  Flow cytometry is a high-throughput technology used to quantify multiple
surface and intracellular markers at the level of a single cell. This enables
to identify cell sub-types, and to determine their relative proportions.
Improvements of this technology allow to describe millions of individual cells
from a blood sample using multiple markers. This results in high-dimensional
datasets, whose manual analysis is highly time-consuming and poorly
reproducible. While several methods have been developed to perform automatic
recognition of cell populations, most of them treat and analyze each sample
independently. However, in practice, individual samples are rarely independent
(e.g. longitudinal studies). Here, we propose to use a Bayesian nonparametric
approach with Dirichlet process mixture (DPM) of multivariate skew
$t$-distributions to perform model based clustering of flow-cytometry data. DPM
models directly estimate the number of cell populations from the data, avoiding
model selection issues, and skew $t$-distributions provides robustness to
outliers and non-elliptical shape of cell populations. To accommodate repeated
measurements, we propose a sequential strategy relying on a parametric
approximation of the posterior. We illustrate the good performance of our
method on simulated data, on an experimental benchmark dataset, and on new
longitudinal data from the DALIA-1 trial which evaluates a therapeutic vaccine
against HIV. On the benchmark dataset, the sequential strategy outperforms all
other methods evaluated, and similarly, leads to improved performance on the
DALIA-1 data. We have made the method available for the community in the R
package NPflow.
