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sc targeted proteomics:Stan model for latent Dirichlet allocation

Banff International Research Station for Mathematical Innovation and Discovery

Dr. Pratheepa Jeganathan received her masters (2013) and PhD (2016) from Texas Tech University and is currently a postdoctoral research fellow working with Prof Susan Holmes at Stanford University (https://profiles.stanford.edu/pratheepa-jeganathan) Her work considered solutions for 1) how should we approach integrating partially-overlapping proteomic data collected on different patients with similar phenotypes 2) Without including the spatial x-y coordinate data, how well can we predict cell co-location She will illustrate the topic modeling on discretized targeted proteomics data and the method to infer cell co-location. We integrated the two SingleCellExperiment using MultiAssayExperiment class in the R/Bioconductor package. We converted the normalized data to original protein expression and discretized (for the preliminary analysis, we added a minimum of the normalized value for each marker, but we need to know the sample mean and standard deviation of marker expressions in the MIBI data). We considered each cell is a document and wrote a Stan model for latent Dirichlet allocation. Using posterior samples of topic proportions, we inferred the latent topics with a higher proportion in each cell. We proposed a solution to the alignment issue. Code is available at https://github.com/PratheepaJ/Banff_proteomics

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: Standford University

2020-12-14

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/76781

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/