Introduction to BASS
Zheng Li
2022-02-06
Last updated: 2022-02-27
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Knit directory: BASS-analysis/
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Welcome to the BASS website!
This website maintains code for reproducing simulation and real data application results described in the upcoming paper.For the software, please to refer to BASS.
Introduction to BASS
BASS is a method for multi-scale and multi-sample analysis in spatial transcriptomics. BASS performs multi-scale transcriptomic analyses in the form of joint cell type clustering and spatial domain detection, with the two analytic tasks carried out simultaneously within a Bayesian hierarchical modeling framework. For both analyses, BASS properly accounts for the spatial correlation structure and seamlessly integrates gene expression information with spatial localization information to improve their performance. In addition, BASS is capable of multi-sample analysis that jointly models multiple tissue sections/samples, facilitating the integration of spatial transcriptomic data across tissue samples.
BASS workflow
BASS Model overview
BASS relies on a Bayesian hierarchical modeling framework that describes the relationship among gene expression features, cell type labels, spatial domain labels, cell type compositions, and neighborhood graphs in a hierarchical fashion: \[ \boldsymbol{x}_i^{(l)} | c_i^{(l)} = c \sim MVN(\boldsymbol{\mu}_c, \boldsymbol{\Sigma}) \] \[ c_i^{(l)} | z_i^{(l)} = r \sim Cat(\boldsymbol{\pi}_r) \] \[ \boldsymbol{z}^{(l)} \sim Potts(V^{(l)}, \beta) \] Above, the first equation models the expression feature of the \(i\)th cell on section \(l\), \(\boldsymbol{x}_i^{(l)}\), as depending on its cell type label \(c_i^{(l)}\) with a multivariate normal distribution parameterized by a cell type-specific mean parameter \(\boldsymbol{\mu}_c\) and a variance-covariance matrix \(\boldsymbol{\Sigma}\). The second equation models the probability of the \(i\)th cell belonging to the cell type \(c\) as depending on the underlying spatial domain with a categorical distribution parameterized by the \(r\) domain-specific cell type composition vector \(\boldsymbol{\pi}_r\). The third equation models the spatial domain label of all cells on the section \(l\), \(\boldsymbol{z}^{(l)}\), as a function of the neighoborhood graph \(V^{(l)}\) through a homogeneous Potts model characterized by an interaction parameter \(\beta\).