LAVA visualizes T-cell differentiation as cells moving across a landscape of hills and valleys, leading to attractor basins representing stable or semi-stable differentiation states. The model illustrates several principles, including: i) cell populations may behave more predictably than individual cells; ii) analogous to reticulate evolution, differentiation may proceed through a network of interconnected states, rather than a single well-defined pathway; iii) relatively minor changes in the barriers between attractor basins can change the stability or plasticity of a population; iv) intra-population variability of gene expression may be an important regulator of differentiation, rather than inconsequential noise; v) the behavior of some populations may be defined mainly by the behavior of outlier cells. LAVA is not a quantitative representation of actual differentiation. Instead, it is intended to provoke discussion of T-cell differentiation pathways, particularly highlighting a probabilistic view of transitions between states.
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Disclaimer: We share the LAVA source code in the hope that it will be useful to the flow cytometry community. The software comes with no warranties what so ever. We try to ensure that the LAVA software can be used "out of the box" but have very limited bandwidth to respond to user requests for support.