Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.

Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies

Cluster validation by measurement of clustering characteristics relevant to the user

There are many cluster analysis methods that can produce quite different clusterings on the same dataset. Cluster validation is about the evaluation of the quality of a clustering; "relative cluster validation" is about using such criteria to compare clusterings. This can be used to select one of a set of clusterings from different methods, or from the same method ran with different parameters such as different numbers of clusters. There are many cluster validation indexes in the literature. Most of them attempt to measure the overall quality of a clustering by a single number, but this can be inappropriate. There are various different characteristics of a clustering that can be relevant in practice, depending on the aim of clustering, such as low within-cluster distances and high between-cluster separation. In this paper, a number of validation criteria will be introduced that refer to different desirable characteristics of a clustering, and that characterise a clustering in a multidimensional way. In specific applications the user may be interested in some of these criteria rather than others. A focus of the paper is on methodology to standardise the different characteristics so that users can aggregate them in a suitable way specifying weights for the various criteria that are relevant in the clustering application at hand.Comment: 20 pages 2 figure

The 3-D clustering of radio galaxies in the TONS survey

We present a clustering analysis of the Texas-Oxford NVSS Structure (TONS) radio galaxy redshift survey. This complete flux-limited survey consists of 268 radio galaxies with spectroscopic redshifts in three separate regions of the sky covering a total of 165 deg^2. By going to faint radio flux densities (s_1.4>3 mJy) but imposing relatively bright optical limits (E R 19.5), the TONS sample is optimised for looking at the clustering properties of low luminosity radio galaxies in a region of moderate (0 < z < 0.5) redshifts. We use the two point correlation function to determine the clustering strength of the combined TONS08 and TONS12 sub-samples and find a clustering strength of r_0(z)=8.7+/-1.6 Mpc (h=0.7). If we assume growth of structure by linear theory and that the median redshift is 0.3, this corresponds to r_0(0)=11.0+/-2.0 Mpc which is consistent with the clustering strength of the underlying host galaxies (~ 2.5 Lstar ellipticals) of the TONS radio galaxy population.Comment: 18 pages, MNRAS accepted. Full paper including all spectra can be found at http://www.noao.edu/noao/staff/brand/brand_corr_fn.ps.g

Role of Alpha Oscillations During Short Time Memory Task Investigated by Graph Based Partitioning

In this study, we investigate the clustering pattern of alpha band (8 Hz - 12 Hz) electroencephalogram (EEG) oscillations obtained from healthy individuals during a short time memory task with 3 different memory loads. The retention period during which subjects were asked to memorize a pattern in a square matrix is analyzed with a graph theoretical approach. The functional coupling among EEG electrodes are quantified via mutual information in the time-frequency plane. A spectral clustering algorithm followed by bootstrapping is used to parcellate memory related circuits and for identifying significant clusters in the brain. The main outcome of the study is that the size of the significant clusters formed by alpha oscillations decreases as the memory load increases. This finding corroborates the active inhibition hypothesis about alpha oscillations