bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P62. FLOREMI: SURVIVAL TIME PREDICTION
BASED ON FLOW CYTOMETRY DATA
Sofie Van Gassen 1,2,3* , Celine Vens 2,3,4 , Tom Dhaene 1 , Bart N. Lambrecht 2,3 & Yvan Saeys 2,3 .
Department of Information Technology, Ghent University—iMinds 1 ; VIB Inflammation Research Center 2 ; Department of
Respiratory Medicine, Ghent University 3 ; Department of Public Health and Primary Care, kU Leuven Kulak 4 .
* sofie.vangassen@irc.vib-ugent.be
Flow cytometry is a high-throughput technique for single cell analysis. It enables researchers and pathologists to study
blood and tissue samples by measuring several cell properties, such as cell size, granularity and the presence of cellular
markers. While this technique provides a wealth of information, it becomes hard to analyze all data manually. To
investigate alternative automatic analysis methods, the FlowCAP challenges were organized. We will present an
algorithm that obtained the best results on the FlowCAP IV challenge, predicting the time of progression to AIDS for
HIV patients.
INTRODUCTION
The main task of the most recent FlowCAP IV challenge
was a survival modeling challenge: participants had to
predict the time of progression to AIDS for HIV patients,
based on flow cytometry data of an unstimulated and a
stimulated blood sample. Additionally, a secondary task
was the identification of cell populations that could be
indicative of this progression rate. Several challenges
needed to be taken into account: the raw dataset was about
20GB large and about eighty percent of the survival times
were censored.
METHODS
We developed a new algorithm, FloReMi, which
combined several preprocessing steps with a density based
clustering algorithm, a feature selection step and a random
survival forest (Van Gassen et al., 2015).
The input for our algorithm consisted of 2 flow cytometry
samples for each patient: one unstimulated PBMC sample
and one PBMC sample stimulated with HIV antigens. For
each of these samples, 16 parameters were measured for
hundreds of thousands of cells.
First, we included quality control to remove erroneous
measurements from the samples. We also made an
automatic selection of live T cells to focus on the cells of
interest in this specific flow cytometry staining.
Once the dataset was cleaned up, we extracted features for
each patient. This was done by clustering the cells using
the flowDensity (Malek et al., 2015) and flowType
algorithms (Aghaeepour et al., 2012). These algorithms
divide the values for each feature into either “high” or
“low” and use all combinatorial options of “high”, “low”
or “neutral” marker values to group the cells. This resulted
in 3 10 different cell subsets.
For each of these subsets, we computed the number of
cells assigned to it and the mean fluorescence intensity for
13 markers. Per patient, we collected these numbers for
both samples and also computed the differences between
the two. This resulted in a total of 2,480,058 features per
patient.
Because traditional machine learning algorithms cannot
handle this amount of features, we then applied a feature
selection step. To estimate the usefulness of a feature, we
applied a Cox proportional hazards model on each feature.
The resulting p-value indicates how well the feature
corresponds with the known survival times for the training
set. We ordered the features based on these scores, and
picked only those that were uncorrelated with the others.
This resulted in a final selection of 13 features, on which
we applied several machine learning techniques. We
compared the results of the Cox Proportional Hazards
model, the Additive Hazards model and the Random
Survival Forest.
RESULTS & DISCUSSION
All three methods performed well on the training dataset.
However, on the test dataset, both the Cox Proportional
Hazards model and the Additive Hazards model obtained
bad results, probably due to overfitting on the training data.
Only the Random Survival Forest obtained good results on
the test dataset (Figure 1). This method outperformed all
other methods submitted to the challenge.
FIGURE 1. On the training dataset, there was a strong correlation
between the scores and the actual survival times for all models. On the
test dataset, only the Random Survival Forest performed well.
One important challenge remains: the biological
interpretation of our final features. Although they correlate
with the transition times from HIV to AIDS, it is hard to
interpret them as known cell types, due to our
unsupervised feature extraction. Our method delivers a
first step towards new insights in the progress from HIV to
AIDS.
REFERENCES
Malek M et al. Bioinformatics 31.4, 606-607 (2015).
Aghaeepour N et al. Bioinformatics 28, 1009-1016 (2012).
Van Gassen S et al. Cytometry A, DOI 10.1002/cyto.a.22734
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