  Flow cytometry (FC) is a single-cell profiling platform for measuring the
phenotypes of individual cells from millions of cells in biological samples. FC
employs high-throughput technologies and generates high-dimensional data, and
hence algorithms for analyzing the data represent a bottleneck. This
dissertation addresses several computational challenges arising in modern
cytometry while mining information from high-dimensional and high-content
biological data. A collection of combinatorial and statistical algorithms for
locating, matching, prototyping, and classifying cellular populations from
multi-parametric FC data is developed.
  The algorithmic pipeline, flowMatch, developed in this dissertation consists
of five well-defined algorithmic modules to (1) transform data to stabilize
within-population variance, (2) identify cell populations by robust clustering
algorithms, (3) register cell populations across samples, (4) encapsulate a
class of samples with templates, and (5) classify samples based on their
similarity with the templates. Components of flowMatch can work independently
or collaborate with each other to perform the complete data analysis. flowMatch
is made available as an open-source R package in Bioconductor.
  We have employed flowMatch for classifying leukemia samples, evaluating the
phosphorylation effects on T cells, classifying healthy immune profiles, and
classifying the vaccination status of HIV patients. In these analyses, the
pipeline is able to reach biologically meaningful conclusions quickly and
efficiently with the automated algorithms. The algorithms included in flowMatch
can also be applied to problems outside of flow cytometry such as in microarray
data analysis and image recognition. Therefore, this dissertation contributes
to the solution of fundamental problems in computational cytometry and related
domains.
