Implementing high dimensional reductional analysis on histocytometric data

<p>In the previous protocol article (Munoz-Erazo, Schmidt, Shinko, Eccles, et al., 2022), we demonstrated construction of a histocytometry pipeline that is capable of both segmenting highly-aggregated cell populations and retaining the original intensity data range of the input microscopic images. In the protocol article presented here, using the output from the aforementioned protocol article, we demonstrate how to phenotype the data using the high dimensional reductional analysis technique opt-t-SNE (optimized t-distributed Stochastic Neighbor Embedding) and compare it to traditional manual gating.</p> <p>Additional, we present a support protocol illustrating the advantage of the inclusion of cell junction/membrane marker in accurately segmenting highly-aggregated cell populations in ilastik.</p> <p>In the previous protocol article (Munoz-Erazo, Schmidt, Shinko, Eccles, et al., 2022), we demonstrated construction of a histocytometry pipeline that is capable of both segmenting highly-aggregated cell populations and retaining the original intensity data range of the input microscopic images. In the protocol article presented here, using the output from the aforementioned protocol article, we demonstrate how to phenotype the data using the high dimensional reductional analysis technique opt-t-SNE (optimized t-distributed Stochastic Neighbor Embedding) and compare it to traditional manual gating.</p> <p>Additional, we present a support protocol illustrating the advantage of the inclusion of cell junction/membrane marker in accurately segmenting highly-aggregated cell populations in ilastik.</p>