Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Structure learning for zero-inflated counts, with an application to
  single-cell RNA sequencing data   The problem of estimating the structure of a graph from observed data is of
growing interest in the context of high-throughput genomic data, and
single-cell RNA sequencing in particular. These, however, are challenging
applications, since the data consist of high-dimensional counts with high
variance and over-abundance of zeros. Here, we present a general framework for
learning the structure of a graph from single-cell RNA-seq data, based on the
zero-inflated negative binomial distribution. We demonstrate with simulations
that our approach is able to retrieve the structure of a graph in a variety of
settings and we show the utility of the approach on real data.

31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
1
The categories of this paper are: cs
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th