  Motivation: The scratch assay is a standard experimental protocol used to
characterize cell migration. It can be used to identify genes that regulate
migration and evaluate the efficacy of potential drugs that inhibit cancer
invasion. In these experiments, a scratch is made on a cell monolayer and
recolonisation of the scratched region is imaged to quantify cell migration
rates. A drawback of this methodology is the lack of its reproducibility
resulting in irregular cell-free areas with crooked leading edges. Existing
quantification methods deal poorly with such resulting irregularities present
in the data. Results: We introduce a new quantification method that can analyse
low quality experimental data. By considering in-silico and in-vitro data, we
show that the method provides a more accurate statistical classification of the
migration rates than two established quantification methods. The application of
this method will enable the quantification of migration rates of scratch assay
data previously unsuitable for analysis. Availability and Implementation: The
source code and the implementation of the algorithm as a GUI along with an
example dataset and user instructions, are available in
https://bitbucket.org/anavictoria-ponce/local_migration_quantification_scratch_assays/src/master/.
The datasets are available in
https://ganymed.math.uni-heidelberg.de/~victoria/publications.shtml.
