Example

Data inputs

The first input is Gene_expression_mat which is \(N \times G\) dataframe. Here \(N\) denotes the number of spatial locations and \(G\) denotes number of genes.

The second input is Spatial_locations is a dataframe which contains spatial coordinates.

The third input is Cluster_annotations.

The fourth input is sPMM. If TRUE, the code will return the estimates of sigma1_sq and sigma2_sq from the spatial Poisson mixed model.

The fifth input is 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.

The final input is numCore. The number of requested cores for parallel computing and default is set to be 1.

Output

You will obtain a list of objects as output.

Posterior_samples contains all the posterior samples.

Shared_network provides the shared co-expression matrix (transformed correlation matrix of \(G_{s} = \Phi \Phi^{T}\)).

Cluster_network provides the cluster specific co-expression matrices (transformed correlation matrices of \(G_{c} = \Phi \Phi^{T} + \Psi^{c} {\Psi^{c^{T}}}\)).

Example

Here we provide an example to run the SpaceX method.

# Reading the Breast cancer data

# Spatial locations
head(BC_loc)

# Gene expression for data
head(BC_count) 

# Data processing
G <-dim(BC_count)[2] # number of genes
N <-dim(BC_count)[1] # number of locations

Next, we’ll apply the SpaceX method on the Breast cancer dataset.

# Application to SpaceX method
BC_fit <- SpaceX(BC_count,BC_loc[,1:2],BC_loc[,3],sPMM=FALSE,Post_process = TRUE, numCore=2)

# Shared_network :: Shared co-expression matrix
# Cluster_network :: Cluster specific co-expression matrices