Pseudo-Mallows for Efficient Probabilistic Preference Learning

Liu, Qinghua; Vitelli, Valeria; Mannino, Carlo; Frigessi, Arnoldo; Scheel, Ida

doi:10.48550/arxiv.2205.13911

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The average computing time for Bayes Mallows with C = 16 was 9 h12’, while mixtures with a smaller (larger) number of groups require slightly less (more) computing time. We are currently working on a Variational Bayes alternative to the MCMC algorithm [ 37 ] that samples efficiently from an approximate model; this will significantly speed up computation, as this alternative implementation is scalable in the number of features and of mixture components.…”

Section: Discussionmentioning

confidence: 99%

Transcriptomic pan‐cancer analysis using rank‐based Bayesian inference

et al. 2023

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The analysis of whole genomes of pan‐cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank‐based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top‐ranked genomic features. We applied our method to RNA‐seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan‐cancer clusters. Importantly, we identified three pan‐squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over‐represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan‐cancer samples.

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Section: Discussionmentioning

confidence: 99%

Transcriptomic pan‐cancer analysis using rank‐based Bayesian inference

et al. 2023

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“…Nonetheless, we would still need an estimation of its most reasonable value when we are given a new ranking dataset. We propose one possible method for estimating such a value, inspired by Liu et al 34 …”

Section: Lower‐dimensional Bayesian Mallows Modelmentioning

confidence: 99%

Rank‐based Bayesian variable selection for genome‐wide transcriptomic analyses

Emilie

Fleischer

Vitelli

2022

Statistics in Medicine

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Variable selection is crucial in high‐dimensional omics‐based analyses, since it is biologically reasonable to assume only a subset of non‐noisy features contributes to the data structures. However, the task is particularly hard in an unsupervised setting, and a priori ad hoc variable selection is still a very frequent approach, despite the evident drawbacks and lack of reproducibility. We propose a Bayesian variable selection approach for rank‐based unsupervised transcriptomic analysis. Making use of data rankings instead of the actual continuous measurements increases the robustness of conclusions when compared to classical statistical methods, and embedding variable selection into the inferential tasks allows complete reproducibility. Specifically, we develop a novel extension of the Bayesian Mallows model for variable selection that allows for a full probabilistic analysis, leading to coherent quantification of uncertainties. Simulation studies demonstrate the versatility and robustness of the proposed method in a variety of scenarios, as well as its superiority with respect to several competitors when varying the data dimension or data generating process. We use the novel approach to analyze genome‐wide RNAseq gene expression data from ovarian cancer patients: several genes that affect cancer development are correctly detected in a completely unsupervised fashion, showing the usefulness of the method in the context of signature discovery for cancer genomics. Moreover, the possibility to also perform uncertainty quantification plays a key role in the subsequent biological investigation.

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Pseudo-Mallows for Efficient Probabilistic Preference Learning

Cited by 2 publications

References 6 publications

Transcriptomic pan‐cancer analysis using rank‐based Bayesian inference

Transcriptomic pan‐cancer analysis using rank‐based Bayesian inference

Rank‐based Bayesian variable selection for genome‐wide transcriptomic analyses

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