Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast approximate inference for variable selection in Dirichlet process
  mixtures, with an application to pan-cancer proteomics   The Dirichlet Process (DP) mixture model has become a popular choice for
model-based clustering, largely because it allows the number of clusters to be
inferred. The sequential updating and greedy search (SUGS) algorithm (Wang and
Dunson, 2011) was proposed as a fast method for performing approximate Bayesian
inference in DP mixture models, by posing clustering as a Bayesian model
selection (BMS) problem and avoiding the use of computationally costly Markov
chain Monte Carlo methods. Here we consider how this approach may be extended
to permit variable selection for clustering, and also demonstrate the benefits
of Bayesian model averaging (BMA) in place of BMS. Through an array of
simulation examples and well-studied examples from cancer transcriptomics, we
show that our method performs competitively with the current state-of-the-art,
while also offering computational benefits. We apply our approach to
reverse-phase protein array (RPPA) data from The Cancer Genome Atlas (TCGA) in
order to perform a pan-cancer proteomic characterisation of 5,157 tumour
samples. We have implemented our approach, together with the original SUGS
algorithm, in an open-source R package named sugsvarsel, which accelerates
analysis by performing intensive computations in C++ and provides automated
parallel processing. The R package is freely available from:
https://github.com/ococrook/sugsvarsel

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The categories of this paper are: physics
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
1
The categories of this paper are: cs
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat