16,055 research outputs found
Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
In systems biomedicine, an experimenter encounters different potential
sources of variation in data such as individual samples, multiple experimental
conditions, and multi-variable network-level responses. In multiparametric
cytometry, which is often used for analyzing patient samples, such issues are
critical. While computational methods can identify cell populations in
individual samples, without the ability to automatically match them across
samples, it is difficult to compare and characterize the populations in typical
experiments, such as those responding to various stimulations or distinctive of
particular patients or time-points, especially when there are many samples.
Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous
modeling and registration of populations across a cohort. JCM models every
population with a robust multivariate probability distribution. Simultaneously,
JCM fits a random-effects model to construct an overall batch template -- used
for registering populations across samples, and classifying new samples. By
tackling systems-level variation, JCM supports practical biomedical
applications involving large cohorts
EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions
This paper presents an R package EMMIXcskew for the fitting of the canonical
fundamental skew t-distribution (CFUST) and finite mixtures of this
distribution (FM-CFUST) via maximum likelihood (ML). The CFUST distribution
provides a flexible family of models to handle non-normal data, with parameters
for capturing skewness and heavy-tails in the data. It formally encompasses the
normal, t, and skew-normal distributions as special and/or limiting cases. A
few other versions of the skew t-distributions are also nested within the CFUST
distribution. In this paper, an Expectation-Maximization (EM) algorithm is
described for computing the ML estimates of the parameters of the FM-CFUST
model, and different strategies for initializing the algorithm are discussed
and illustrated. The methodology is implemented in the EMMIXcskew package, and
examples are presented using two real datasets. The EMMIXcskew package contains
functions to fit the FM-CFUST model, including procedures for generating
different initial values. Additional features include random sample generation
and contour visualization in 2D and 3D
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