Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data

Gao, Yilin; Zhu, Zifan; Sun, Fengzhu

doi:10.1016/j.synbio.2022.01.005

Cited by 22 publications

(22 citation statements)

References 47 publications

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“…On the whole, the LODO analysis revealed the random forest models trained on these heterogeneous datasets have solid generalization and robustness to make accurate predictions on other metagenomic CRC studies. The prediction ability (AUROC >0.80) achieved based on gut viral signatures was competitive with that of whole gut microbial signatures (AUROC >0.83) ( Thomas et al., 2019 ; Wirbel et al., 2019 ; Gao et al., 2022 ). Although it can not further enhance the performance of the bacterial signatures, the prediction reuslts still show the important role of viruses in the homeostasis of gut microbiota.…”

Section: Resultsmentioning

confidence: 91%

“…Bacterial signatures of the human gut have been shown to be predictive of CRC status ( Thomas et al., 2019 ; Wirbel et al., 2019 ; Gao et al., 2022 ). To study whether the viral signatures can further enhance the prediction performance of this disease, we combined both bacterial and viral abundance profiles together and re-run the random forest model.…”

Section: Resultsmentioning

confidence: 99%

“…Since the random forests algorithm ( Breiman, 2001 ) has been proven to have better performance than other machine learning models, especially on microbial abundance data ( Pasolli et al., 2016 ; Gao et al., 2022 ), all experiments were carried out using the random forests classifier implemented by the python package scikit-learn v1.0.2 ( Pedregosa et al., 2011 ). We set the number of estimators as 1000 ( Thomas et al., 2019 ; Gao et al., 2022 ) and all other parameters as their default values ( Probst et al., 2019 ) in all prediction experiments. The area under the ROC curve (AUROC) was used as a criterion measuring the performance of every prediction model.…”

Section: Methodsmentioning

confidence: 99%

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Metagenomic Analyses of Multiple Gut Datasets Revealed the Association of Phage Signatures in Colorectal Cancer

Zuo

Michail

Sun

2022

Front. Cell. Infect. Microbiol.

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The association of colorectal cancer (CRC) and the human gut microbiome dysbiosis has been the focus of several studies in the past. Many bacterial taxa have been shown to have differential abundance among CRC patients compared to healthy controls. However, the relationship between CRC and non-bacterial gut microbiome such as the gut virome is under-studied and not well understood. In this study we conducted a comprehensive analysis of the association of viral abundances with CRC using metagenomic shotgun sequencing data of 462 CRC subjects and 449 healthy controls from 7 studies performed in 8 different countries. Despite the high heterogeneity, our results showed that the virome alpha diversity was consistently higher in CRC patients than in healthy controls (p-value <0.001). This finding is in sharp contrast to previous reports of low alpha diversity of prokaryotes in CRC compared to healthy controls. In addition to the previously known association of Podoviridae, Siphoviridae and Myoviridae with CRC, we further demonstrate that Herelleviridae, a newly constructed viral family, is significantly depleted in CRC subjects. Our interkingdom association analysis reveals a less intertwined correlation between the gut virome and bacteriome in CRC compared to healthy controls. Furthermore, we show that the viral abundance profiles can be used to accurately predict CRC disease status (AUROC >0.8) in both within-study and cross-study settings. The combination of training sets resulted in rather generalized and accurate prediction models. Our study clearly shows that subjects with colorectal cancer harbor a distinct human gut virome profile which may have an important role in this disease.

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Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Metagenomic Analyses of Multiple Gut Datasets Revealed the Association of Phage Signatures in Colorectal Cancer

Zuo

Michail

Sun

2022

Front. Cell. Infect. Microbiol.

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“…We compared these 5 ensemble weighted learning methods in our study as well. In addition, we also implemented two other methods combined with RF classifier as implemented in [13]. The detailed ensemble weighted learning methods are described as follows.…”

Section: Methodsmentioning

confidence: 99%

“…Leave-One-Sample-Out (LOSO) AUCs were calculated for all test samples, and then used the corresponding AUC 0.5 as weights to combine predictors from “Training1” and “Training2”. This method was proposed in [13]. …”

Section: Methodsmentioning

confidence: 99%

Batch Normalization Followed by Merging Is Powerful for Phenotype Prediction Integrating Multiple Heterogeneous Studies

Gao

Sun

2022

Preprint

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Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifier's reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations presented, and could successfully remove batch effects within the same population. We also showed that the machine learning classifier's prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches.

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Optimized Machine Learning for Classifying Colorectal Tissues

et al. 2023

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Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data

Cited by 22 publications

References 47 publications

Metagenomic Analyses of Multiple Gut Datasets Revealed the Association of Phage Signatures in Colorectal Cancer

Metagenomic Analyses of Multiple Gut Datasets Revealed the Association of Phage Signatures in Colorectal Cancer

Batch Normalization Followed by Merging Is Powerful for Phenotype Prediction Integrating Multiple Heterogeneous Studies

Optimized Machine Learning for Classifying Colorectal Tissues

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