Markov chain Monte Carlo for active module identification problem

Abstract

Background Integrative network methods are commonly used for interpretation of high-throughput experimental biological data: transcriptomics, proteomics, metabolomics and others. One of the common approaches is finding a connected subnetwork of a global interaction network that best encompasses significant individual changes in the data and represents a so-called active module. Usually methods implementing this approach find a single subnetwork and thus solve a hard classification problem for vertices. This subnetwork inherently contains erroneous vertices, while no instrument is provided to estimate the confidence level of any particular vertex inclusion. To address this issue, in the current study we consider the active module problem as a soft classification problem. Results We propose a method to estimate probabilities of each vertex to belong to the active module based on Markov chain Monte Carlo (MCMC) subnetwork sampling. As an example of the performance of our method on real data, we run it on two gene expression datasets. For the first many-replicate expression dataset we show that the proposed approach is consistent with an existing resampling-based method. On the second dataset the jackknife resampling method is inapplicable due to the small number of biological replicates, but the MCMC method can be run and shows high classification performance. Conclusions The proposed method allows to estimate the probability that an individual vertex belongs to the active module as well as the false discovery rate (FDR) for a given set of vertices. Given the estimated probabilities, it becomes possible to provide a connected subgraph in a consistent manner for any given FDR level: no vertex can disappear when the FDR level is relaxed. We show, on both simulated and real datasets, that the proposed method has good computational performance and high classification accuracy.

Quantitative model for assessment of lower‐extremity perfusion in patients with diabetes

“…In clinical practice, many diabetes patients with severe vascular stenosis show no or only mild symptoms of limb ischemia, while some patients with normal ABI present with typical ischemia symptoms 10–13 . We tried to explain this phenomenon using perfusion kinetics indices.…”

“…The original data of the right and left lower limbs in each group were collected. We used Markov Chain Monte Carlo algorithm programming in MATLAB (2014b, MathWorks Inc. USA) 10 to simulate the parameters in the model. API and BPI were estimated using the model parameters.…”

“…In clinical practice, many diabetes patients with severe vascular stenosis show no or only mild symptoms of limb ischemia, while some patients with normal ABI present with typical ischemia symptoms. [10][11][12][13] We tried to explain this phenomenon using perfusion kinetics indices. Specifically, we selected four representative patients from the LEAD_DM group and evaluated their clinical manifestations (Table 3).…”

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

“…Markov chains are a very popular methodology to model change overtime, in which a transition probability is estimated, and the chain is executed overtime to generate long-term predictions [18][19][20][21][22] .…”

LSB: Local Self-Balancing MCMC in Discrete Spaces

“…Sampling from complex and intractable probability distributions is of fundamental importance for learning and inference [MacKay, 2003]. MCMC algorithms are promising solutions to handle the intractability of sampling in high dimensions and they have found numerous applications, in Bayesian statistics and statistical physics [Neal, 1993, Robert andCasella, 2013], bioinformatics and computational biology [Altekar et al, 2004, Alexeev et al, 2020 as well as machine learning [Andrieu et al, 2003, Koller and Friedman, 2009, Nijkamp et al, 2020.…”