BACKGROUND
cell motility is an essential component of normal development, inflammation, tissue repair, angiogenesis, and tumor invasion  <cit> .
after conception, selected cells of the developing mammalian zygote invade the uterine wall to establish the placenta, while the intricately programmed migration of other cells within the embryo shapes the complex form of the emerging organism.
fetal nervous system presents us with an example of large scale migration of immature neuroblasts from their place of origin to other locations in order to make right connections  <cit> .
studies about cell migration pattern have been commonly performed at the population level.
although such analysis gives a general idea of what is happening, they are void of what happens at the sub-population level  <cit> .
a common example is cancer, where variation in cancer cells leads to therapeutic treatments that target a particular subset of the population  <cit> .
therefore, for studies aiming at potential therapeutic treatment, it is important to focus on sub-populations that can be treated to prevent recurrence and metastasis.
several studies have developed techniques to mine and infer biological knowledge from high-content screening  data.
perlman et al employed the kolomogorov smirnov statistic for high throughput cytological profiling.
their study reflected more on biological mechanism rather than chemical implications  <cit> .
tanaka et al used principal component analysis to identify small molecule kinase inhibitors in morphological based screens and neural networks were used by bakal et al to perform a genetic screen to identify genes controlling different aspects of cell morphology  <cit> .
support vector machines  and factor analysis were employed to identify compound activity  <cit> .
these studies have demonstrated the need for simultaneous analysis of features, but the computational and graphical difficulties remain an unsolved challenge for data analysis  <cit> .
the few existing analytical methods used to validate hcs data across a series of experiments require extensive operator interaction and, more importantly lack statistical rigour, resulting in underutilization of information available from the powerful hcs technology.
as hcs applications become increasingly complex, so does the composition of the cell populations as well as the underlying covariance structure of the cellular data.
in order to overcome these bottlenecks, we propose to develop a sub-population analysis technique based on kinetic features.
our technique is semi-supervised and aims to find the correlation between cell motility  and cell morphology  and infer biological mechanisms underlying cell motility of tissues under different conditions.
this will facilitate studies on molecular signalling pathways involved in cell motility.
in this manuscript, we illustrate our sub-population analysis by applying it to a population of ic  <dig> macrophage cells.
our method consists of several phases: using active contours without edges  <cit>  for segmentation, autoregressive models  <cit>  for modeling cellular trajectories and k-means  <cit>  clustering to classify the cells.
RESULTS
dataset
the cells used in this experiment were mouse macrophage cell lines ic- <dig>  tib-186).
the cell line was cultured in 85% rpmi- <dig> medium, 15% fbs, and antibiotics .
cell culture and maintenance techniques were performed as described by atcc.
cells are plated overnight to allow them to attach onto 96-well plate.
the media is aspirated and incubated in 1: <dig> dilution cmfda.
the media is aspirated again and new media containing  <dig> μm trolox is added.
cells were imaged over a period of  <dig> minutes at every  <dig> minute interval using cellomics kineticscan giving a total of six snapshots.
the cells do not undergo mitotic division during this time due to absence of activating factors.
four fields per well are imaged and each field contained approximately  <dig> cells.
each image is  <dig> bit grayscale  of size  <dig> ×  <dig> pixels.
cell segmentation
macrophage cells are identified by level sets, preserving the topology of the cells that is vital for extracting the static parameters.
the results are furnished in fig  <dig>  almost all the cells were properly identified by this method.
fig  <dig> shows a snapshot of unprocessed image.
fig  <dig> shows the segmented cells .
the cpu time for this method is  <dig>  mins compared to classical otsu method which took  <dig> s and fuzzy c-means clustering  on a  <dig>  ghz and  <dig> gb ram desktop computer  <cit> .
segmentation is the first step in any image analysis pipeline and extraction of static features depend on how well shape features are conserved during segmentation.
this method was compared with classical otsu and fuzzy c-means, which resulted in severe loss of morphological information .
