Andrews, Tallulah S.
As a post-doctoral fellow in the Hemberg group, I'm currently developping statistical tools for analyzing single-cell RNASeq data. My main focus is on inferring regulatory networks from scRNASeq by taking advantage of the high levels of biological heterogeneity between single cells. Here I am employing various parametric and non-parametric statistical tests to capture both the significance and the nature of regulatory interactions.
A key issue in this project is differentiating the high levels of technical noise from the underlying biological variation present in scRNASeq. Thus, I'm interested in normalization methods for scRNASeq and the sensitivity of different analyses to the normalization method applied. In addition, I'm actively involved in developing novel methods to interpret and correct for the high frequency of drop-outs, cases where a cell has no reads mapping to a particular gene, observed in single-cell data.
The clustering of functionally related genes contributes to CNV-mediated disease.
Genome research 2015;25;6;802-13
Gene networks underlying convergent and pleiotropic phenotypes in a large and systematically-phenotyped cohort with heterogeneous developmental disorders.
PLoS genetics 2015;11;3;e1005012
GeneNet Toolbox for MATLAB: a flexible platform for the analysis of gene connectivity in biological networks.
Bioinformatics (Oxford, England) 2015;31;3;442-4