SpaceX: Estimation of shared and cluster specific gene co-expression...

In bayesrx/SpaceX: Gene Co-expression Network Estimation for Spatial Transcriptomics

Estimation of shared and cluster specific gene co-expression networks for spatial transcriptomics data.

Description

SpaceX function estimates shared and cluster specific gene co-expression networks for spatial transcriptomics data. Please make sure to provide both inputs as dataframe. More details about the SpaceX algorithm can be found in the reference paper.

Usage

SpaceX(
  Gene_expression_mat,
  Spatial_locations,
  Cluster_annotations,
  sPMM = FALSE,
  Post_process = FALSE,
  numCore = 1,
  nrun = 10000,
  burn = 5000
)

Arguments

Gene_expression_mat	Gene expression dataframe (N X G).
Spatial_locations	Spatial locations with coordinates. This should be provided as dataframe.
Cluster_annotations	Cluster annotations for each of the spatial location.
sPMM	If TRUE, the code will return the estimates of sigma1_sq and sigma2_sq from the spatial Poisson mixed model.
Post_process	If FALSE, the code will return the posterior samples of Phi and Psi^c (based on definition in equation 1 of the SpaceX paper) only. Default is TRUE and the code will return all the posterior samples, shared and cluster specific co-expressions.
numCore	The number of cores for parallel computing (default = 1).
nrun	default = 10000
burn	default = 5000

Value

Posterior_samples	Posterior samples
Shared_network	Shared co-expression matrix
Cluster_network	Cluster specific co-expression matrices

References

Acharyya S., Zhou X., Baladandayuthapani V. (2021). SpaceX: Gene Co-expression Network Estimation for Spatial Transcriptomics.

Examples

Implementation details and examples can be found at this link https://bookdown.org/satwik91/SpaceX_supplementary/.

bayesrx/SpaceX documentation built on April 26, 2023, 6:46 p.m.