static features
ensemble of basic and zernike shape features were measured using matlab's image processing toolbox .
zernike features describe more intuitive aspects of the cell and are calculated using an orthogonal basis set, the zernike polynomials, which are defined over the unit circle.
the amplitude of these complex-valued moments were used as features in subsequent analysis  <cit> .
for the current analysis, zernike polynomials from order  <dig> to  <dig> were calculated, giving in total  <dig> basic and  <dig> zernike measurements.
while there is no limit to the order which can be calculated, we found that this number of features was sufficient.
kinetic features
snapshot of tracks identified by auto-regressive models  are shown in fig  <dig>  as we can observe, the movement of cells are anisotrophic.
features like  speed of cell,  chemotactic index,  length of the trajectory, and  total displacement of the cell were measured using the method described above.
sub-population analysis
in k-means clustering, each cell is treated as an object having a location in multidimensional space.
it finds a partition in which the cells in one cluster are close to each other and far off from cells belonging to other clusters.
eventually, total sum of the distances levels out even if data is partitioned into multiple clusters.
the point of inflection indicates optimal cluster number.
clustering static features showed three clusters  with minimum homogeneity and maximum separation, whereas clustering with dynamic features showed four clusters with maximum homogeneity and minimum separation .
only when dynamic features were combined with static, did we get better resolution clusters which are homogeneous and well separated as shown in table  <dig>  fig  <dig> shows optimal cluster number for combined static and dynamic features clustering.
the three clusters of cells identified from the data can be described as follows:  cluster 1: compact cells which move or slide against the 2d surface steadily.
this population formed the bulk and they had fan a shaped spread suggestive of active mobility.
cluster 2: cells with much bigger area and perimeter.
the speed of migration is the maximum among the three clusters showing directional bias.
cluster 3: bigger cells which move very slowly and do not translate much.
this is evident from low path displacement measure.
this cluster showed clear directional bias and pattern of movement and is quite different from the previous clusters.
a wind rose plot for top  <dig> cells representing each cluster shows the trend in directionality and variation in total displacement.
a wind rose plot for cluster  <dig> is shown in , cluster  <dig> , cluster  <dig> .
the features along with the mean values for each cluster are shown in table  <dig>
CONCLUSIONS
we have demonstrated a novel exploratory method of identifying sub-populations by combining static and dynamic features, in particular how dynamic features improve sub-population analysis at the tissue level.
this kind of analysis is important in transplantation studies because macrophages being the sentries of immune systems, accumulate at the site of transplantation and the number and type of inflammatory cells often are more related to the surgical trauma and material  <cit> .
static features like area, eccentricity and perimeter are indicators of the protrusive forces that steer the cell to move forward or retract and are the main indicators to study the impact of any perturbation on macrophage cells.
this is further supported by kinetic features like speed, chemotactic index and path length.
the changes in these features indicate the response of cells to stimulus which in turn depends on the milieu where the cell is positioned.
since our approach is semi-supervised, no prior knowledge or little intervention is required.
this study also showcases the importance of kinetic features needed to quantify sub-populations.
features such as area, eccentricity, perimeter, speed, chemotactic index, path length/displacement capture all the essential information required to characterize any group of cells.
there is much to be done to understand the intricacies of the features in play during cell motility.
we outlined a framework that can be easily reproduced by using common data mining techniques  to gain insight into morphological and motility features obtained from hcs image data.
our future work will focus on bridging cell morphology with cell motility to understand the underlying biological mechanisms.
not many studies have been dedicated to understanding the spatio-temporal relation at the sub-population level especially by making use of kinetic features.
understanding how these sub-populations change with drug treatment will give insight to how any morphology is related to one another and with motility factors.
in particular, this framework will help in associating cell morphology with function which might be useful in detecting aberrant cancer cells or the differentiating potential of stem cells.

