Andreas Leha

Postdoctoral Fellow

 

Alumni

This person is a member of Sanger Institute Alumni.

Andreas Leha is a computational geneticist. His research interests center around the statistical analysis of high-dimensional data from molecular biology. In his current role as part of HipSci, the human induced pluripotent stem cells initiative which aims to provide a resource of hundreds of iPSC lines, he is interested in genetic causes for phenotypic variation in iPSCs.

The focus of my current work is the characterisation of induced pluripotent stem cells (iPSCs). As iPSCs show great potential for many applications from model systems to regenerative medicine, good understanding of their behaviour is vital for their therapeutic use. The human induced stem cells initiative (HipSci) — regarded as “one of the most important resource projects this decade in my (Ewan Birney’s) view” — aims to contribute to that effort by generating a resource of hundreds of iPSC lines.

Specifically I am studying the ability of iPSCs to differentiate as well as diverse cellular phenotypes measured through high content imaging in collaboration with the wattlab at KCL.

Coming from a background in mathematics and computer science I was early on most excited by stochastics/statistics and machine learning. After a first application in robotics and manufacturing (on a project with an intelligent factory) I ventured into the fields of biometry and computational biology/bioinformatics during my PhD where I collaborated on numerous biological and medical studies and developed a novel approach to ordinal classification.

My timeline

 

My publications

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Intensities from immunohistochemistry. This shows sample intensities from one iPSC line that was differentiated towards neuroektoderm. It is a two channel image where the first channel shows DAPI intensity and the second the differentiation marker.

Intensities from immunohistochemistry. This shows sample intensities from one iPSC line that was differentiated towards neuroektoderm. It is a two channel image where the first channel shows DAPI intensity and the second the differentiation marker.

The clinical standard score for Charcot-Marie-Tooth syndrome can be improved by exchanging some of its current subscores (primary) with other secondary subscores.

The clinical standard score for Charcot-Marie-Tooth syndrome can be improved by exchanging some of its current subscores (primary) with other secondary subscores.

miRNA expression of tumor educated macrophages is predictive with respect to disease free survival.

miRNA expression of tumor educated macrophages is predictive with respect to disease free survival.

Comparison of several classifiers capable of doing ordinal classification. This shows results from a simulation with varying effect sizes between the (five) simulated ordinally related groups. The proposed classifier hierarchical twoing (hi2) outperforms its competitors here.

Comparison of several classifiers capable of doing ordinal classification. This shows results from a simulation with varying effect sizes between the (five) simulated ordinally related groups. The proposed classifier hierarchical twoing (hi2) outperforms its competitors here.