Testing Equality of Cell Populations Based on Shape and Geodesic Distance

STATISTICAL COMPARISONS OF CHROMOSOMAL SHAPE POPULATIONS

This paper develops statistical tools for testing differences in shapes of chromosomes resulting from certain gene knockouts (KO), specifically RIF1 gene KO (RKO) and the cohesin subunit RAD21 gene KO (CKO). It utilizes a two-sample test for comparing shapes of KO chromosomes with wild type (WT) at two levels: (1) Coarse shape analysis, where one compares shapes of full or large parts of chromosomes, and (2) Fine shape analysis, where chromosomes are first segmented into (TAD-based) pieces and then the corresponding pieces are compared across populations. The shape comparisons - coarse and fine - are based on an elastic shape metric for comparing shapes of 3D curves. The experiments show that the KO populations, RKO and CKO, have statistically significant differences from WT at both coarse and fine levels. Furthermore, this framework highlights local regions where these differences are most prominent.

A New Kind of Nonparametric Test for Statistical Comparison of Multiple Classifiers Over Multiple Datasets

Nonparametric statistical analysis, such as the Friedman test (FT), is gaining more and more attention due to its useful applications in a lot of experimental studies. However, traditional FT for the comparison of multiple learning algorithms on different datasets adopts the naive ranking approach. The ranking is based on the average accuracy values obtained by the set of learning algorithms on the datasets, which neither considers the differences of the results obtained by the learning algorithms on each dataset nor takes into account the performance of the learning algorithms in each run. In this paper, we will first propose three kinds of ranking approaches, which are the weighted ranking approach, the global ranking approach (GRA), and the weighted GRA. Then, a theoretical analysis is performed to explore the properties of the proposed ranking approaches. Next, a set of the modified FTs based on the proposed ranking approaches are designed for the comparison of the learning algorithms. Finally, the modified FTs are evaluated through six classifier ensemble approaches on 34 real-world datasets. The experiments show the effectiveness of the modified FTs.

Joint modeling of cell and nuclear shape variation

Modeling cell shape variation is critical to our understanding of cell biology. Previous work has demonstrated the utility of nonrigid image registration methods for the construction of nonparametric nuclear shape models in which pairwise deformation distances are measured between all shapes and are embedded into a low-dimensional shape space. Using these methods, we explore the relationship between cell shape and nuclear shape. We find that these are frequently dependent on each other and use this as the motivation for the development of combined cell and nuclear shape space models, extending nonparametric cell representations to multiple-component three-dimensional cellular shapes and identifying modes of joint shape variation. We learn a first-order dynamics model to predict cell and nuclear shapes, given shapes at a previous time point. We use this to determine the effects of endogenous protein tags or drugs on the shape dynamics of cell lines and show that tagged C1QBP reduces the correlation between cell and nuclear shape. To reduce the computational cost of learning these models, we demonstrate the ability to reconstruct shape spaces using a fraction of computed pairwise distances. The open-source tools provide a powerful basis for future studies of the molecular basis of cell organization.