 cell motility is an essential component of normal development, inflammation, tissue repair, angiogenesis, and tumor invasion. after conception, selected cells of the developing mammalian zygote invade the uterine wall to establish the placenta, while the intricately programmed migration of other cells within the embryo shapes the complex form of the emerging organism. fetal nervous system presents us with an example of large scale migration of immature neuroblasts from their place of origin to other locations in order to make right connections. studies about cell migration pattern have been commonly performed at the population level. although such analysis gives a general idea of what is happening, they are void of what happens at the sub-population level. a common example is cancer, where variation in cancer cells leads to therapeutic treatments that target a particular subset of the population. therefore, for studies aiming at potential therapeutic treatment, it is important to focus on sub-populations that can be treated to prevent recurrence and metastasis. several studies have developed techniques to mine and infer biological knowledge from high-content screening data. perlman et al employed the kolomogorov smirnov statistic for high throughput cytological profiling. their study reflected more on biological mechanism rather than chemical implications. tanaka et al used principal component analysis to identify small molecule kinase inhibitors in morphological based screens and neural networks were used by bakal et al to perform a genetic screen to identify genes controlling different aspects of cell morphology. support vector machines and factor analysis were employed to identify compound activity. these studies have demonstrated the need for simultaneous analysis of features, but the computational and graphical difficulties remain an unsolved challenge for data analysis. the few existing analytical methods used to validate hcs data across a series of experiments require extensive operator interaction and, more importantly lack statistical rigour, resulting in underutilization of information available from the powerful hcs technology. as hcs applications become increasingly complex, so does the composition of the cell populations as well as the underlying covariance structure of the cellular data. in order to overcome these bottlenecks, we propose to develop a sub-population analysis technique based on kinetic features. our technique is semi-supervised and aims to find the correlation between cell motility and cell morphology and infer biological mechanisms underlying cell motility of tissues under different conditions. this will facilitate studies on molecular signalling pathways involved in cell motility. in this manuscript, we illustrate our sub-population analysis by applying it to a population of ic macrophage cells. our method consists of several phases: using active contours without edges for segmentation, autoregressive models for modeling cellular trajectories and k-means clustering to classify the cells.  dataset the cells used in this experiment were mouse macrophage cell lines ic- tib-186). the cell line was cultured in 85% rpmi- medium, 15% fbs, and antibiotics. cell culture and maintenance techniques were performed as described by atcc. cells are plated overnight to allow them to attach onto 96-well plate. the media is aspirated and incubated in 1: dilution cmfda. the media is aspirated again and new media containing μm trolox is added. cells were imaged over a period of minutes at every minute interval using cellomics kineticscan giving a total of six snapshots. the cells do not undergo mitotic division during this time due to absence of activating factors. four fields per well are imaged and each field contained approximately cells. each image is bit grayscale of size × pixels. cell segmentation macrophage cells are identified by level sets, preserving the topology of the cells that is vital for extracting the static parameters. the results are furnished in fig almost all the cells were properly identified by this method. fig shows a snapshot of unprocessed image. fig shows the segmented cells. the cpu time for this method is mins compared to classical otsu method which took s and fuzzy c-means clustering on a ghz and gb ram desktop computer. segmentation is the first step in any image analysis pipeline and extraction of static features depend on how well shape features are conserved during segmentation. this method was compared with classical otsu and fuzzy c-means, which resulted in severe loss of morphological information. static features ensemble of basic and zernike shape features were measured using matlab's image processing toolbox. zernike features describe more intuitive aspects of the cell and are calculated using an orthogonal basis set, the zernike polynomials, which are defined over the unit circle. the amplitude of these complex-valued moments were used as features in subsequent analysis. for the current analysis, zernike polynomials from order to were calculated, giving in total basic and zernike measurements. while there is no limit to the order which can be calculated, we found that this number of features was sufficient. kinetic features snapshot of tracks identified by auto-regressive models are shown in fig as we can observe, the movement of cells are anisotrophic. features like speed of cell, chemotactic index, length of the trajectory, and total displacement of the cell were measured using the method described above. sub-population analysis in k-means clustering, each