# Topslam: Waddington Landscape Recovery for Single Cell Experiments

Max Zwiessele, University of Sheffield
Neil D. Lawrence, University of Sheffield

#### Abstract

We present an approach to estimating the nature of the Waddington (or epigenetic) landscape that underlies a population of individual cells. Through exploiting high resolution single cell transcription experiments we show that cells can be located on a landscape that reflects their differentiated nature. Our approach makes use of probabilistic non-linear dimensionality reduction that respects the topology of our estimated epigenetic landscape. In simulation studies and analyses of real data we show that the approach, known as , outperforms previous attempts to understand the differentiation landscape. Hereby, the novelty of our approach lies in the correction of distances before extracting ordering information. This gives the advantage over other attempts, which have to correct for extracted time lines by post processing or additional data.

  @TechReport{zwiessele-topslam16, title = {Topslam: Waddington Landscape Recovery for Single Cell Experiments}, author = {Max Zwiessele and Neil D. Lawrence}, year = {2016}, institution = {}, month = {00}, edit = {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2016-06-20-zwiessele-topslam16.md}, url = {http://inverseprobability.com/publications/zwiessele-topslam16.html}, abstract = {We present an approach to estimating the nature of the Waddington (or epigenetic) landscape that underlies a population of individual cells. Through exploiting high resolution single cell transcription experiments we show that cells can be located on a landscape that reflects their differentiated nature. Our approach makes use of probabilistic non-linear dimensionality reduction that respects the topology of our estimated epigenetic landscape. In simulation studies and analyses of real data we show that the approach, known as , outperforms previous attempts to understand the differentiation landscape. Hereby, the novelty of our approach lies in the correction of distances *before* extracting ordering information. This gives the advantage over other attempts, which have to correct for extracted time lines by post processing or additional data.}, key = {Zwiessele:topslam16}, doi = {10.1101/057778}, linkpdf = {http://biorxiv.org/content/early/2016/06/08/057778.full.pdf}, OPTgroup = {} }
 %T Topslam: Waddington Landscape Recovery for Single Cell Experiments %A Max Zwiessele and Neil D. Lawrence %B %D %F zwiessele-topslam16 %P -- %R 10.1101/057778 %U http://inverseprobability.com/publications/zwiessele-topslam16.html %X We present an approach to estimating the nature of the Waddington (or epigenetic) landscape that underlies a population of individual cells. Through exploiting high resolution single cell transcription experiments we show that cells can be located on a landscape that reflects their differentiated nature. Our approach makes use of probabilistic non-linear dimensionality reduction that respects the topology of our estimated epigenetic landscape. In simulation studies and analyses of real data we show that the approach, known as , outperforms previous attempts to understand the differentiation landscape. Hereby, the novelty of our approach lies in the correction of distances *before* extracting ordering information. This gives the advantage over other attempts, which have to correct for extracted time lines by post processing or additional data. 
 TY - CPAPER TI - Topslam: Waddington Landscape Recovery for Single Cell Experiments AU - Max Zwiessele AU - Neil D. Lawrence PY - 2016/06/20 DA - 2016/06/20 ID - zwiessele-topslam16 SP - EP - DO - 10.1101/057778 L1 - http://biorxiv.org/content/early/2016/06/08/057778.full.pdf UR - http://inverseprobability.com/publications/zwiessele-topslam16.html AB - We present an approach to estimating the nature of the Waddington (or epigenetic) landscape that underlies a population of individual cells. Through exploiting high resolution single cell transcription experiments we show that cells can be located on a landscape that reflects their differentiated nature. Our approach makes use of probabilistic non-linear dimensionality reduction that respects the topology of our estimated epigenetic landscape. In simulation studies and analyses of real data we show that the approach, known as , outperforms previous attempts to understand the differentiation landscape. Hereby, the novelty of our approach lies in the correction of distances *before* extracting ordering information. This gives the advantage over other attempts, which have to correct for extracted time lines by post processing or additional data. ER - 
 Zwiessele, M. & Lawrence, N.D.. (2016). Topslam: Waddington Landscape Recovery for Single Cell Experiments.:-