BayesPrism Service

BayesPrism is a fully Bayesian inference of tumor microenvironment composition and gene expression. It consists of the deconvolution module and the embedding learning module. The deconvolution module leverages cell-type-specific expression profiles from scRNA-seq and implements a fully Bayesian inference to jointly estimate the posterior distribution of cell-type composition and cell type-specific gene expression from bulk RNA-seq expression of tumor samples. The embedding learning module uses Expectation-maximization (EM) to approximate the tumor expression using a linear combination of tumor pathways while conditional on the inferred expression and fraction of non-tumor cells estimated by the deconvolution module. Only the deconvolution module has been implemented as an online service due to the limitation of running time on the BayesPrism Gateway.

Registered users need only upload experimental data in the required format and push the start button. Once the job is finished, the user will be notified by e-mail. Results can be downloaded to the user’s local machine. Users need to login -> upload data -> run data. Results can be downloaded and further analyzed in R or Python.

See our documentation, FAQ, GitHub, or paper for additional questions.

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SpaceFold Service

Mapping gene expression cartography from spatial transcriptomics for tissues with stereotypical structures.

It takes the BayesPrism deconvolution Visium output as the input, and computes the pseudo-axis to impute a one-dimensional coordinate that recapitulates the tissue structure. The cell type fraction and cell type-specific gene expression are then projected onto the pseudo-axis to generate the gene expression cartography.

Data explorer: scRNA-seq of mouse small and large intestine tissue.

See our documentation, FAQ, GitHub, or paper for additional questions.

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