak ◽  
Malinee Suksangpanomrung ◽  
Supatcharee Netrphan ◽  
Asawin Meechai ◽  
...  
Keyword(s):  
Data Integration ◽  
Starch Biosynthesis ◽  
Carbon Utilization ◽  
Transcriptomic Data

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

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).

scholarly journals First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Marco Moretto ◽  
Paolo Sonego ◽  
Ana B. Villaseñor-Altamirano ◽  
Kristof Engelen
Keyword(s):  
Gene Expression ◽  
Data Integration ◽  
Data Acquisition ◽  
Gene Expression Data ◽  
Expression Data ◽  
Transcriptomic Data

scholarly journals Thylakoid proteome modulation in pea plants grown at different irradiances: quantitative proteomic profiling in a non-model organism aided by transcriptomic data integration

2018 ◽  
Vol 96 (4) ◽  
pp. 786-800 ◽  
Author(s):  
Pascal Albanese ◽  
Marcello Manfredi ◽  
Angela Re ◽  
Emilio Marengo ◽  
Guido Saracco ◽  
...  
Keyword(s):  
Data Integration ◽  
Model Organism ◽  
Proteomic Profiling ◽  
Transcriptomic Data
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