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. 2022 Nov;2(11):e586.
doi: 10.1002/cpz1.586.

Implementing High Dimensional Reduction Analysis on Histocytometric Data

Luis Munoz-Erazo  1 , Diana Shinko  2   3 , Alfonso J Schmidt  1 , Kylie M Price  1
Affiliations

Implementing High Dimensional Reduction Analysis on Histocytometric Data

Luis Munoz-Erazo et al. Curr Protoc. 2022 Nov.

Abstract

In a previous protocol article, 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 microscopy images. In the protocol presented here, using the output from the aforementioned article, we demonstrate how to phenotype the data using the high dimensional reduction analysis technique optimized t-distributed stochastic neighbor embedding (opt-t-SNE) and compare it to traditional manual gating. Additionally, we present a protocol illustrating the advantage of the inclusion of cell junction/membrane markers for accurately segmenting highly aggregated cell populations in ilastik. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Phenotyping lymph node populations using manual gating Basic Protocol 2: Phenotyping lymph node populations using t-SNE dimensional reduction Support Protocol: ilastik segmentation using a pan marker.

Keywords: cell segmentation; high dimensional reduction analysis; histocytometry; immunophenotyping; microscopy.

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References

Literature Cited

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    1. https://forum.image.sc/
    1. https://github.com/
    1. https://support.rstudio.com/hc/en-us/sections/200271437-Getting-Started

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