Topslam: Waddington Landscape Recovery for Single Cell Experiments

Max ZwiesseleNeil 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
@InProceedings{pmlr-v-zwiessele-topslam16, title = {Topslam: Waddington Landscape Recovery for Single Cell Experiments}, author = {Max Zwiessele and Neil D. Lawrence}, year = {}, editor = {}, 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 Conference Paper %T Topslam: Waddington Landscape Recovery for Single Cell Experiments %A Max Zwiessele %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-zwiessele-topslam16 %I PMLR %J Proceedings of Machine Learning Research %P -- %R 10.1101/057778 %U http://inverseprobability.com %V %W PMLR %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 - CPAPER TI - Topslam: Waddington Landscape Recovery for Single Cell Experiments AU - Max Zwiessele AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-zwiessele-topslam16 PB - PMLR SP - DP - PMLR EP - DO - 10.1101/057778 L1 - 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.. (). Topslam: Waddington Landscape Recovery for Single Cell Experiments. , in PMLR :-

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