Topslam: Waddington Landscape Recovery for Single Cell Experiments

Max Zwiessele, Neil D. Lawrence
, 2016.

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.

Cite this Paper

BibTeX
@Misc{Zwiessele-topslam16, title = {Topslam: Waddington Landscape Recovery for Single Cell Experiments}, author = {Zwiessele, Max and Lawrence, Neil D.}, year = {2016}, doi = {10.1101/057778}, pdf = {http://biorxiv.org/content/early/2016/06/08/057778.full.pdf}, 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.} }
Endnote
%0 Generic %T Topslam: Waddington Landscape Recovery for Single Cell Experiments %A Max Zwiessele %A Neil D. Lawrence %D 2016 %F Zwiessele-topslam16 %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.
RIS
TY - GEN TI - Topslam: Waddington Landscape Recovery for Single Cell Experiments AU - Max Zwiessele AU - Neil D. Lawrence DA - 2016/06/20 ID - Zwiessele-topslam16 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 -
APA
Zwiessele, M. & Lawrence, N.D.. (2016). Topslam: Waddington Landscape Recovery for Single Cell Experiments. doi:10.1101/057778 Available from http://inverseprobability.com/publications/zwiessele-topslam16.html.

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