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Gene set analysis approaches for RNA-seq data: Performance evaluation and application guideline
Yasir Rahmatallah
,
Frank Emmert-Streib
, Galina Glazko
*
*
Corresponding author for this work
University of Arkansas for Medical Sciences
Tampere University
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Keyphrases
Performance Evaluation
100%
Application Performance
100%
Gene Analysis
100%
RNA-seq Data
100%
Evaluation Guidelines
100%
Application Guidelines
100%
Data Performance
100%
RNA Sequencing (RNA-seq)
60%
Microarray
60%
Competitive Method
60%
Gene Set
40%
Result Reproducibility
20%
Low Power
20%
Biological Processes
20%
Microarray Data
20%
Differentially Expressed Genes
20%
Downregulated Genes
20%
Functional Genes
20%
Phenotypic Differences
20%
Selection Bias
20%
Transcriptome Sequencing
20%
Sample Heterogeneity
20%
Type I Error Rate
20%
High-throughput Study
20%
Statistical Hypothesis
20%
Biological Knowledge
20%
Method Choice
20%
Self-contained Method
20%
Microarray Gene
20%
Biochemistry, Genetics and Molecular Biology
RNA Sequencing
100%
Gene Expression
14%
Biological Phenomena and Functions Concerning the Entire Organism
14%
Sample Size
14%
Differentially Expressed Gene
14%
Microarray Data
14%