Biology subjects

Yong Wang

Publications and source records attributed to Yong Wang.

3 recordsLinked to original sources

bioRxiv2016

Robust nonparametric descriptors for clustering quantification in single-molecule localization microscopy

We report a robust nonparametric descriptor, J'(r), for quantifying the spatial organization of molecules in singlemolecule localization microscopy. J'(r), based on nearest neighbor distribution functions, does not require any parameter as an input for analyzing point patterns. We show that J'(r) displays a valley shape in the presence of clusters of molecules, and the characteristics of the valley reliably report the clustering features in the data. More importantly, the position of the J'(r) valley (rJ'm) depends exclusively on the density of clustering molecules ({rho}c). Therefore, it is ideal for direct measurements of clustering density of molecules in single-molecule localization microscopy.

Biophysics↗

bioRxiv2016

Molecular Counting with Localization Microscopy: A Redundant Labeling Approach

Super-resolved localization microscopy (SLM) has the potential to serve as an accurate, singlecell technique for counting the abundance of intracellular molecules. However, the stochastic blinking of single fluorophores can introduce large uncertainties into the final count. Here we provide a theoretical foundation for applying SLM to the problem of molecular counting based on the distribution of blinking events from a single fluorophore. We also show that by redundantly tagging single-molecules with multiple, blinking fluorophores, the accuracy of the technique can be enhanced by harnessing the central limit theorem. The coefficient of variation (CV) then, for the number of molecules M estimated from a given number of blinks B, scales like [Formula], where Nl is the mean number of labels on a target. As an example, we apply our theory to the challenging problem of quantifying the cell-to-cell variability of plasmid copy number in bacteria.

Biophysics↗

bioRxiv2016

AC-PCA: simultaneous dimension reduction and adjustment for confounding variation

Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biologically or technically originated. In this study, we extend Principal Component Analysis to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding variation. We show that AC-PCA can adjust for a) variations across individual donors present in a human brain exon array dataset, and b) variations of different species in a model organism ENCODE RNA-Seq dataset. Our approach is able to recover the anatomical structure of neocortical regions, and to capture the shared variation among species during embryonic development. For gene selection purposes, we extend AC-PCA with sparsity constraints, and propose and implement an efficient algorithm. The methods developed in this paper can also be applied to more general settings.

Bioinformatics↗