Quantification and Analysis of Complex Behaviors in Zebrafish Using Argus
Matthew
a
Scicluna ,
Soaleha
b
Shams ,
& Robert
b,c
Gerlai
a Department
of Mathematical and Computational Sciences, bDepartment Cell & Systems Biology,
cDepartment of Psychology, University of Toronto Mississauga, Mississauga, ON, Canada
INTRODUCTION
ZEBRAFISH
• Zebrafish (Danio Ranio) are a model organism for vertebrate
behavior due to the ease of their maintenance and their (relative)
evolutionary closeness to humans.
• This species is reproductively prolific, and is an ideal choice to be
used for experiments involving high throughput screening of
behavioral patterns.
• They exhibit a range of quantifiable behaviors whose expression
under various environmental manipulations can help us understand
the functional relevance of vertebrate behaviour
ARGUS
• A computer program that recognizes complex behavioral patterns
and quantifies them meaningfully.
• Argus utilizes a previously created program called the
RealFishTracker, which tracks movement of each fish and outputs
x and y coordinates indicating their location in 2-dimentions.
• Argus uses these coordinates to calculate behavioral variables
such as speed, distance traveled, and distance to any specified
stimulus.
RATIONALE
• Argus was made to work with large data sets consistent with high
throughput sequencing, so it can handle large data sets with ease,
producing result summaries for multiple trials simultaneously.
• Argus is a free, specialized alternative to more expensive and
limited commercially available software packages.
RESEARCH QUESTION
• Does Argus produce similar output to other commercially available
software packages like Ethovision by Noldus?
Conclusion
RESULT
A vs. RFT
A vs. E
RFT vs. E
A vs. RFT
A vs. E
RFT vs. E
0.33
0.05
0.17
0.06
0.34
0.19
0.23
0.62
0.75
0.00
0.00
0.08
0.68
0.78
0.31
0.01
0.00
0.38
0.46
0.99
0.02
1.00
0.11
0.02
0.09
0.26
0.90
0.99
0.62
0.75
0.38
0.87
0.15
0.18
0.33
0.26
0.53
0.70
0.31
0.23
0.54
0.77
0.80
0.60
0.16
0.99
0.18
0.79
0.55
0.95
0.01
0.00
0.01
0.23
0.29
0.24
0.05
0.00
0.01
0.98
0.52
0.20
0.13
0.01
0.00
0.50
0.49
0.55
0.72
1.00
0.07
0.29
0.30
0.17
0.18
1.00
0.98
0.87
0.99
1.00
0.92
0.05
0.02
0.99
0.70
0.95
0.55
0.00
0.16
0.00
0.16
0.32
0.36
0.59
0.29
0.04
Next Steps
• We are going to continue testing Argus and modifying it until it can
replicate the results of the current standards at least as well as any other
software packages commercially available.
• Since the program was made in R there is no limit to the changes we
can do to it.
• We will continue to add more exotic and specialized commands to it to
give Argus a more robust functionality.
• We are beginning to explore the possibility of using neural networks
with a hidden Markov model to do more complex behavioral screening.
Currently there are no programs available that do this well.
• We are exploring the possibility of implementing a more user-friendly
interface.
REFERENCES
• Ljung, G & Box, G (1978). On a measure of lack of fit in time series
models. Biometrika , 65(2), 297-303.
• Kokel, D, et al. (2012) Behavioral barcoding in the cloud: Embracing
data-intensive digital phenotyping in neuropharmacology. Trends
Biotechnol. 30(8): 421–425.
• Mirat, O, et al. (2013). ZebraZoom: an automated program for highthroughput behavioral analysis and categorization. Frontiers in Neural
Circuits. 107(7): 1-12.
• Gerlai, R, and Blaser, R. (2006) Behavioral phenotyping in zebrafish:
Comparison of three behavioral quantification methods behavior
research methods. 38(3): 456-469
METHOD
• We compared the output of 10 trials from 2 experimental
treatments using Argus Ethovision, and the built-in output from
RealFishTracker to calculate the total distance travelled and the
average distance from a stimulus.
• We used a portmanteau test called the Ljung-Box test to determine
whether the residuals were white noise.
• If the residuals are white noise then they ~ ! So we can compare
our test statistic to this!
• Better models will have higher p-values, we want the variation
between the models to only be white noise (and not some trend
we would otherwise miss).
• I ran a loop over the behavioral trials from 2 different measured
behaviors and compared the difference in output for each program
in a pairwise fashion.
• Most of the tests returned results consistent with the hypothesis that
the residuals between our model and other competing models were
merely white noise (p<0.05)
• The experimental treatment did not seem to have any effect on the
fitting of the data.
• The programs agreed more often (had similar p-values) with the total
distance travelled behavioral variable than with the distance to stimuli
variable. This indicates computer programs may have more variability
in measuring this particular variable.
ACKNOWLEDGEMENTS
• The p-values were plotted and graphed on a heatmap, so the
influence of the experimental treatment groups and the different
behavioral variable could be checked as well as the relationship
between any of the three separate measurements. The residuals
and the ACF were also plotted to further analyze any outliers
among the residuals.
I would like to thank Dr. Robert Gerlai for opening up his lab for me, Soaleha
Shams for her time and (indefinite) patience, James McCrae for developing
theRealFishTracker, Dr. Alison Weir for her advice, Niveen Fulcher for graciously
donating her data, as well as to my other colleagues from the Gerlai Lab for their
continued help and support. Funding was provided by NSERC and NIH/NIAAA
Grant 1R01AA015325-01A2.