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

Emilie, Eliseussen,; Fleischer, Thomas; Vitelli, Valeria

doi:10.48550/arxiv.2107.05072

Cited by 2 publications

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“…The method's limitation to ~ 1000 genes also puts limitations on the biological interpretation of cluster‐associated gene lists, as different selections of genes will give different results in a gsea . We are currently working on a dimension reduction version of the method, which will address this limitation [ 38 ].…”

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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“…In our current approach, we estimate the rankings of all items in each iteration, instead of focusing only on a subset of relevant items. In our Pseudo-Mallows algorithm, the ranking for each item is sampled sequentially, and it is possible to sample only a top part of the items, similarly to what has been proposed for the Bayesian Mallows MCMC in Eliseussen et al (2021). Clearly, the overall computation time can be greatly reduced.…”

Section: Pseudo Mallowsmentioning

confidence: 99%

Pseudo-Mallows for Efficient Probabilistic Preference Learning

Liu¹,

Vitelli²,

Mannino³

et al. 2022

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We propose the Pseudo-Mallows distribution over the set of all permutations of n items, to approximate the posterior distribution with a Mallows likelihood. The Mallows model has been proven to be useful for recommender systems where it can be used to learn personal preferences from highly incomplete data provided by the users. Inference based on MCMC is however slow, preventing its use in real time applications. The Pseudo-Mallows distribution is a product of univariate discrete Mallows-like distributions, constrained to remain in the space of permutations. The quality of the approximation depends on the order of the n items used to determine the factorization sequence. In a variational setting, we optimise the variational order parameter by minimising a marginalized KL-divergence. We propose an approximate algorithm for this discrete optimization, and conjecture a certain form of the optimal variational order that depends on the data. Empirical evidence and some

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

Cited by 2 publications

References 0 publications

Transcriptomic pan‐cancer analysis using rank‐based Bayesian inference

Transcriptomic pan‐cancer analysis using rank‐based Bayesian inference

Pseudo-Mallows for Efficient Probabilistic Preference Learning

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