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Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer
Kailin Tang1, Xuejie Ji1, Mengdi Zhou1
1Department of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China.
Nucleic Acids Research
|July 2, 2021
Summary
Rank-In corrects nonbiological variability between microarray and RNA-seq data, enabling integrated transcriptomics analysis. This method accurately identifies cancer profiles and differentially expressed genes, advancing large-scale genomic studies.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Transcriptomics technologies like microarray and RNA-sequencing (RNA-seq) have advanced rapidly.
- Integrating data from these different technologies presents challenges due to inherent variability.
- Consolidated analysis of mixed data is crucial for comprehensive genomic studies.
Purpose of the Study:
- To introduce Rank-In, a novel method for correcting nonbiological effects in mixed microarray and RNA-seq data.
- To enable the seamless integration of data from different transcriptomic technologies for consolidated analysis.
- To validate the efficacy of Rank-In in classifying biological samples and identifying differentially expressed genes (DEGs).
Main Methods:
- Development of Rank-In algorithm to correct for cross-technology variability.
- Validation using public datasets, including SEQC reference samples, Glioblastoma (GBM), and colon cancer profiles.
- Performance comparison with existing methods on mixed seq-array data.
Main Results:
- Rank-In accurately classified 44 profiles from SEQC reference samples.
- Achieved high accuracy (0.9) in predicting TaqMan-validated DEGs.
- Successfully discriminated cancer profiles from normal controls in large Glioblastoma and colon cancer datasets, outperforming other methods.
- Demonstrated robust DEG overlap (0.74-0.83) in mixed seq-array GBM profiles, exceeding other methods (0.72).
Conclusions:
- Rank-In is the first effective method for cross-technology integrative analysis of transcriptomic data.
- Enables the hybrid use of microarray and RNA-seq profiles for large-scale studies.
- Facilitates integrative analysis across various sample types and sizes, potentially reducing clinical sampling needs.
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