cell is treated as an object having a location in multidimensional space. it finds a partition in which the cells in one cluster are close to each other and far off from cells belonging to other clusters. eventually, total sum of the distances levels out even if data is partitioned into multiple clusters. the point of inflection indicates optimal cluster number. clustering static features showed three clusters with minimum homogeneity and maximum separation, whereas clustering with dynamic features showed four clusters with maximum homogeneity and minimum separation. only when dynamic features were combined with static, did we get better resolution clusters which are homogeneous and well separated as shown in table fig shows optimal cluster number for combined static and dynamic features clustering. the three clusters of cells identified from the data can be described as follows: cluster 1: compact cells which move or slide against the 2d surface steadily. this population formed the bulk and they had fan a shaped spread suggestive of active mobility. cluster 2: cells with much bigger area and perimeter. the speed of migration is the maximum among the three clusters showing directional bias. cluster 3: bigger cells which move very slowly and do not translate much. this is evident from low path displacement measure. this cluster showed clear directional bias and pattern of movement and is quite different from the previous clusters. a wind rose plot for top cells representing each cluster shows the trend in directionality and variation in total displacement. a wind rose plot for cluster is shown in, cluster, cluster. the features along with the mean values for each cluster are shown in table  we have demonstrated a novel exploratory method of identifying sub-populations by combining static and dynamic features, in particular how dynamic features improve sub-population analysis at the tissue level. this kind of analysis is important in transplantation studies because macrophages being the sentries of immune systems, accumulate at the site of transplantation and the number and type of inflammatory cells often are more related to the surgical trauma and material. static features like area, eccentricity and perimeter are indicators of the protrusive forces that steer the cell to move forward or retract and are the main indicators to study the impact of any perturbation on macrophage cells. this is further supported by kinetic features like speed, chemotactic index and path length. the changes in these features indicate the response of cells to stimulus which in turn depends on the milieu where the cell is positioned. since our approach is semi-supervised, no prior knowledge or little intervention is required. this study also showcases the importance of kinetic features needed to quantify sub-populations. features such as area, eccentricity, perimeter, speed, chemotactic index, path length/displacement capture all the essential information required to characterize any group of cells. there is much to be done to understand the intricacies of the features in play during cell motility. we outlined a framework that can be easily reproduced by using common data mining techniques to gain insight into morphological and motility features obtained from hcs image data. our future work will focus on bridging cell morphology with cell motility to understand the underlying biological mechanisms. not many studies have been dedicated to understanding the spatio-temporal relation at the sub-population level especially by making use of kinetic features. understanding how these sub-populations change with drug treatment will give insight to how any morphology is related to one another and with motility factors. in particular, this framework will help in associating cell morphology with function which might be useful in detecting aberrant cancer cells or the differentiating potential of stem cells. 
in this paper, we present sub-populations analysis of cells at the tissue level by using dynamic features of the cells. we report a novel method to identify sub-populations using kinetic features and demonstrate that these features improve sub-population analysis at the tissue level. the demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods. we used active contour without edges for segmentation of cells, which preserves the cell morphology, and autoregressive modeling to model cell trajectories. high content screening techniques are increasingly used to understand the regulation and progression of cell motility.
 high content screening techniques are increasingly used to understand the regulation and progression of cell motility. the demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods.  in this paper, we present sub-populations analysis of cells at the tissue level by using dynamic features of the cells. we used active contour without edges for segmentation of cells, which preserves the cell morphology, and autoregressive modeling to model cell trajectories. the sub-populations were obtained by clustering static, dynamic and a combination of both features. we were able to identify three unique sub-populations in combined clustering.  we report a novel method to identify sub-populations using kinetic features and demonstrate that these features improve sub-population analysis at the tissue level. these advances will facilitate the application of high content screening data analysis to new and complex biological problems. 7– september asia pacific bioinformatics network eighth international conference on bioinformatics singapore 