bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: O5
Oral presentation
10th Benelux Bioinformatics Conference bbc 2015
O5. INFERRING DEVELOPMENTAL CHRONOLOGIES FROM SINGLE CELL
RNA
Robrecht Cannoodt 1,2,3* , Katleen De Preter 3 & Yvan Saeys 1,2 .
Data Mining and Modelling for Biomedicine group, VIB Inflammation Research Center, Ghent 1 ; Department of
Respiratory Medicine, Ghent University Hospital, Ghent 2 ; Center of Medical Genetics, Ghent University Hospital,
Ghent 3 . * robrecht.cannoodt@ugent.be
With the advent of single cell RNA sequencing, it is now possible to analyse the transcriptomes of hundreds of individual
cells in an unbiased manner. Reconstructing the developmental chronology of differentiating cells is a challenging task,
and doing so in a unsupervised and robust manner is a hitherto untackled problem. We developed a truly unsupervised
developmental chronology inference technique, and evaluated its performance and robustness using multiple datasets.
INTRODUCTION
Early attempts at inferring the chronologies of single cells
are MONOCLE (Trapnell et al., 2014) and NBOR
(Schlitzer et al., 2015). However, these techniques are not
unsupervised as they require knowledge of the cell type of
each cell prior to analysis, which biases the results to prior
knowledge and possibly obstructs the discovery of novel
subpopulations.
METHODS
Our approach consists of four steps.
In the first step, the feature space (~30000 genes) is
reduced to three dimensions.
Secondly, outliers are detected and removed, using a K-
nearest neighbour approach. After outlier removal, the
original feature space is again reduced to three dimensions.
Next, a nonparametric nonlinear curve is iteratively fitted
to the data.
Finally, each cell is projected onto the curve, thus
resulting in a cell chronology.
RESULTS & DISCUSSION
A single-cell RNAseq dataset (Schlitzer et al., 2015)
contains profilings of DC progenitor cells. These cells are
expected to differentiate from MDP to CDP to PreDC. Our
method is able to intuitively visualise known population
groups (Figure 1), as well as infer the developmental
chronology of the individual cells (Figure 2).
We evaluated our method on four datasets (Shalek et al.,
2014; Trapnell et al., 2014; Buettner et al., 2015 and
Schlitzer et al., 2015), and found it to perform better and
more robustly than existing methods MONOCLE and
NBOR.
This approach opens opportunities to further study known
mechanisms or investigate unknown key regulatory
structures in cell differentiation, or detect novel
subpopulations in a truly unsupervised manner.
REFERENCES
Buettner F et al. Nature Biotechnology 33, 155-160 (2015).
Schlitzer A et al. Nature Immunology 16, 718-726 (2015).
Shalek A et al. Nature 509, 363-369 (2014).
Trapnell C et al. Nature Biotechnology 32, 381-386 (2014).
FIGURE 1. After feature space reduction and outlier detection of 244 DC
progenitor cells (Schlitzer et al., 2015), our method can intuitively
visualise known populations.
FIGURE 2. An iterative curve fitting results in a smooth curve reflecting
the developmental chronology. After projecting each cell to the curve,
regulatory patterns in expression which correlate with this timeline can
be investigated.
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