An Integrative Multi-Omics Random Forest Framework for Robust Biomarker Discovery

  1. Wei Zhang
  2. Hanchen Huang
  3. Lily Wang
  4. Brian D. Lehmann
  5. Steven X. Chen

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Abstract

High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating these diverse data types can yield deeper insights into the biological mechanisms driving complex traits and diseases. Yet, extracting key shared biomarkers from multiple data layers remains a major challenge. We present a multivariate random forest (MRF)–based framework enhanced by a novel inverse minimal depth (IMD) metric for integrative variable selection. By assigning response variables to tree nodes and employing IMD to rank predictors, our approach efficiently identifies essential features across different omics types, even when confronted with high-dimensionality and noise. Through extensive simulations and analyses of multi-omics datasets from The Cancer Genome Atlas, we demonstrate that our method outperforms established integrative techniques in uncovering biologically meaningful biomarkers and pathways. Our findings show that selected biomarkers not only correlate with known regulatory and signaling networks but can also stratify patient subgroups with distinct clinical outcomes. The method’s scalable, interpretable, and user-friendly implementation ensures broad applicability to a range of research questions. This MRF-based framework advances robust biomarker discovery and integrative multi-omics analyses, accelerating the translation of complex molecular data into tangible biological and clinical insights.

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  1. GigaScience

    ABSTRACTHigh-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating these diverse data types can yield deeper insights into the biological mechanisms driving complex traits and diseases. Yet, extracting key shared biomarkers from multiple data layers remains a major challenge. We present a multivariate random forest (MRF)–based framework enhanced by a novel inverse minimal depth (IMD) metric for integrative variable selection. By assigning response variables to tree nodes and employing IMD to rank predictors, our approach efficiently identifies essential features across different omics types, even when confronted with high-dimensionality and noise. Through extensive simulations and analyses of multi-omics datasets from The Cancer Genome Atlas, we demonstrate that our method outperforms established integrative techniques in uncovering biologically meaningful biomarkers and pathways. Our findings show that selected biomarkers not only correlate with known regulatory and signaling networks but can also stratify patient subgroups with distinct clinical outcomes. The method’s scalable, interpretable, and user-friendly implementation ensures broad applicability to a range of research questions. This MRF-based framework advances robust biomarker discovery and integrative multi-omics analyses, accelerating the translation of complex molecular data into tangible biological and clinical insights.Competing Interest StatementThe authors have declared no competing interest.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf148), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 3: Yingxia Li

    Summary: This manuscript presents a novel multivariate random forest (MRF)-based framework, incorporating the Inverse Minimal Depth (IMD) metric, for integrative multi-omics variable selection and robust biomarker discovery. The method is thoughtfully developed, rigorously evaluated through comprehensive simulations, and effectively demonstrated on TCGA datasets. The topic is highly relevant, and the manuscript is generally well-organized and clearly written.

    Major comments: The proposed MRF-IMD framework demonstrates significant advantages in handling nonlinear relationships and high-dimensional data integration. However, a more comprehensive comparison with other nonlinear ensemble methods (e.g., gradient boosting or deep learning approaches) is recommended to highlight its uniqueness.

  2. GigaScience

    ABSTRACTHigh-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating these diverse data types can yield deeper insights into the biological mechanisms driving complex traits and diseases. Yet, extracting key shared biomarkers from multiple data layers remains a major challenge. We present a multivariate random forest (MRF)–based framework enhanced by a novel inverse minimal depth (IMD) metric for integrative variable selection. By assigning response variables to tree nodes and employing IMD to rank predictors, our approach efficiently identifies essential features across different omics types, even when confronted with high-dimensionality and noise. Through extensive simulations and analyses of multi-omics datasets from The Cancer Genome Atlas, we demonstrate that our method outperforms established integrative techniques in uncovering biologically meaningful biomarkers and pathways. Our findings show that selected biomarkers not only correlate with known regulatory and signaling networks but can also stratify patient subgroups with distinct clinical outcomes. The method’s scalable, interpretable, and user-friendly implementation ensures broad applicability to a range of research questions. This MRF-based framework advances robust biomarker discovery and integrative multi-omics analyses, accelerating the translation of complex molecular data into tangible biological and clinical insights.Competing Interest StatementThe authors have declared no competing interest.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf148), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 2: Yun-Juan Bao

    The article presents an Integrative Multi-Omics Random Forest Framework for Robust Biomarker Discovery. It addresses the challenge of extracting key shared biomarkers from multiple omics data types by introducing a multivariate random forest-based approach enhanced by an inverse minimal depth metric.

