12,924 research outputs found
Non-parametric Bayesian modelling of digital gene expression data
Next-generation sequencing technologies provide a revolutionary tool for
generating gene expression data. Starting with a fixed RNA sample, they
construct a library of millions of differentially abundant short sequence tags
or "reads", which constitute a fundamentally discrete measure of the level of
gene expression. A common limitation in experiments using these technologies is
the low number or even absence of biological replicates, which complicates the
statistical analysis of digital gene expression data. Analysis of this type of
data has often been based on modified tests originally devised for analysing
microarrays; both these and even de novo methods for the analysis of RNA-seq
data are plagued by the common problem of low replication. We propose a novel,
non-parametric Bayesian approach for the analysis of digital gene expression
data. We begin with a hierarchical model for modelling over-dispersed count
data and a blocked Gibbs sampling algorithm for inferring the posterior
distribution of model parameters conditional on these counts. The algorithm
compensates for the problem of low numbers of biological replicates by
clustering together genes with tag counts that are likely sampled from a common
distribution and using this augmented sample for estimating the parameters of
this distribution. The number of clusters is not decided a priori, but it is
inferred along with the remaining model parameters. We demonstrate the ability
of this approach to model biological data with high fidelity by applying the
algorithm on a public dataset obtained from cancerous and non-cancerous neural
tissues
Clear Visual Separation of Temporal Event Sequences
Extracting and visualizing informative insights from temporal event sequences
becomes increasingly difficult when data volume and variety increase. Besides
dealing with high event type cardinality and many distinct sequences, it can be
difficult to tell whether it is appropriate to combine multiple events into one
or utilize additional information about event attributes. Existing approaches
often make use of frequent sequential patterns extracted from the dataset,
however, these patterns are limited in terms of interpretability and utility.
In addition, it is difficult to assess the role of absolute and relative time
when using pattern mining techniques.
In this paper, we present methods that addresses these challenges by
automatically learning composite events which enables better aggregation of
multiple event sequences. By leveraging event sequence outcomes, we present
appropriate linked visualizations that allow domain experts to identify
critical flows, to assess validity and to understand the role of time.
Furthermore, we explore information gain and visual complexity metrics to
identify the most relevant visual patterns. We compare composite event learning
with two approaches for extracting event patterns using real world company
event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data
Science (VDS), 201
Element-centric clustering comparison unifies overlaps and hierarchy
Clustering is one of the most universal approaches for understanding complex
data. A pivotal aspect of clustering analysis is quantitatively comparing
clusterings; clustering comparison is the basis for many tasks such as
clustering evaluation, consensus clustering, and tracking the temporal
evolution of clusters. In particular, the extrinsic evaluation of clustering
methods requires comparing the uncovered clusterings to planted clusterings or
known metadata. Yet, as we demonstrate, existing clustering comparison measures
have critical biases which undermine their usefulness, and no measure
accommodates both overlapping and hierarchical clusterings. Here we unify the
comparison of disjoint, overlapping, and hierarchically structured clusterings
by proposing a new element-centric framework: elements are compared based on
the relationships induced by the cluster structure, as opposed to the
traditional cluster-centric philosophy. We demonstrate that, in contrast to
standard clustering similarity measures, our framework does not suffer from
critical biases and naturally provides unique insights into how the clusterings
differ. We illustrate the strengths of our framework by revealing new insights
into the organization of clusters in two applications: the improved
classification of schizophrenia based on the overlapping and hierarchical
community structure of fMRI brain networks, and the disentanglement of various
social homophily factors in Facebook social networks. The universality of
clustering suggests far-reaching impact of our framework throughout all areas
of science
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