Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer.
Nucleic Acids Res
; 49(17): e99, 2021 09 27.
Article
in En
| MEDLINE
| ID: mdl-34214174
ABSTRACT
Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http//www.badd-cao.net/rank-in/index.html.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Computational Biology
/
Oligonucleotide Array Sequence Analysis
/
Gene Expression Profiling
/
RNA-Seq
/
Neoplasms
Type of study:
Diagnostic_studies
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Nucleic Acids Res
Year:
2021
Type:
Article