Name
Title | ||
|---|---|---|
Assessing reproducibility of matrix factorization methods in independent transcriptomes. |
Abstract | ||
|---|---|---|
Motivation: Matrix factorization (MF) methods are widely used in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). MF algorithms have never been compared based on the between-datasets reproducibility of their outputs in similar independent datasets. Lack of this knowledge might have a crucial impact when generalizing the predictions made in a study to others. Results: We systematically test widely used MF methods on several transcriptomic datasets collected from the same cancer type (14 colorectal, 8 breast and 4 ovarian cancer transcriptomic datasets). Inspired by concepts of evolutionary bioinformatics, we design a novel framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the MF methods for their ability to produce generalizable components. We show that a particular protocol of application of independent component analysis (ICA), accompanied by a stabilization procedure, leads to a significant increase in the between-datasets reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other standard methods. We developed a user-friendly tool for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors associated to biological processes or to technological artifacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bioinformatics/btz225 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Reproducibility,Computer science,Matrix decomposition | Journal | 35 |
Issue | ISSN | Citations |
21 | 1367-4803 | 1 |
PageRank | References | Authors |
0.36 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura Cantini | 1 | 1 | 0.36 |
| Ulykbek Kairov | 2 | 6 | 1.21 |
| Aurélien de Reyniès | 3 | 1 | 0.70 |
| Emmanuel Barillot | 4 | 950 | 165.00 |
| François Radvanyi | 5 | 264 | 35.47 |
| Andrei Zinovyev | 6 | 282 | 27.30 |