    I have some concerns and comments below:

    1. The new algorithm described in the study selected omics variables by assigning response variable to decision tree nodes. How the response variables relate to biological responses/outcomes? From the authors' description, it seems that the selected omics variables using the IMD are almighty, i.e., they can predict anything needed, such as prognosis, cancer types, and et al. Actually, the usual logic to select omics variables to predict prognosis is to evaluate the association between omics variables and survival time.
    2. Following the discussion in 1, what is the biological meaning to extract shared biomarkers from multiple data layers? While it is straightforward to think that the shared biomarkers between multiple data layers or data types may induce the same biological responses, the unique biomarkers also matter depending on what biological responses we care.
    3. The Introduction section is not sufficient. The biological significance and technical details of "extract shared biomarkers from multiple data layers" need to be explained in more details.
    4. It is advised to provide some examples of the statement in the Introduction: "may fail to capture nonlinear interactions" of the current methods (sPLS, CCA).
    5. It is also advised to explain and illustrate how the new method proposed in this study addressed the challenge of traditional methods for capturing nonlinear relationships. Ablation study could be one of the choices.
    6. The authors showed that their new approach "uncovered known cancer biological relevant pathways". How about the functional enrichment of genes selected from traditional methods, such as sPLS, CCA?
    7. The authors showed that the selected RNA-seq and ATAC-seq features using the new approach are able to capture the distinction between different cancer types (Figure 8). It is suggested to quantitatively evaluate this capability using metrics of recall, precision, and et al. to calculate how many samples are corrected classified and how many are mis-classified in comparison with other methods.
    8. It is advised to re-find the Discussion. In what scenario their new method can be applied? What biological insights can be obtained and what can be missed by the new method?
    9. The authors did not provide sufficient details about the datasets they used in the section Method. How many samples in TCGA? How many features did they use? How many features left after filtering?
    10. Although the performance of the new approach showed some kind of superior in comparison with other methods, the authors only used the currently known databases. It is advised to apply their approach to additional testing datasets or real-world datasets to increase the confidence of the conclusion of this study. It is also observed that the performance of sPLS is better than others in some cases (Figure 4).
    11. It is suggested to re-fine the figures. The labels and legends are too tiny to be seen.
    12. There is no sub-figure labels a,b,c,d,e,f in Figure 8. The positions of sub-figure labels in Figure 3, Figure 4, Figure 5, Figure 7 are not correct.
  3. GigaScience

    ABSTRACTHigh-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating these diverse data types can yield deeper insights into the biological mechanisms driving complex traits and diseases. Yet, extracting key shared biomarkers from multiple data layers remains a major challenge. We present a multivariate random forest (MRF)–based framework enhanced by a novel inverse minimal depth (IMD) metric for integrative variable selection. By assigning response variables to tree nodes and employing IMD to rank predictors, our approach efficiently identifies essential features across different omics types, even when confronted with high-dimensionality and noise. Through extensive simulations and analyses of multi-omics datasets from The Cancer Genome Atlas, we demonstrate that our method outperforms established integrative techniques in uncovering biologically meaningful biomarkers and pathways. Our findings show that selected biomarkers not only correlate with known regulatory and signaling networks but can also stratify patient subgroups with distinct clinical outcomes. The method’s scalable, interpretable, and user-friendly implementation ensures broad applicability to a range of research questions. This MRF-based framework advances robust biomarker discovery and integrative multi-omics analyses, accelerating the translation of complex molecular data into tangible biological and clinical insights.Competing Interest StatementThe authors have declared no competing interest.FootnotesAuthor Name Correction and Documentation Update.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf148), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 1: Moran Chen

    This manuscript presents a novel multivariate random forest (MRF) framework enhanced by the inverse minimal depth (IMD) metric for integrative multi-omics biomarker discovery. The authors clearly demonstrate the robustness and superiority of the proposed methods through comprehensive simulation studies and validation on TCGA datasets. The manuscript provides clear methodological explanations, offering valuable insights into its practical utility. I recommend accepting the manuscript after minor revisions. Minor Concern:

    1. Biological Interpretation Depth: While the authors identified biologically relevant biomarkers, the biological interpretations remain somewhat superficial. A deeper exploration of novel or less-known biomarkers in the context of disease mechanisms would strengthen the biological relevance of the findings.
    2. Sensitivity Analysis of Randomness: The authors should conduct and discuss sensitivity analyses regarding different random states or random seeds to assess the stability of the method's results.
    3. Comparison with Existing Methods on Real Data: While the simulation studies provide thorough benchmarking, the manuscript could enhance its practical value by including detailed comparisons with methods such as SPLS, PMDCCA, and SGCCA using the real-world TCGA datasets.
    4. Applicability to Other Diseases: The authors primarily focus on cancer datasets. It is recommended to discuss potential applicability to other disease contexts, such as neurodegenerative or immunological diseases, to illustrate broader utility.
    5. Improved Visualization: Some figures in the manuscript have font sizes that are too small, which might impair readability. It is recommended to enlarge the text labels, legends, and axis annotations to ensure that all information is clearly visible and accessible. In Figure 8, the use of sub-labels (such as a, b, c) is mentioned in the text, but these labels are not visible in the figure itself.
  4. Version published to 10.1101/2025.03.05.641533 on bioRxiv