Diffusion maps for high-dimensional single-cell analysis of differentiation data
Open Access
- 21 May 2015
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 31 (18), 2989-2998
- https://doi.org/10.1093/bioinformatics/btv325
Abstract
Motivation: Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages. Results: Here, we propose the use of diffusion maps to deal with the problem of defining differentiation trajectories. We adapt this method to single-cell data by adequate choice of kernel width and inclusion of uncertainties or missing measurement values, which enables the establishment of a pseudotemporal ordering of single cells in a high-dimensional gene expression space. We expect this output to reflect cell differentiation trajectories, where the data originates from intrinsic diffusion-like dynamics. Starting from a pluripotent stage, cells move smoothly within the transcriptional landscape towards more differentiated states with some stochasticity along their path. We demonstrate the robustness of our method with respect to extrinsic noise (e.g. measurement noise) and sampling density heterogeneities on simulated toy data as well as two single-cell quantitative polymerase chain reaction datasets (i.e. mouse haematopoietic stem cells and mouse embryonic stem cells) and an RNA-Seq data of human pre-implantation embryos. We show that diffusion maps perform considerably better than Principal Component Analysis and are advantageous over other techniques for non-linear dimension reduction such as t-distributed Stochastic Neighbour Embedding for preserving the global structures and pseudotemporal ordering of cells. Availability and implementation: The Matlab implementation of diffusion maps for single-cell data is available at https://www.helmholtz-muenchen.de/icb/single-cell-diffusion-map. Contact:fbuettner.phys@gmail.com, fabian.theis@helmholtz-muenchen.de Supplementary information: Supplementary data are available at Bioinformatics online.Keywords
This publication has 33 references indexed in Scilit:
- Data exploration, quality control and testing in single-cell qPCR-based gene expression experimentsBioinformatics, 2012
- A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocystBioinformatics, 2012
- Stability and Multiattractor Dynamics of a Toggle Switch Based on a Two-Stage Model of Stochastic Gene ExpressionBiophysical Journal, 2012
- Extracting a cellular hierarchy from high-dimensional cytometry data with SPADENature Biotechnology, 2011
- Hierarchical Differentiation of Myeloid Progenitors Is Encoded in the Transcription Factor NetworkPLOS ONE, 2011
- Non-genetic heterogeneity of cells in development: more than just noiseDevelopment, 2009
- Hematopoiesis: An Evolving Paradigm for Stem Cell BiologyCell, 2008
- Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined FactorsCell, 2006
- Purification and unique properties of mammary epithelial stem cellsNature, 2006
- Exact stochastic simulation of coupled chemical reactionsThe Journal of Physical Chemistry, 1977